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It’s that time of the year when we ask industry leaders for their thoughts on what happened in 2016 and what they forsee will happen in the new year. Here’s Part 1.

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Steve Hafner, CEO & Co-founder, KAYAK

Top 3 Things That Happened and That Mattered in 2016

1.  Rise of AI. It’s getting even tougher to be a travel startup or to afford innovation. You simply need too many developers, on too many platforms, with access to too much data, to make a difference.

2.  Ctrip buying Skyscanner. Now all of the big three OTAs have made their bets. It’s going to be fun to watch.

3.  My fiancee getting preggo again.

Top 3 predictions for 2017

1.  Expedia will buy more growth and OTA market share (probably Odigeo).

2.  TripAdvisor will start acting like it’s 1943 against Trivago. Now that Trivago is subject to the same public investor pressure as TripAdvisor, it’s a level playing field. I, for one, like Kaufer’s chances. They have great content on top of a great search engine, and an P&L that can sustain more marketing.

3.  Google will start intercepting branded search terms for their flight engine and hotel price ads. It’ll start slowly but will gain steam. Can you imagine how the travel industry will howl when HPA is above Marriott hotel results?

 


 

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Hugo Burge, CEO, Momondo Group

3 Top Things That Happened and That Mattered in 2016

1.  The Populist Politics of Polarisation took a grip, as a reaction against local disenfranchisement.

2.  The UK voted for Brexit.

3.  Momondo group completed re-invention of business with final site launch in Cheapflights.com

3 Top Predictions for 2017

1.  Ongoing political uncertainty, ruptures and simmering tension.

2.  Digital payments re-inventing the way we purchase.

3.  To keep an open world, everyone will have to do their bit to keep fighting for it.


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Timothy Hughes, Vice President Business Development, Agoda

3 Top Things That Happened and That Mattered in 2016

1.  Inventory is everywhere. Some of the big global players – Agoda, Booking, Airbnb – passed the 1mm properties live and bookable threshold (and climbing). You can truly book online for anywhere in the world.

2.  Google the OTA. Google has products that make them an OTA, even if they refuse to say it.

3.  A year to remember. 2016 will be remember like other big event years. Like 1989 for the fall of the Berlin Wall and 2001 for the 9/11 Attacks. We will remember and write for years about the events of 2016 – Brexit, Trump, Syria, Nice and more.

3 Top Predictions for 2017

1. “Losing Loads of Money” will stop being a business plan. With the IPO of Trivago and tightening in economic conditions we will see many of the Asian travel companies building a business off massive losses feel the pressure to move to the real world of sustainable, profitable, business models.

2. Direct and OTA will co-exist. Property owners will become more and more aware that direct and indirect (ie OTA) distribution are complementary not competitive channels.

3.  Chat rules, calls don’t. Chat will take a more prominent role in everything. We are not yet at “peak chat” – where chat completely kills voice. And 2017 wont be the year for “peak chat”. But it will be the year for some changes in customer behaviour (and that travel company products) driven by the accelerated use of chat.


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Ken Mishima, VP Ecommerce Strategy, i.JTB

3 Top Things That Happened and That Mattered in 2016

1.   Online really matters. Any players connected to internet now concerning on online marketing & distribution. TripAdvisor moved to transactions while earning their most of revenue as medias which clients are OTAs and suppliers. Hotels and Airlines’ key strategy is direct distribution via online which pressured OTAs and any online community. At a local level, hitting nearly 40% online penetration in Japan and still growing. Online was discussed as ‘as part of’, but now it must be everyone’s core strategy.

2.   New opportunity matters. SG enjoyed their strategic injection of casino business with Suns group for some years which justified the new angle really matters to the industry if it was done well. Inbound into Japan developed the new market segment with strong traffic of over 20M PPL. Likely 24M PPL by the end of the year. To accommodate this demand for their stays, vacation rental can be the only hope to the market. Travel is the old fashion, but these new waves even push policy makers to think differently.

3.   Re-starting:  Consolidation

Growing stars or big players in segment were becoming the part of big ones. Meta, Hotels, OTAs, etc… Yet seeing the outcome of these games if it can be good for customers/partners, or providing more financial pressures to them with some returns.

3 Top Predictions for 2017

1.  Data, data, data management and analytics – to be ready for new user experience. Voice, Text/chatbot, graphic search. Also the form of new traffic or customer acquisition which can be unique to voice/chatbot etc – which does not match with conventional web marketing strategy and operation model.

2.  Partnership model with suppliers (from OTA/online distributor standpoints). Now we are living at the age of suppliers if their products are strong and unique. What would be the role of distributors – if they are not Expedia nor Priceline. What would be the new agenda for them to survive and grow?

3.  Any more new sharing model for travel? Home, building, space, car-ride, suitcase, and what else? Would it also become main stream of user experience?

Author:  WIT

Source:  http://www.webintravel.com/reflections-2016-predictions-2017-part

Categorized in Business Research

Large platform companies like Amazon, Apple, Google, Samsung, and Microsoft want to provide the operating system for our lives, and they will fight hard in 2017 to establish their foothold in the emerging technologies we will likely come to rely on in the future.

Who will succeed? Those with the most complete product offerings have an advantage. Since people like to buy products that play well with the other products they already own, a platform company risks losing customers by not having a product in a hot category. These large companies already have an advantage over smaller companies due to their massive R&D budgets and their ability to hire the best people to build the stuff we want now and to anticipate the technology we’ll want in the future. And if a hot product is developed by some ambitious startup, these giants can easily swoop in and acquire both the product and the people who created it.

These categories, while not new, will be the front lines of the platform wars in 2017.

SMART BLUETOOTH SPEAKERS

Amazon brilliantly hit upon a whole new product category with its Echo home personal assistant device. While other platform companies like Google and Apple were limiting their respective personal assistants to smartphones, Amazon saw that people wanted a personal assistant that stood on the countertop, could hear and understand voices in the room very well, and contained a speaker that actually sounded good.

Google has since created a competing device called Google Home, and there is plenty of speculation that Apple and Microsoft have something in the works as well. Amazon wisely opened up its personal assistant (called Alexa) to third-party developers, and thousands of them are now creating new "skills" for the home assistant. This trend will continue to escalate throughout 2017, and we will soon begin to see a new wave of skills that are more useful and easier to call up at your command.

As more personal assistant devices find their way into homes in 2017, the platform companies that sell them will increasingly compete to get developers to create better and better skills for the devices. And the platform companies themselves will try to integrate more of their own services through the devices. For instance, Amazon might offer more useful shopping services through the Echo, while Google will try to offer new search and productivity services.

VIRTUAL PERSONAL ASSISTANTS

Home assistant devices are just one vehicle for the natural language assistants of the platform companies. Assistants like Apple’s Siri, Google’s Assistant, Microsoft’s Cortana, and Amazon’s Alexa will begin showing up in new places, and in more useful ways, in the coming year. (Samsung has something called S Voice, but it this year acquired the company that developed Siri, so we may be seeing a new assistant technology from the company in the next year).

The platform companies are already investing heavily in the research and development that will make these assistants better listeners and more suited to completing tasks. Natural language assistants must understand our words, but also the meaning and intent behind them. That first part is easier than the second: Microsoft said in October that its Cortana assistant can now understand language roughly as well as a human transcriptionist.

The problem of teaching assistants to learn more about the user (identity, preferences, habits) is harder, but assistants will show improvement in this area in the coming year. Some will begin learning about the emotion expressed in the user’s comments and commands, which is harder still. They'll begin to display what seems like "common sense," which will enable them to communicate and interpret commands and requests in a more natural (and accurate) way.

And assistants will become more knowledgable about more things. They’ll be harder to stump when asking random questions that you might normally use a search engine to answer. They’ll say "I can’t help you with that" less often.

But assistants are in general not ready to learn in an open-ended, autonomous way; rather, they're being taught to learn in a highly structured way within well-defined contexts. An assistant, for example, may be tasked with learning what it can about a user’s habits based on calendar usage.

Assistants are a prime example of a product that is increasingly linked to other products and services offered by the platform. They’re increasingly the thing we’ll use to call up all kinds of data and services, and they’ll show up in more and more contexts. If a consumer sees one assistant as clearly better than others, they might be very tempted to adopt the services the assistant is able to call up.

CAR BRAINS

Automakers have been building platform companies’ infotainment systems into new models for some time, but the integration will soon go much deeper, and it will heat up the competition once again.

Google and Apple each have a platforms (Android Auto and CarPlay, respectively) for extending the set of apps and services (messaging, music, navigation, phone calls, etc.) in Android and iOS to the car. They’re generally regarded as superior to the stock infotainment systems in cars.

But now the platform war for the car extends way beyond the dashboard. Google, for example (and very likely Apple, too) has built a software central nervous system for the car, an operating system that will control the semi-autonomous or autonomous operation of the vehicle. Google may have first intended to sell an autonomous vehicle, but the company refocused efforts on creating the software brains for the vehicles, which could be used in the vehicles of more traditional car companies. Apple has very likely taken the same path.

Google recently formed a new company called Waymo under the Alphabet parent company to market its auto software. Fiat Chrysler will be the first partner to use the system in its vehicles; it said in may it will first use it in 100 of its minivans.

Apple has never formally announced its "Project Titan," but Uber, Tesla, and various automakers are furiously developing self-driving systems. And other platform players like Samsung may eventually jump into the fray.

Microsoft HoloLens

VR/AR HEADSETS

Virtual reality and augmented reality products and experiences are new to many consumers, and it’s yet to be seen how popular the technology will be.

Virtual reality, at least in the consumer space, may be the more mature technology. VR headsets like Oculus Rift and HTC Vive cover close out the outside world and create a 360-degree 3D world for the user. Companies like Facebook’s Oculus, Google, and HTC are already well down the road with the development of VR headsets and will continue to refine the technology during 2017. A growing number of phone makers are readying their devices to power the VR experiences in headsets based in Google’s new Daydream platform.

Apple has so far stayed out of the virtual reality space. This may be because the company is more interested in augmented reality, as CEO Tim Cook has suggested in his comments. Augmented reality superimposes digital data and images over the real world as seen through the camera lens on a mobile device or headset.

Microsoft’s HoloLens AR headset has been available to developers (and, technically, anyone else) for some time now, but augmented reality arguably had its coming out in 2016 with the Pokemon Go phenomenon in July. But that app requires viewing the overlaid content on the screen of a mobile device, which can be a clunky experience. The same type of experience is being used by toy makers to overlay digital imagery over dolls and action figures to make the play more interesting and to sell add-on products (accessories, media, etc.).

Perhaps the biggest name in the consumer AR space is Florida-based Magic Leap, which says it has a new kind of headset lens to create sharper digital imagery. The company’s investors have put more than a billion dollars behind the product, but a 2017 release looks less and less likely. Two sources in the AR space have told me that if Apple releases some kind of AR product, it won’t be until 2018. So 2017 may be more of a warmup year for AR. If the technology captures the imaginations of consumers, the platform war may ensue in 2018 and 2019.

ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE

I saved this one for last because AI is now finding its way into many of the products and services sold by the platforms. Personal assistants (like Siri or Alexa) may be the first context in which many people encounter a conversational AI, but the technology will begin showing up in lots of different contexts across the platform in the coming years.

Google and Apple already use AI in photo apps to automatically identify and tag images. Apple uses the same technology in the iPhone camera to recognize objects in the frame, and make adjustments accordingly. Microsoft, Google, and Apple are using AI in bots that can act as customer service reps on behalf of businesses.

Eventually, more advanced versions of the neural networks we see today will be used as the means of processing virtually all kinds of complex data. Where today’s AI needs lots of human training, the technology will become increasingly able to learn on its own. We’ll eventually stop thinking about computers as input/output if-this-then-that machines, and more like huge systems arranged like the neurons in the human brain. They'll process data more like the brain does.

We’re also in the early stages of a shift toward voice interactions with computers and applications. In the next year we may see some leaps forward in the machines’ ability not only to understand our words but also understand the real intent behind the words. We'll increasingly be able to speak commands to the devices in our lives. We'll tap screens less.

The good news in all this is that as these big, well-monied companies battle it out, specific products naturally get better, and whole platforms get more complete. Today, no one company can provide everything we need throughout the day. This may become less true as the major platforms increasingly extend new products and services into our work, home, entertainment, and personal lives.

Author:  MARK SULLIVAN

Source:  https://www.fastcompany.com/3066746/tech-forecast/these-are-the-five-key-battlegrounds-for-the-big-tech-platforms-in-2017

Categorized in Internet Technology

2016 was the banner year for cyber security – and not in a good way. But what does 2017 have in store?

There is no denying that 2016 was a big year for cybercrime. From the Bank of Bangladesh/SWIFT heist in February to the Dyn DDoS attack a few weeks ago, there was plenty of proof that hackers are getting smarter and their innovation is on a growth trajectory.

If there is one good thing derived from these hacks, it is that they have made alarm bells ring loud and true for consumers and organisations alike. This is the starting point for five cyber security predictions for the year ahead.

1. Consumers will prioritise security when deciding which companies to do business with

Following high-profile data breaches in 2016, including Yahoo and Three Mobile, consumers are more anxious than ever about the downstream financial crime that follows a cyber attack.

As the realisation of what a criminal can achieve once they have taken our data sinks in, consumers are beginning to demand guarantees that their services providers are safe.

In 2017, a trend will emerge around customers wanting to understand more about the security of the organisations they do business with.

Just as companies promote ‘seals of approval’ for accomplishments like being ‘green’, promoting gender equality or having accident-free workplaces, customers will look for some sort of seal of assurance that the companies they do business with have a strong cybersecurity posture.

In fact, Ofcom has recently highlighted that broadband providers such as BT are worse at customer service than financial services providers and must do more to deliver a reliable internet connection.

2. Consumers will take ownership of their own cybersecurity

The great doorbell hack of 2016 kicked off the year with a loud ding-dong. Hackers have figured out that smart home devices, such as doorbells and refrigerators, are gateways to home Wi-Fi networks and email logins.

Similarly, to how they developed new and more inventive scams to get hold of consumers’ data in the ‘90s, this is just the beginning of consumer-targeted cybercrime.

As people add more Internet of Things (IoT) devices to their smart homes and take more of their daily affairs online, the security of their online environment will become even more important.

In 2017, new services will emerge that allow consumers to evaluate their own cyber security as they work to protect their data and savings from criminals, and strive to take ownership of our cybersecurity.

3. Consumers and businesses will acknowledge the threat potential of IoT devices

Beyond hacked doorbells and refrigerators, certain IoT devices, like self-driving cars, can present serious security threats. Expect more attacks to follow, especially as it is currently easier for a hacker to create an IoT botnet to compromise a device than it is to phish for data in traditional ways. There is a serious lack of security features in the code developed for IoT devices which needs to be addressed.

Due to the risk some of these devices pose to human life, it should be no surprise to hear that the security of IoT coding will come under stricter scrutiny than ever before.

As IoT devices become widely used by businesses and individuals alike, people and organisations will make security considerations a priority in their decisions to use smart devices, not an afterthought.

4. Businesses will assess the cyber security of their own and partners’ networks

Led by the Office of the Comptroller of the Currency (OCC) directive requiring banks to manage risks – including cybersecurity risk – in their third-party relationships, companies in all industries will start paying a lot more attention to their business partners’ cybersecurity posture in 2017.

 

Most businesses have large and complex networks of partners, suppliers, vendors and other stakeholders with whom they exchange information on a regular basis. This means that the web of risk is incredibly wide, and a security breach in any link of the chain can expose the entire network.

Boardrooms across all industries have brought concerns about partner network security to the top of their agenda, so in 2017 we will see growth in the adoption of tools that assess risk across the entire network and bring a company’s security status to the forefront for partners, enterprises, and insurers.

5. Biometric security data may become the biggest security vulnerability of all

It started with the innovative Apple TouchID, developed to make it easier for consumers to unlock their phones. But, in 2016, we have seen biometric identification go mainstream – even three year old kids’ fingerprints are being captured when they visit Disney World.

Many believe that biometric security data is safer than digit-based passwords and, if used correctly, it may be so. However, in the wrong hands, biometric security data also has explosive potential.

In the aftermath of the compromise of 5.6 million US government military, civilian and contractor personnel fingerprints, Eva Velasquez, CEO of the Identity Theft Resource Center, explained that stolen fingerprints may be a big problem in the future.

This is especially the case if biometric technology is used to verify bank accounts, home security systems and even travel verifications.

Author:  Ben Rossi

Source:  http://www.information-age.com/5-cyber-security-predictions-2017-123463528

Categorized in Internet Privacy

Major advances in artificial intelligence (AI) have opened the door to many new ideas that were just impossible a few years ago

When creating a new AI-based app, there are many generic problems that are already being solved by other companies, for example face and gesture detection.

Unless this is the main business and focus of the company, they will prefer to look for an out-of-the-box AI-as-a-service solution which will save them time, expertise and money.

This type of solutions are called AI platforms and give their users many out-of-the-box services, such as computer vision (feature/face and gesture detection), natural language processing (NLP), speech to text, and translations between different language.

Many companies including Google and Amazon sell this kind of AI services. During 2017, we will continue to see many improvements in those platforms mainly in the ease of use, accuracy and performance.

Businesses whose goals can’t be achieved using AI-as-a-service will create customised modules on top of those platforms or completely start from scratch to create their own image recognition algorithm.

Companies in the medical field such as Zebra medical will continue to improve their AI algorithm, which can now detect under-diagnosed medical conditions in MRI scans.

Other companies such as Tesla will use similar technologies to improve their fully autonomous level five self-driving vehicles.

2017 will continue to see many businesses taking advantage of existing platforms and others creating their own customised AI algorithms.

Another major trend that will grow dramatically next year is apps being built on top of existing AI-ecosystems such as Siri by Apple, Alexa by Amazon and Assistant by Google.

Joining one or more of these closed AI-ecosystems brings tremendous benefits to the business, including huge ecosystem and distribution channels.

For the developers, it instantly harnesses the great power of AI, without the need to build and understand artificial intelligence.

All this is worth a lot of money, which can be saved by taking advantage of the existing AI-ecosystems.

As an example, let’s take Amazon’s AI-ecosystem with its ‘Alexa’ brain. One of the ways to interact with Alexa is to buy Amazon Echo, a hands-free speaker that you can control with your voice.

Domino’s pizza created an app for the Echo that allows hands-free pizza ordering with voice-control. This type of AI technology combined with a strong community of third-party developers will dramatically change the way we interact with technology around the house, office or on the go.

Similar to Allo, an instant messaging app developed by Google that includes a virtual assistant which provides a ‘smart reply’ function that allows users to reply without typing, we’ll see AI services further weave into everyday interactions in a conversational manner.

In terms of customer service, we’re seeing bots becoming a go-to tool. These bots are built into current platforms such as Facebook to allow people to check their account, make reservations or be redirected to the right department.

By not needing to wait for a physical body, customers are able to lessen the time they need to spend finding help, without draining companies resources.

Moreover, texting a bot feels more natural than conversing with one over the phone where they may only understand simple phrases.

Beyond customer servicing, bots on Facebook have been built by news sites to keep users up to date in a conversational manner.

Quartz introduced a conversational style app that updates throughout the day while CNN has built a bot directly for Facebook messenger that can reply with news depending on the input. So if a user wishes to read news about a certain topic, they can send a text message to CNN to request it.

Author:  Ben Rossi

Source:  http://www.information-age.com/2017-hold-digital-economy-123463767

Categorized in Business Research

Do you think 2017 will be the year of video? Again? If so, you’re in good company.

In fact, if I were forced to TLDR this post, the big social media trends for 2017 could be boiled down to this:

  1. Video (live, recorded, and 360-degree)
  2. Influencer marketing
  3. Bots

But there is much more you can and should do in 2017 to be successful on the top social media platforms.

One of those things mentioned by a few of our experts may seem a bit obvious, but it could be the most crucial: you must understand your audience!

Tactics are great. Understanding all the big social networks where people hang out is also great. Data is also super important.

But really, if you want to drive more engagement and ROI from social media, you need to know – and be responsive to – your customers. Maybe this isn’t so much a trend as a proven principle of good old-fashioned marketing, but it’s especially in social media: make it personal! Put a little more humanity in your 2017 social media strategy.

Here’s what 26 of the top marketing experts say will be the biggest trends in social media in 2017 – and beyond.

We’ve gathered insights from these social media pros:


 

Heidi Besik, Group Product Marketing Manager, Adobe

 

Heidi Besik

 

The biggest trends in social media in 2017:

Video

In 2016, the biggest takeaway from the success of video is that platforms like Facebook are beginning to challenge traditional media for ad dollars. What we used to know as big television events are now consumed through snackable clips.

Next year, the continued importance and consumer appetite for video will drive further refinement. Social media platforms will introduce easier ways for users to access video, as well as better tools for creators.

At the same time, we will see brand advertisers begin talking about platforms like Twitter and Facebook as a new form of television. And as consumers get increasingly more comfortable (and familliar) with video, we will see a shift in organic content where brands beginning building out dedicated video teams and putting together an infrastructure that decreases turnaround times and gets content out faster.

Measurement

Social networks have matured into some of the most targeted ad channels around. As a result, it’s put a bigger spotlight on justifying ROI.

Advertisers need robust data in the same way they have for existing channels like desktop Web and broadcast TV. This will be top of mind in the new year, as we see social networks work to deliver on comparable metrics and certain advertisers advocating for more third-party auditing.

We will also see more measurement conversations within organic content. Despite continued calls for the “death of organic content”, it will continue to occupy a big role in a brand’s communication strategy.

What we will see are social teams tapping into some of the analytics disciplines in other channels like desktop web – becoming much more diligent in measuring what works and adjusting in real-time. The practice overall will become increasingly more data-driven to drive ROI, as they compete with paid and earned.

Internal Collaboration

We are moving toward the year of integration for social media, where it impacts all stages of the customer journey and has become a standard, integral part of the marketing mix. Social strategists will need to better integrate with existing digital programs across web, ecommerce and mobile. As the roles of content marketing and social marketing become more intertwined, we’ve seen this need accelerate through 2016 and think it will continue to be an area of focus for marketers moving through 2017.

Customers expect brands to deliver a consistent, personalized experience across touch points. Yet most social marketers are still using up to 7 different, disconnected tools to manage social activities.

Disconnected data, content, workflows and teams are leading to poor customer experiences. Tool consolidation that facilitates integrated content, workflows and data will be critical to leveraging the power of social to drive better more consistent omnichannel digital experiences.


 

Kendall Bird, Associate Social Media Manager, Collegis Education

 

Kendall Bird

As we move into 2017, there are major trends that we are already seeing transpire within the social media space including live and recorded video that create immersive social media experiences and organic reach continuing to decline. The two trends go hand-in-hand as video is prioritized within organic social and is seen as an authentic way to connect with followers. With that said, social marketers continue to need to keep their head on a swivel and stay creative in their strategy plans.

Within the broader social media community we are seeing platforms such as Facebook, Snapchat, and Instagram moving toward live, real-time video. These platforms have products that enable marketers to connect with their followers in a completely different light than they are used to experiencing.

Video presents the opportunity to create more candid experiences for your followers. Social media users will continue to demand to live vicariously through social media content put out by brands and influencers. The question is how will you make genuine real-time experiences that make your followers feel as though they are there?

Several brands are already doing this right including Birchbox, Sweetgreen and Inspiralized. Each one of these brands showcases their products in an authentic, creative and interesting manner.

  • Birchbox‘s Facebook Live experience is one of my favorites to watch because they unbox their product (Birchbox, for those who don’t know, is a curated, monthly subscription box for men and women) and show what the product is and how to use it.
  • Sweetgreen (an East Coast fast-casual restaurant) is the most creative when it comes to Snapchat. Recently, one of their Stories, was introducing a Sweetgreen customer sharing their favorite salad bowl. They snapped the story from the lens of the customer and through the chef’s perspective with Snapchat Spectacles.
  • Inspiralized is one of my favorite food bloggers (small bias!), but Ali really does a wonderful job maintaining her social media accounts and really focuses in on what her followers are interested in and what the channels are about. Inspiralized uses Instagram Stories to focus more on the personal side of her life and business, whereas Snapchat is focused more on tutorials and product.

As you envision your social media strategies for 2017, be thoughtful about each platform and your followers. Why are they watching Facebook Live, Snapchat, and Instagram Stories? Are you differentiating your accounts to fit what your followers are interested in? Why are they following you?

Consider conducting an competitor analysis of what they are doing on these platforms products, focusing on what is successful for them and how you can better those efforts for the brands you are managing. Always remember, you are building a community – would you want to be part of it?


 

Lisa Buyer, Speaker, Author & Consultant, The Buyer Group

 

Lisa Buyer

 

In 2017 social media marketers and brands will be expected to do more in less time; short of performing social media miracles. I see the start of a new condition called Social Media Stress Syndrome.

Everyone is chasing the ROI and trying to stay on top of the constant change and introduction of new platforms, tactics and tools. Live video, Snapchat (aka Crackchat), the increasing complexities of Facebook and the unknowns behind augmented and virtual reality will be keeping social media marketers awake at night.

Prioritization

Brands will need to fine tune focus on the platforms that are most important to their audience and figure out how to make the most of them. Do an audit of best performers and eliminate the time suckers.

Better content

In 2017 content is no longer king. Social media marketers will need to step up the game and only the brands investing in talented journalistic style writers will survive.

Distributed Content Management Systems (DCMS)

Creating, reaching and publishing is going beyond WordPress with platforms such as RebelMouse introducing the first DCMS.

Creativity

With platforms such as CanvaAdobe Spark and Buffer’s Pablo, visuals are spoonfed to social media marketers. Brands will be expected to take canned visuals to the next level in 2017. Standing out in the newsfeed’s visual competition will require more than just using stock visuals.

AR/VR/MR/PR

Augmented reality (AR) virtual reality (VR) and mixed reality (MR) are the latest public relations (PR) buzzwords turning heads. Social media marketers will be faced with figuring out how to make sense of this new technology. Follow sources such as Cathy Hackl, Robert Scoble and VR Scout.

Productivity & Reporting

This is where the Social Media Stress Syndrome will come into play. Social media marketers will need to reinvent the meaning of productivity and fine tune the social media management aspects by investing in social media management dashboards with excellent reporting features such as Buffer, Hootsuite, Agorapulse, or Sprout Social.

Balance

In 2017, avoiding Social Media Stress Syndrome and maintaining digital work/life balance should be a priority in order to keep your sanity. Staying ahead of the social media marketing industry gets tougher each year and more complex.

In order to avoid being “taken down” by social media, marketers will need to find ways to reset and restore in order to stay fresh and creative without getting burned out. Apps such as Buddhify, integrating yoga into your weekly routine, and going offline for a walk at lunch are great ways begin finding balance in 2017. I’m writing my next book, “Digital Detox Secrets”, to help digital marketers find space for balance, opportunity, and productivity happiness.


 

Mel Carson, Founder, CEO & Principal Strategist, Delightful Communications

Mel Carson

Putting people first will be one of the trends we’ll see in social media in 2017.

Elections on both sides of the pond have proved that no matter how much data you have to suggest one outcome is imminent; unless you sit down with your target audience and ask them for their thoughts and opinions on whatever product or service you are trying to sell you might be barking up the wrong alley.

Also, our personal branding consulting business has tripled in the last 12 months which shows professionals increasingly see the benefit in having their wisdom and experience be more discoverable, shareable, and memorable across social networks, which is why I’ll be watching the Microsoft/LinkedIn integration with a keen eye!


 

Ashley Carlisle, Brand Relationship Strategist, Fractl

 

Ashley Carlisle

In 2016 we saw a huge surge in influencer marketing, which will no doubt continue into 2017 – but inevitably it will evolve as all trends do. The new year will see an increased emphasis on authenticity and transparency among influencers as they become more commonly integrated into social strategies.

Typically when we think of influencer marketing, we think of a product placement posted on a major celebrity’s Instagram account to hundreds of thousands (if not millions) of followers, but it goes beyond that. It can also include hashtag campaigns to encourage a genuine conversation among followers or account takeovers to help build a following. The latter examples, along with other creative, more organic ideas, will likely grow in popularity in 2017 as they emphasize authenticity and encourage engagement.

While the spotlight this year was primarily on pop culture celebrities promoting products falling into the discretionary consumer goods category (think apparel, beauty, alcohol, etc.), other brands will start looking into incorporating influencers in their 2017 social strategy.

As more research is becoming available to prove influencer marketing’s ROI, B2B and other types of B2C companies will likely join. These brands especially better work with powerful middle- and micro-influencers as they often have more influence over a more intimate, targeted audience – which also translates to more engagement.

As far as the networks themselves, Snapchat and Instagram were the networks of choice. While Snapchat has grown exponentially, Instagram will likely continue to take the lead when it comes to engagement into 2017, given its more diverse user base. Some even argue while Vine is officially dead, influencers could save Twitter with the help of livestreaming and Twitter Moments.

Even with the algorithm changes and crackdown on compliance with the FTC guidelines, influencers on social media will continue to prove their value into 2017.


 

Geoffrey Colon, Communications Designer, Microsoft

 

Geoffrey Colon

Two things for social media marketers to watch:

1. CRM

CRM via social has existed forever but now it’s a form of marketing. The better you do it, the better the word of mouth about your company, product, service spreads to others.

Reputation economics is only getting more influential on how people choose what company to use. Many companies have ignored this and as a result will pay a steep price in 2017 with either more customer churn or loss of potential new customers altogether.

2. Bots

Because of the volume of conversations that may exist in these channels, conversation bots are key.

The best companies will program and experiment with these in 2017 as they will only become more influential and allow companies who may have small staffs to handle mass quantities of inquiries through channels like Facebook Messenger, Twitter, and Skype.

 


 

Brent Csutoras, Founder & CEO, Pixel Road Designs

Brent Csutoras

If you look at the success of Pinterest, Snapchat, and Instagram, it’s clear there is a real shift in social media storytelling. It has been something optional for most businesses up until now, but I think in 2017 companies need to really give some serious thought and strategy to how they can incorporate more visual storytelling in their social media marketing plans.

Where possible, I also think it is going to be important for companies to start thinking about how they can incorporate beacon or location-based marketing efforts into their strategy as well. Virtually every app I’ve seen or been pitched this last year has had some beacon or location-based feature, so companies are going to have to start looking into incorporating this as well.

Over the past two years we’ve been building toward a significant shift in how we tell our stories through social media. In 2017 it’s time to get with the times and start embracing this shift – like now!

 


 

Melissa Fach, Social Community Manager, Pubcon

Melissa Fach

Customer service via social is huge already, but I believe it will grow in 2017. People prefer to do as much as possible via their phones, and we also know people go straight to their phones to complain about things on social media networks while they are on the go.

So, all businesses need to come up with more defined plans on how they are going to handle customer service issues via social media for 2017. Not handling complaints the right way, quickly, can result in terrible PR for any company. For example:

comcastcares unhappy customers

Some things to cover:

  • Availability hours & response times: Facebook is already giving badges for good response times and both Facebook and Twitter allow you to set availability hours & auto-respond to messages. I recommend all businesses learn about the customer service options available on both Twitter and Facebook.
  • Staff: Choosing staff is critical. Who has the temperament/self-control to handle potential and current customers the right way? Say the wrong thing and you will end up on the news.
  • Create protocols for all situations: Support, Q&A, Requests, Billing, Complaints, User Error, Crisis Management and Trolls.
  • Education: Make upper level management understand how critical social media customer service is and why resources and money are needed.

Another thing that will grow via social in 2017, video – every major social media network has focused on enhancing video options. Businesses of all sizes need to find creative ways to utilize video and to reach their targeted audiences.

Boring video just will not do. I am sure that we are going to be overrun with video, as we have been with content, soon enough. To stand out and be remembered businesses will need to go the extra mile.


 

Nikki Fica, Founder & CEO, Social Media Facelift

 

Nikki Fica

Smart brands and businesses should focus on the power of influencer marketing in 2017 for great social media success. They shouldunderstand what makes an influencer in their niche and take micro influencers into consideration.

Brands and businesses should also explore more live video options. With the rollout of Instagram’s live videos at the end of 2016 and Twitter’s livestreaming without Periscope, platforms are looking for you to share your authenticity on their platform. Show your “why” and make people fall in love with not only your product, but the brand and who is behind the brand itself.

As an avid “Shark Tank” viewer, the backstory of the entrepreneur is often reflected during the episode; where they came from, what their passions are, etc. Things that others can relate to. If a brand on social media plays a similar role and can relate to the consumer (the use of livestreaming can help tremendously), it may influence stronger.

Consumers may be more likely to purchase over a bland brand with a similar product who only talks about the product itself. Humanize your brand in 2017!


 Kat Haselkorn, Director of Content, Go Fish Digital

 

Kat Haselkorn

Measure everything. In social, it can sometimes be tricky to keep track of what works and what doesn’t across multiple platforms, but when you can show changes over time, that’s when you have something that plays into a more holistic marketing strategy.

One of the biggest complaints I’ve heard from business owners about Snapchat is the lack of metrics the platform offers its users. Their feeling is, If I’m investing all this time and energy into creating content, putting together stories, and building a following, I need to be able to prove its worth. That’s a totally fair criticism of the app.

Luckily, there have been whispers that Snapchat is unveiling more detailed reporting over the coming months and other platforms (and social media management tools like Sprout Social) seem to be headed down that path as well.

The more time you spend tracking and evaluating posts, the better your social presence will be. I can’t stress it enough: Don’t just throw something out there and see what sticks. Measure everything and use the data to adjust your social media strategy accordingly.


 Kelsey Jones, Executive Editor, Search Engine Journal

 

Kelsey Jones

Here are three social media trends you need to know in 2017:

Live Video

With the introduction of live video for Instagram, and the continuation of Facebook’s preference for live video in the newsfeed, businesses should continue to develop live video strategies and consider how it can tie into their existing marketing efforts. What events, learning opportunities, or internal team happenings are going on in the new year that could be translated into live video?

360-Degree Photos & Videos

Mark my words: this is going to blow up, due to Facebook’s acceptance of it and VR headsets. Soon you are going to be seeing brands and people posting tons of 360-degree media. Take advantage of it now by buying yourself a $100-200 camera that shoots these types of images (this is the one I have*) and beat your competitors to the punch.

Take Advantage of Your Data

I’m always amazed at the data we have at our fingertips when it comes to Google Analytics and Facebook Insights, as well as Twitter analytics. Instead of glancing over it each month to see how many new likes your pages or profiles got, really buckle down and look at what types and topics of posts do best. Dedicate yourself to taking more action based on data, and your social media presence will only continue to grow.

*Disclosure: This is an SEJ affiliate link


 Jordan Kasteler, Marketing Consultant & Entrepreneur

 

Jordan Kasteler

2017 is sure to bring about many new social media trends. There may even be new, hot social networks that spring up and give currently dominate social networks a run for their money.

However, before getting involved with any new trend or site be sure that it’s a right fit for your audience. If not, you may be wasting your time drawing people toward you that aren’t interested in your product or service.

As for big trends to focus on, here are four:

Ephemeral Social Media

In other words, Instagram and Snapchat Stories are content that disappears within a limited amount of time. As this is the new rage, it keeps people coming back to these social networks to view daily content before it disappears. Use ephermal social media wisely but posting timely content, gifts for your audience, contests, show behind the scenes, etc.

Live Video

This is another trend social networks are allowing users to take advantage of. Instagram now has a live video option. This is another opportunity to take your users behind the scenes and provide real-time content.

Paid Social

More social networks are moving toward a pay-to-play landscape. As organic reach declines, it’s more important to pay for visibility. Social networks will continue to grow their abilities for businesses to narrowly target their users by demographic, psychographic, and technographics.

Mobile

With social traffic referring from mobile over desktop, it’s important that your content is speedy and provides a good user-experience on a smartphone with 4G bandwidth.


 Katy Katz, Senior Consultant, SmartBug Media

 

Katy Katz

Social media platforms are continually searching for ways to artificially reproduce the sentiments of real human interaction within a platform that is inherently non-human. This is why images are more popular than text, videos are more effective than stills, and live video is starting to gain traction.

This race to replicate human contact is only going to continue in 2017 and beyond; especially as 360 technology and virtual reality start to penetrate the market more deeply.

Brands should be thinking about ways to increase consumer access to that human factor – through strategic campaigns as well as organic interactions. That will be the best way for companies to improve their social performance in 2017.


 Jabez LeBret, CMO, GNGF

 

Jabez LeBret

We are about to see a move towards live video for brands. This will take two main forms including brand events and non-brand sponsored content.

For non-brand sponsored events the customer will become the promoter and producer. This user-generated content will be scary for many brands.

Years ago I wrote an article on Forbes comparing brand marketing to an API. Regardless of if brands are interested in letting the customer market the brand, they do not have a choice.

This means companies should be more proactive in engaging users to submit content. Instead of fighting the trend, get ahead of issues by facilitating the messaging and delivery. This will require companies to become proficient at creating live video content.

It would be wise to test various methods of encouraging your customers to create content on behalf of your brand. We are entering a new era of social media marketing and it is both scary yet exciting at the same time!


 Debbie Miller, President, Social Hospitality

 

Debbie Miller

Social video will continue to be a key trend in 2017. Between Snapchat, Instagram Stories, and Facebook Video, the options are becoming more vast and are constantly evolving.

It will be critical for brands to formulate how to best optimize their video strategy for maximum impact. It’s easy to get bogged down in scheduling written copy and photos, but video is a field that should be given more time and consideration moving forward.

With the rise in video comes the lure of real-time content. Both Instagram and Facebook are leveraging their live streaming components and businesses are able to connect with their audiences in unprecedented ways as a result. Businesses should consider the best routes for optimizing live video content, whether it be interviews, behind-the-scenes tours, exclusive announcements, etc.

One important thing to remember is that the social media world is constantly evolving, and the pace seems to be constantly becoming more rapid. It’s important to stay on top of current trends and changes so that you’re not left behind. It’ll be beneficial for companies to adopt more training and development of their teams to ensure comfortability in the space across your organization.


 

Merry Morud, Senior Creative Strategist, aimClear

 

Merry Morud

 

No longer will social marketers be considered the scrappy rebel force, existing on the fringe of marketing and pointing to false idol metrics. Social media marketers must integrate into the greater multi-touch nurture marketing ecosystem – digital and otherwise – to not only survive but become an undeniable force in 2017.

And BTW, it’s social’s job to integrate, not everyone else’s, for the greater marketing good.

Social marketers will be responsible for making money in a multi-touch environment. One or two-touch conversions in any channel are a finite asset after that brands need to (still) nurture with content.

Social will become a more powerful ROI-positive machine as marketers tap even more consumers further down the funnel with lookalike modeling and clean up retargeting rebounds from other performance marketers by layering on psychographic filters and promoting content that solves problems, demystifies, answers questions, explains benefits, empowers users and removes barriers to purchase.

Filtered performance retargeting has the potential to radically redefine how higher level marketers view social. So social marketers seeking to stay relevant should take heed.

If you didn’t notice, 2016 has been a wake-up call for, well, just about everyone as it peeled back the veil on just how insidious social, “news,” and search truly are. Social drives news. News drives search. And perception is reality. Social propagation is that which can create or cause cultural shifts and the goal of branding is a cultural shift.


 Rebecca Murtagh, Founder & Chief Strategist, Karner Blue Marketing

 

Rebecca Murtagh

In 2017, the race for social media fans, followers, likes, etc. will be overshadowed by a paradigm shift toward relationship cultivation. And, in case you haven’t noticed, this shift has already begun in a big way.

Relationships are the future of social media. Here are five reasons why:

1. Aggregation is So 2016

In 2017 In-tune marketers will shift the focus of social media from vanity metrics (followers, fans, likes, etc.) to relationships. The longer the relationship, the greater the return on investment, and lower cost of acquisition. The follower, fan or connection that has never been touched by the brand has zero value.

As attractive as it may be to report growth of the audience, sustainability of the brand will be determined by revenue. Winning hearts and minds is more important than ever to brands seeking to cultivate leads, customers, and champions. And, it will take a lot more than personalized website pages and emails.

2. Social Media Offers So Much More Than Mass Media

To make social media manageable, marketers have largely reduced social media into a new form of mass media. Broadcasting messaging without leveraging the social aspect of the channel leaves most of the unique potential of social platforms untapped.

Audience aggregation merely reflects the first introduction, a handshake if you will. Today’s consumer wants to do business with brands they trust. Trust has been redefined from just offering a quality product or service, to meeting the expectations of audiences in a transparent, ethical manner.

This is especially true with millennials, who will be between the ages of 20-37 years old in 2017. The good news is that millennials are willing to reward brands they trust.

Millennials are seven times more likely to give personal information to a trusted brand. In fact, 46 percent of surveyed millennials said they would share personal data if in exchange they received a more consistent, relevant, personalized experience, complemented by free perks, discounts and better customer service, across all platforms.

3. It’s a New Era

Millennials have, and will continue to, yield tremendous influence over consumer and B2B purchases. No longer youngsters, millennials will not only make purchase decisions differently than previous generations, as a “digital-first” generation, they will influence the decisions of Gen Y and Baby Boomers for years to come.

Millennials expect reciprocity; a two-way, mutual relationship with companies and their brands, and they consider a brand’s social, environmental or philanthropic efforts when making purchase decisions.

4. Social is Part of the Omnichannel Experience

More than 85 percent of millennials and 75 percent of baby boomers are ready for omnichannel interactions. Brands may not fully understand how broad this expectation is.

Omnichannel is often referred to as seamless integration between on and offline customer experiences. We have seen studies and surveys over the years reveal how consumers use multiple devices, across multiple channels, and across media channels for news, social interaction, job searches, shopping, and solutions for work, business and life.

The social experience is as important as the in-store, face-to-face, or website interaction with the brand. In addition to seek a “a hassle free, omnichannel, client experience personalized to their needs”, according to an IBM report.

Engagement requires much more effort than merely broadcasting to the masses. This is a tough pill for many brands to swallow.

Many have not kept up with the expectations of their audiences. And, in doing so, these brands have essentially begun the spiral into self-imposed obsolescence and extinction.

5. Tribes, Community & Crowds

There is untapped potential inherent to social media that can help brands connect with member of the audience, while connecting audiences to one another.

People don’t want to just do business with a business. They want to be connected with the people behind the business, and they want know how the business interacts with customers like them.

This is why review websites, social sharing of content, crowdfunding, etc. have been so effective. The power of tribes, community, and crowds have only begun to realize their potential.

The next generation of social media will promote greater access and transparency between brands and their fans, creating the sense of belonging and community the next generation craves.

Brands that embrace this new normal and invest in building relationships will be the winners in 2017.


 

Maddy Osman, SEO Copywriter & Founder, The Blogsmith

 

Maddy Osman

Here are two big social media marketing trends for 2017:

1. Instagram

Instagram will become a major player of the top social networks, thanks in part to tactics that effectively take a direct attack on Snapchat (like Instagram’s own “Stories”). Once their new Shopping feature is released to all brands, more clickable links will mean more conversions for retailers.

Facebook owns Instagram, so that means the Instagram ad platform will continue to evolve in the right direction. In my opinion, Instagram’s ad platform has yet to peak (in terms of saturation), and there are still plenty of opportunities for brands to stand out and accomplish specific goals.

2. Video

Video will continue to be important in 2017, but brands will need to keep innovating in the way it’s presented. You still don’t necessarily need high production tactics to be effective, but you should experiment with new technologies, like 360 Video.

Make sure to keep in mind the role sound plays, or doesn’t play in many cases. Many people watch video without sound, so make sure captions are enabled, and that you include the video’s title in the first frames of the video.


 

Erik Qualman, Bestselling Author & Motivational Speaker

 

Erik Qualman

 

Video killed the social media photo. While it seems obvious, the obvious isn’t always easy to execute.

2017 is the year that social goes truly video. Brands will need to invest in both beautifully produced video as well as more organic video adaptations.

There will be a window of time where quality video will be able to help separate your business/brand. However, that window will shrink as advances in technology make artistic video common place.


 

Michelle Stinson Ross, VP of Marketing & Client Relations, K’nechtology

 

Michelle Stinson Ross

Two trends and one time-tested principle. First for the trends.

Live Video

As each year goes by more options on more devices lead to a lot of noise. What can brands big and small do to cut through that noise?

Video has always been key, especially for reaching an audience on mobile devices. Live video broadcasting on Facebook, Twitter, LinkedIn and YouTube are indicators that authentic interactive moments are here to stay.

Live video gives small brands an option to level the playing field without having to spend big production budgets. Live interactions also give brands a chance to test video content with instant and real feedback from their established communities.

But with live video broadcasting comes the necessity to have a quality brand spokesperson. That spokesperson must be able to talk both fluidly and fluently about the brand and it’s products/services. That spokesperson also needs to be someone who is relevant and relatable to a brand’s potential customers.

Yeah, you’re CEO may not be the best choice here. Save them to be interviewed by the spokesperson as a subject matter expert instead.

Influencer Marketing

Another way to cut through the noise in social media is to partner with influential users. It’s important to put just as much commitment to time an energy cultivating influencers (movers and shakers) as it is your customer base.

Getting the attention of someone that’s never heard of your brand before gets more and more difficult every year. Securing influential advocates to share is critical to that top of funnel awareness.

But don’t count on your free samples to be enough to entice influencers to shout your praises. You are going to have to market the value of a business partnership to them just like you are marketing your product/service to your customers.

A Time-Tested Principle

Make marketing a priority and not an afterthought. One of the issues we see consistently with new clients is the dawning realization that they need marketing.

Too many startups focus every resource on developing their ideas without considering how they are going to attract investors and customers. Struggling businesses tend to cut marketing budgets first.

The businesses large and small that move marketing up the priority list from luxury to necessity will always come out ahead of those that don’t. Just because there are a lot of self-serve DIY marketing options available doesn’t mean that brand’s can skimp on marketing budgets. If anything, it requires more time and attention to resources, personnel and media spend.

Commit to marketing and know when to hire, either in-house or a consultant/agency.


 

Jes Stiles, CMO Emerging Markets, Ringier AG

Jes Stiles

Building your own chatbot to distribute your content (ideally for Facebook Messenger). Why?

Traditional social media is fraught by algorithms and ads. More and more, we see not only millennials, but now also the other generations, moving away from broadcasting focused social media posting and towards narrowcasting in smaller message groups or 1-1.

A messenger bot can allow you to have personalized 1-1 conversations at scale, opening up a whole new audience who does not wish to connect with brand over email or download an app. Moreover, when built in a user-friendly manner, chatbots can actually provide a better experience than a human for common use cases with faster response times (no matter what time or day of the week) and greater personalization of content.

For examples of good bots in action, check out TechCrunch or eBay Messenger bots.


 

Bas van den Beld, Digital Marketing Consultant, Speaker & Trainer

 

Bas van den Beld

With the “fake news” discussion in 2016, the overflowing amount of posts on social media and the changing algorithms, things are about to change in 2017.

What is bound to happen is a trend in which brands and businesses have to “prove” they are legit. That they know what they are talking about and that they (will) do a good job.

This means more focus on helping clients and consumers. Customer service through social media will be more important than ever. If there is any trend businesses should focus on, it’s getting their business ready for that.


 

Ashley Ward, Director of Marketing, Madhouse Matters

Ashley Ward

Video. Video has already taken over social media in 2016 and has helped social media pages increase their engagement, conversions, and exposure for brands. In 2017, I predict an even larger increase in video posts from brands and businesses.

I’m not just talking about Facebook and Instagram, either. Facebook Live is helping brands create more organic videos and less production-heavy, which has been enjoyable for users.

But, Snapchat and Instagram Stories are great resources to show customers an “insider’s view”, give product demonstrations, and tours through video. You can then reuse this video content on other social media channels like Twitter and LinkedIn.

Unlike images, which one single image shouldn’t be used multiple times due to image fatigue, one video can be clipped into multiple 5-, 30-, and 60-second clips and then shared on different social media outlets to provide followers with unique content.

If you haven’t already started thinking about adding more video content in 2017, start now.


 

Tessa Wegert, Freelance Journalist & Branded Content Developer

Tessa Wegert

 

The single biggest social media trend coming our way has got to be live streaming video. We’ve seen the live video market grow with Meerkat and Periscope, but now that Facebook is putting all its weight behind Facebook Live

Consumers are becoming more accustomed to seeing – and seeking – live video content. Brands can continue to push the boundaries and provide exciting live experiences for their customers and fans.

With live video, companies can take consumers behind the scenes in real-time, and consumers dig that kind of authenticity. I think we’re going to see some pushback against all of those staged, polished, and over-filtered Instagram posts brands have been investing in as consumers become disenchanted with social media marketing that feels as forced as the TV commercials and print ads of yesterday.

Live video is the cure for synthetic content, and brands that embrace the opportunity to take consumers inside their factories and test kitchens, to their photo shoots and runway shows, and backstage at the concerts and events they’ve been sponsoring for years will be rewarded with increased loyalty and affinity.


 

Dennis Yu, Chief Technology Officer, BlitzMetrics

Dennis Yu

Instead of trying to crank out endless content to distribute on a growing number of channels– a challenge for the modern day Sisyphus– get your customers to do the work for you. Here’s how to specifically do this, even if you have a tiny team and tiny budget.

1. Can I quote you on that?

Say this to anyone who has something nice to say about you – especially if on Facebook, Twitter, YouTube, or other sites. But often, these comments are coming through in direct mail, in store, at conferences, or when your customers are getting serviced. When you ask this question, they almost always say YES.

2. Place that quote in a spreadsheet.

Have columns for who said it, the category of customer, the type of comment (value, great quality, great service – however you bucket pain points), source, headshot of the person, and permission flag.

3. Make a Facebook organic post.

Do multiple images in a carousel and boost to lookalike audiences or custom audiences, depending on what stage in the funnel. The key is to have 10-15 of these posts. Test carousel versus not. You may find that video performs best, in carousel or not, as video view objective or for website clicks. Test it.

To go further in how to boost Facebook posts, see Digital Marketer’s most popular podcast episode of all time here.


 

Ashley Zeckman, Director of Agency Marketing, TopRank Marketing

 

Ashley Zeckman

Many brands today (even some of the best ones) are still struggling with one key element that leads to social media marketing success: understanding the people that they want to interact with.

In 2017, I think that smart brands will shift their focus from pushing messages out, to personalizing communications for a more meaningful interaction. That means it will be less about the on-page interactions and more about personal exchanges with prospects, customers, and influencers.

The rise of influencer marketing will make this shift even more imperative for brands that want to get on the radar of busy experts. There are a variety of tools that exist today, and many that I’m sure will be developed in the coming years that provide helpful insights into the habits and minds of your social media audience.

It’s our job as marketers to use that data to create a more inclusive, one-to-one experience in an environment that everyone is engaged with; social media platforms.


 

OK – the experts have spoken. Your turn! What do you think will be the biggest social media trends in 2017?

Author:  Danny Goodwin

Source:  https://www.searchenginejournal.com/social-media-trends-2017/181768

Categorized in Future Trends

We have jobs to pay the bills, typically. Some of us are fortunate enough to have jobs that allow for exploration and indulgence in certain passions or to push the boundaries of technology and innovation. But by and large, people go to work because they have to — their jobs earn them money, and with that money, they make a living.

As the modern economy churns and turns, some jobs simply become redundant or less valuable. That can happen for a number of reasons, ranging from automation to an influx of cheaper labor. When it does happen, though, wages drop as the labor market become saturated. When there are more people with similar skill sets as you, odds are there are people out there who are willing to do the same job for less pay.

That’s when you either take a pay cut or lose your job entirely. There are a lot of factors at play, but at the very core of it is a supply-demand dynamic.

As of right now, if you’re a software engineer, this is good news. If you’re in manufacturing? It’s not — and as you’ll see on the following pages, manufacturing specifically is a segment of the economy that is being hit very, very hard by globalization and automation.

Which jobs are experiencing negative wage growth, or at least a very bleak outlook going into 2017? Using data from the Bureau of Labor Statistics, here are 10 you’ll either want to avoid or get out of as soon as possible.

1. Apparel manufacturing

If you’ve noticed that just about all of your clothing is made in Asia or Central America, there’s a reason for that: cheap labor. Apparel manufacturing in the United States is on a steep decline, with wages dropping too for those still in the industry. This is why clothing that is made in America tends to be much more expensive than other options.

2. Tobacco production

The BLS labels this as “tobacco manufacturing,” and it’s another area in the manufacturing and production sector that is seeing jobs disappear and wages go down. It probably has a lot to do with the fact that demand for tobacco is dropping significantly as smoking becomes less and less common.

3. Postal service

Jobs with the postal service aren’t what they used to be, and there are a number of reasons that USPS has been experiencing trouble for several years now. It’s been shedding jobs and funding, and it’s one area in which wages aren’t exactly on the up and up — if you were considering trying to get a job there.

4. Communication equipment manufacturing

This is a pretty broad category, but it’s essentially referring to things like phones, computers, tablets — really any type of device or gadget that we use to communicate. As most people know, almost all of these things are made in other countries to take advantage of cheaper labor costs, and to pass those savings on to American shoppers.

5. Publishing

The digital age has made it tough for traditional publishing companies to survive. Some are, but they’re not printing money like they used to. Jobs are scarcer, and they don’t pay nearly as much as they did in the glory days of publishing.

6. Textile production

“Textile production” is another incredibly vague category, but it’s another area in which we’re seeing jobs either replaced with cheaper foreign labor or automation.

7. A/V equipment manufacturing

Audio and video equipment, like communication equipment, is almost exclusivelly produced in foreign markets. Again, to take advantage of cheap labor. This includes things like your TV, cameras, stereos, etc.

8. Glass manufacturing

Here’s an industry you probably haven’t given much thought to — glass production. Glass is everywhere, but you don’t often think about who is producing it, or where. Well, it’s an industry that is seeing some serious contraction in the U.S., and because of that, the jobs within aren’t paying very well.

9. Paper production

You may not know much about the paper industry other than what you’ve learned from those Dunder Mifflinites on The Office, but as far as production of paper goes, it’s rough out there for workers. Paper mills are contracting, and workers are seeing wages stagnate.

10. Miscellaneous manufacturing

Our final installment is as broad as it gets. The BLS includes “miscellaneous manufacturing” among its “most rapidly declining wage and salary” list. That may or may not include many of the aforementioned industries, but includes many others as well. The point is manufacturing, a former backbone of the American economy, is on the outs. If you’re hoping to make big money, you’ll need to do it in another way.

Source: This article was published on cheatsheet.com by Sam Becker

Categorized in News & Politics

Wow! What a year 2016 has been. The big data industry has significant inertia moving into 2017. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. Enjoy!

IT becomes the data hero. It’s finally IT’s time to break the cycle and evolve from producer to enabler. IT is at the helm of the transformation to self-service analytics at scale. IT is providing the flexibility and agility the business needs to innovate all while balancing governance, data security, and compliance. And by empowering the organization to make data-driven decisions at the speed of business, IT will emerge as the data hero who helps shape the future of the business. – Francois Ajenstat, Chief Product Officer at Tableau

In 2017, we’re going to see analytics do more than ever to drive customer satisfaction. As the world of big data exploded, business leaders had a false comfort in having these mammoth data lakes which brought no value on their own when they were sitting unanalyzed. Plain and simple, data tells us about our customers — it’s how we learn more about customers and how to better serve them. As today’s customers expect a personalized experience when interacting with a business, we’re going to see customer analytics become the spinal cord of the customer journey, creating touch points at every level of the funnel and at every moment of interaction. – Ketan Karkhanis, SVP and GM of the Salesforce Analytics Cloud

Knowing the Unknown Unknowns – Enterprises that apply Big Data analytics across their entire organizations, versus those that simply implement point solutions to solve one specific challenge, will benefit greatly by uncovering business or market anomalies or other risks that they never knew existed. For example, an airline using Big Data to improve customer satisfaction might uncover hiccups in its new aircraft maintenance scheduling that could impact equipment availability. Or, a mobile carrier looking to grow its customer base might discover ways to improve call center efficiency. Discovering these unknown unknowns can enable organizations to make changes or fix issues before they become a problem, and empower them to make more strategic business decisions and retain competitive agility. – Laks Srinivasan, Co-COO, Opera Solutions

Democratization of Data Analysis – In 2017 I believe that C-suite executives will begin to understand that there is a real gap between their data visions and the ability of their enterprise to move data horizontally throughout the organization. In the past, big data analysis has lagged in implementation compared to other parts of the business being transformed by advanced technology such as supply chains. I believe companies will begin to place different data storage systems into the hands of end users in a fast and efficient manner that has user self-direction and flexibility, democratizing data analysis. –  Chuck Pieper, CEO, Cambridge Semantics

The battleground for data-enriched CRM will only continue to heat up in 2017. Data is a great way to extend the value proposition of CRM to businesses of all sizes, especially those in the small-to mid-size range. By providing pre-populated data sets, the amount of “busy work” done by sales and other CRM users is reduced, and the better the data, the more effective individuals can be every moment of the day. A lot of M&A as well as in-house development and partnerships will fuel more data-powered CRM announcements in 2017. The key, of course, is seeing which providers provide the most seamless and most sensible use cases out of the box for their customers.” – Martin Schneider, Vice President of Corporate Communications, SugarCRM

In 2017 (and 2018), streaming analytics will become a default enterprise capability, and we’re going to see widespread enterprise adoption and implementation of this technology as the next big step to help companies gain a competitive advantage from their data. The rate of adoption will be a hockey stick model and ultimately take half the time it has taken Hadoop to rise as the default big data platform over the past six years. Streaming analytics will enable the real-time enterprise, serving as a transformational workload over their data platforms that will effectively move enterprises from analyzing data in batch-mode once or twice a day to the order of seconds to gain real-time insights and taking opportunistic actions. Overall, enterprises leveraging the power of real-time streaming analytics will become more sensitive, agile and gain a better understanding of their customers’ needs and habits to provide an overall better experience. In terms of the technology stack to achieve this, there will be an acceleration in the rise and spread of the usage of open source streaming engines, such as Spark Streaming and Flink, in tight integration with the enterprise Hadoop data lake, and that will increase the demand for tools and easier approaches to leverage open source in the enterprise. – Anand Venugopal, Head of Product, StreamAnalytix, Impetus Technologies

The unique value creation for businesses comes not just from processing and understanding transactions as they happen and then applying models, but by actually doing it before the consumer, or the sensor, logs in to do something. I predict we will quickly move from post-event and even real-time to preemptive analytics that can drive transactions instead of just modifying or optimizing them. This will have a transformative impact on the ability of a data-centric business to identify new revenue streams, save costs and improve their customer intimacy. – Scott Gnau, Chief Technology Officer, Hortonworks

Text analytics will be subsumed by ML/AI in 2017. The terms Text Mining and Text Analytics never really gained the kind of cachet and power in the marketplace that most of us hoped they would. This year will see the terms be subsumed by ML/AI and they’ll become component pieces of AI. – Jeff Catlin, CEO, Lexalytics

IT will start automating the choices for data management and analysis, leading to standardized data prep, quality, and governance. BI tools have been making more decisions for people and automating more processes. The knowledge for doing this — e.g., choosing one chart type over another — was embedded into the tools themselves. Data prep and management tends to be different, because the required rules are specific to the business requirements rather than being inherent in the data. Rule-based data management will enable IT to define rules that the business uses in its analytics processes, making business analysts more productive while still ensuring reliability and reproducibility. For a use case, consider a data scientist who sources data externally, and lets the data tools automatically choose which enterprise data prep and cleansing processes need to be applied. – Jake Freivald, Vice President, Information Builders

Managing the sprawl: Self-service analytics technologies have put analysis into the hands of more users and as a byproduct, led to the creation of derivative artifacts: additional datasets and reports, think Tableau workbooks and Excel spreadsheets. These artifacts have taken on a life of their own. In 2017, we will see a set of technologies begin to emerge to help organize these self-service data sets and manage data sprawl. These technologies will combine automation and encourage organic understanding, guided by well thought-out, but broadly applicable policies. – Venky Ganti, CTO, Alation

We will move from “only visual analysis” to include the whole supply chain of data. We will eventually see visualizations in unified hubs that show us more data, including asset management, catalogs, and portals, as well as visual self-service data preparation. Further, visualizations will become a more common means of communicating insights. The result of this is that more users will have a deeper understanding of the data supply chain, and the use of visual analysis will increase. – Dan Sommer, Senior Director and Market Intelligence Lead, Qlik

Artificial Intelligence

AI, ML, and NLP innovations have really exploded this past year but despite a lot of hype, most of the tangible applications are still based on specialized AI and not general AI. We will continue to see new use-cases of such specialized AI across verticals and key business processes. These use-cases would primarily be focused on the evolutionary process improvement side of the digital transformation. Since the efficiency of ML is based on constant improvement through better and wider training data, this would only add to the already expanding size of the data enterprise needs to manage. Good data management policies would be key to achieving a scalable and sustainable AI vision. For the business users this would mean better access to actionable intelligence, and elimination of routine tasks that can be delegated to the bots. For users who want to stay relevant in the new economy, this would allow them transform their roles in to knowledge workers that focus on tasks that can still only be done based on the general intelligence. Business users that can train the AI models would also be very hot commodity in the economy of future. – Vishal Awasthi, Chief Technology Officer, Dolphin Enterprise Solutions Corporation

Why machine-led, human-augmented intelligence is the next tech revolution – In 2017, more C-suite executives are going to prioritize data-driven business outcomes. As C-level executives see the potential for analytics, they’ve begun to show greater participation in getting analytics off the ground in their organizations, and I expect they’ll be leading the charge this year to ensure insights permeate every level and department of the business. All of the true technological revolutions have happened when people at a mass scale are empowered. So, shifting data science from an ivory tower function to giving everyone in an organization access to advanced, interactive AI will help each employee become smarter and more productive. It’s becoming clearer that when data can inform each and every decision a business user is making, businesses are going to see a real a competitive advantage and business outcome. – Ketan Karkhanis, SVP and GM of the Salesforce Analytics Cloud

Graph-Based Databases for Emerging Tech – The key applications companies are exploring — IoT, machine learning and AI – will be constrained by relational database technology. These areas will move towards sitting on top of graph-based architecture, which by definition, expands much more quickly in response to the output of those learnings. If you think of AI, it cycles back on data many, many times, and once it has a conclusion, it asks for more information. If that information in a relational format is not already there, all those AI, IoT and machine learning programs stop. But if it’s on a graph-based arch it automatically allows itself those multiple levels of joins to bring in more information. That will help unleash the real potential of some of those new technologies. – –  Chuck Pieper, CEO, Cambridge Semantics

The symbiotic relationship between man and machine will enable better decisions. Machines will never replace man, but they will empower and complement the data-driven efforts of workers in the coming years, especially as data becomes more accessible across departments and organizations. The democratization of data, the self-service movement and data’s continued simplicity means more people will be leveraging it in more applications – paving the way for a better man vs. machine relationship. For example, IBM Watson can go through medical papers, research and journals and then present top choices, but only a trained doctor can make the right decision for a specific patient. Adding to that, the reskilling of the workforce through nanodegrees will simplify data even further. Technology is sharpening the workforce and putting the power of data into the hands of business users – AI and machine-learning will only help them achieve more.” – Laura Sellers, VP of Product Management, Alteryx

My prediction about Big Data is that it will be subsumed into the topic of AI, as big data is an enabler of AI not an end in itself. The lack of focus on big data will actually let the field mature with only the serious players and result in much better business results. – Anil Kaul, Co-Founder and CEO of Absolutdata

Companies will stop reinventing the AI wheel. More and more companies are applying artificial intelligence and deep learning into their applications, but a unified, standardized engine to facilitate this process has lagged behind. Today, to insert AI into robots, drones, self-driving cars, and other devices, each company needs to reinvent the wheel. In 2017, we will see the emergence of unified AI engines that will eliminate or greatly mitigate these inefficiencies and propel the formation of a mature AI tech supplier industry.” – Massimiliano Versace, cofounder and CEO, Neurala

AI will (still) be the new black. One topic that was covered ad nauseam in 2016 was AI. While it’s important to be cautious about all of the AI hype (especially when it comes to use cases that sound like science fiction), the reality is that this technology is going to evolve even faster from here on out. It’s just in the past few years that innovative business-to-business companies have started using AI to achieve specific business outcomes. Keynoters at this year’s IBM World of Watson conference highlighted ways in which it is already delivering impressive business value, as well as examples of how it might help a CEO decide whether to buy a competitor, or help a doctor diagnose a patient’s symptoms in just the next three to five years. – Sean Zinsmeister, Senior Director of Product Marketing, Infer

Artificial intelligence (AI) initiatives will continue, but in the vein of commoditisation – AI is garnering interest in the legal sector, but a closer inspection of the tools and apps being made available reveal that they are presently more similar to commoditised legal services in the form of packaged, low cost modules for areas such as wills, contracts, pre-nuptials and non-disclosure agreements for the benefit of consumers. Undoubtedly, AI offers tremendous potential and some large law firms have launched initiatives to leverage the technology. However, there’s a significant amount of work to be done in defining the ethical and legal boundaries for AI, before the technology can truly be utilised for delivering legal services to clients with minimal human involvement. Until then, in 2017 and perhaps for a few more years yet, we will continue to see incremental innovative efforts to leverage the technology, but in the vein of commoditisation – similar to what we have seen in the last 12 months. – Roy Russell, CEO of Ascertus Limited

AI and analytics vendor M&A activity will accelerate — There’s no doubt that there’s a massive land grab for anything AI, machine learning or deep learning. Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle’s Adaptive Intelligent Applications. What’s well understood is that AI needs a consistent foundation of reliable data upon which to operate. With a limited number of startups offering these integrated capabilities, the quest for relevant insights and ultimately recommended actions that can help with predictive and more efficient forecasting and decision-making will lead to even more aggressive M&A activity in 2017. – Ramon Chen, CMO, Reltio

AI and machine learning are already infiltrating the workforce across a multitude of industries. In fact, when it comes to HR and people management, more and more companies are starting to deploy technologies that bring transparency to data around the work employees do. This is creating huge opportunities for businesses to leverage frequent touch points, check-ins and opportunities to provide feedback to employees and get a holistic picture of what’s driving work. In 2017 we can expect to see data and analytics used more in HR and management to help visualize behaviors of employees, from the time they were hired to their success down the road, and understand why they have been so successful. By using machine learning companies can focus on building teams to support long-term goal achievement, instead of frantically hiring to fill immediate needs. – Kris Duggan, CEO of BetterWorks

Artificial intelligence (AI) is rapidly becoming more accessible. Previously, you needed a lot of training to implement AI, but this is becoming less and less true as technology becomes more intelligent. Over the next several years, we can expect AI to become more of a commodity and companies like Google and Microsoft will make it extremely easy for developers to analyze large amounts of data on their platform. Once that data analysis is done, developers will be able to implement processes based on those results, which is essentially AI. In the next year we can expect that AI will become much easier to implement for developers via API calls into their applications. – Kurt Collins, Director of Technology Evangelism & Partnerships, Built.io

This year we saw customer interactions evolve from traditional question and answer dialogues, to intelligent machines now enhancing the process and experience. Machines are learning patterns and providing answers to customers to help eliminate some of the mundane tasks that customer service agents used to handle; and intelligent machine personas like the Alexa in the Amazon Echo and Siri in various Apple devices, are paving the way. In 2017, we’ll see more capabilities when it comes to artificial intelligence and customer service like Alexa triggering a call from contact center based on a question about online order status, thermostats submitting a trouble ticket after noticing a problem with the heater, or Siri searching through a cable company’s FAQ to answer to a commonly asked question about internet service troubleshooting. However, one thing will always remain true – human interactions will still be critical when dealing with complex situations or to provide the empathy that is needed in customer service. – Mayur Anadkat, VP of Product Marketing, Five9

For some, the mere mention of artificial intelligence (AI) corresponds to a fashion return from decades ago. So yes, those wide ties are back, and in 2017 we’ll see the rapid adoption of AI in the form of relatively straightforward algorithms deployed on large data sets to address repetitive automated tasks. First a brief history of AI. In the 1960s, Ray Solomonoff laid the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction. In 1980 the First National Conference of the American Association for Artificial Intelligence (AAAI) was held at Stanford and marked the application of theories in software. AI is now back in mainstream discussions and the umbrella buzzword for machine intelligence, machine learning, neural networks, and cognitive computing. Why is AI a rejuvenated trend? The three V’s come to mind: Velocity, Variety and Volume. Platforms that can process the three V’s with modern and traditional processing models that scale horizontally providing 10-20X cost efficiency over traditional platforms. Google has documented how simple algorithms executed frequently against large datasets yield better results than other approaches using smaller sets. We’ll see the highest value from applying AI to high volume repetitive tasks where consistency is more effective than gaining human intuitive oversight at the expense of human error and cost. – John Schroeder, Chairman and Founder, MapR

The Cognitive Era of computing will make it possible to converge artificial intelligence, business intelligence, machine learning and real-time analytics in various ways that will make real-time intelligence a reality. Such “speed of thought” analyses would not be possible were it not for the unprecedented performance afforded by hardware acceleration of in-memory data stores. By delivering extraordinary performance without the need to define a schema or index in advance, GPU acceleration provides the ability to perform exploratory analytics that will be required for cognitive computing. – Eric Mizell, Vice President, Global Solutions Engineering, Kinetica

We expect three of the well-funded ML/AI companies to go out of business, while a number of the lesser funded companies will not get off the ground. In addition, we’ll lose more than a few pure-play text analytics companies as ML/AI subsumes more and more of the functionality. The influx of cash isn’t infinite, and companies will need to learn the importance of ROI/TCO analysis. Do you really need a slide or firepole between floors? No. Do you need to have budget for things like, say, salary and advertising, yes. Another common failure will be over-investing in the engineering aspect of the business. While it’s critical to have a great product, people also need to hear about it. If you can’t clearly articulate your business necessity, then it doesn’t matter how cool the product is. – Jeff Catlin, CEO, Lexalytics

Deep Learning will move out of the hype zone and into reality. Deep learning is getting massive buzz recently. Unfortunately, many people are once again making the mistake of thinking that deep learning is a magic, cure-all bullet for all things analytics. The fact is that deep learning is amazingly powerful for some areas such as image recognition. However, that doesn’t mean it can apply everywhere. While deep learning will be in place at a large number of companies in the coming year, the market will start to recognize where it really makes sense and where it does not. By better defining where deep learning plays, it will increase focus on the right areas and speed the delivery of value. – Bill Franks, Chief Analytics Officer, Teradata

By the end of 2017, the idea of deep learning will have matured and true use cases will emerge. For example, Google uses it to look at faces and then determine if the face is happy, sad, etc. There are also existing use cases in which the police is using it to compare the “baseline” facial structure to “real time” facial expressions to determine intoxication, duress or other potentially adverse activities. – Joanna Schloss, Director of Product Marketing, Datameer

The future of all enterprise processes will be driven by Artificial Intelligence, which requires the highest quality of data to be successful. AI is where all business processes are headed; however, with the recent push of AI technology advancements for businesses – many companies have not addressed how they will ensure that the data their AI models are built on is high quality. Data quality is key to pulling accurate insights and actions and in 2017, we will see more companies focus on solving the challenge of maintaining accurate, valuable data, so that AI technology lives up to its promise of driving change and improvement for businesses. – Darian Shirazi, CEO and Co-Founder, Radius

Prediction: Artificial Intelligence will Create New Marketing Categories, Like the B2B Business Concierge. In 2017, AI will allow marketers to create highly personalized ads tailored to buyer’s specific interests in real-time through superior and infinite knowledge. AI will also make mass email marketing tools obsolete (and the resulting spam email), automatically scanning out the “bad” leads and creating custom, personalized communication instead. As AI continues to advance, we can expect to see the recommendation engines that power companies like Netflix and Amazon develop specifically for the B2B market. This will start to pave the way for a B2B business concierge – a completely automated and customized buyer’s journey throughout the funnel that is driven by AI. – Chris Golec, Founder & CEO, Demandbase

AI-as-a-Service will take off: In 2016 AI was applied to solve known problems. And as we move forward, we will start leveraging AI to gain greater insights into ongoing problems that we didn’t even know existed. Using AI to uncover these “unknown unknowns” will free us to collaborate more and tackle new, interesting and life-changing challenges … AI will amplify humans: We have made enormous leaps forward to build machines capable of understanding and simulating human tasks, even mimicking our thought process. 2017 will be the year of knowledge-based AI, as we develop systems based on knowledge, which learn and retain knowledge of prior tasks, rather than pure automation of tasks we want performed. This will completely disrupt the way we work as human capabilities are amplified by machines that learn, remember and inform … AI will be seen as solving the workforce crisis, not creating it: As the baby boomer generation retires, enterprises are on the brink of losing significant institutional mindshare and knowledge. With the astronomical price tag of losing these workers, enterprises are turning to knowledge management and machine learning to train AI to capture institutional knowledge and act on our behalf. In the coming year and beyond, we will see AI adoption not only come from technological need, but also from the need to capture current employee insights and know-how. – Abdul Razack, SVP & Head of Platforms, Big Data and Analytics, Infosys

How Does AI Fit in an Enterprise? Whatever the industry, we can take better advantage of AI by making our current work tools — apps, medical devices, supply chain systems — much better through machine learning. The key is in the delivery — in other words, the “operationalization” of the analytics. I like to use the analogy of the self-driving car. The best autonomous vehicle systems will surely be able to handle the driving task in typical conditions; there are lots of little decisions to be made, but they are straightforward and easy to make. It’s when conditions become more challenging that the magic happens; the car will not only know when a human should intervene but also will smoothly transfer control to the driver and then back again to the machine. We’re on the cusp of where our everyday work apps and devices shift from repositories to assistants — and we need to start planning for it. Today, employees — or their boss — determine the next set of tasks to focus on. They log into an app, go through a checklist, generate a BI report, etc. In contrast, AI could automatically serve up 50% (or more) of what a specific employee needs to focus on that day, and deliver those tasks via a Slack app or Salesforce Chatter. Success will be found in making AI pervasive across apps and operations and in its ability to affect people’s work behavior to achieve larger business objectives. – Dan Udoutch, CEO, Alpine Data

Many Fortune 500 brands are already using chatbots, and many more are developing them as we speak. What’s ahead for the industry? Though it may not seem sexy, the next year will be a foundational one when it comes to applying AI. Chatbots are only as valuable as the relationships they build and the scenarios they can support, so their level of sophistication will make or break them. Investing in AI is only one piece of the puzzle, and 2017 will be the year that companies need to expand their AI initiatives while also doubling down on investing to improve them with new data streams and integration across channels. – Dave O’Flanagan, CEO, Boxever

The AI Hypecycle and Trough of Disillusionment, 2017: IDC predicts that by 2018, 75 percent of enterprise and ISV development will include cognitive/AI or machine learning functionality in at least one application. While dazzling POCs will continue to capture our imaginations, companies will quickly realize that AI is a lot harder than it appears at first blush and a more measured, long-term approach to AI is needed. AI is only as intelligent as the data behind it, and we are not yet at a point where enough organizations can harvest their data well enough to fulfill their AI dreams. – Ashley Stirrup, CMO, Talend

Hybrid Deep Learning systems. In 2017 we’ll see the rise of embedded analytics, optimized by cloud-based learning. The hybrid architectures used by autonomous vehicles – systems embedded within the vehicle to make numerous decisions per second, augmented by cloud-based learning platforms capable of optimizing decisions across the fleet – will serve as the foundation for the next generation of IoT machines. – Snehal Antani, CTO, Splunk

The focus will shift from “advanced analytics” to “advancing analytics.” Advanced analytics will continue to grow, and eventually be brought into self-service tools. With more users advancing their analytics, Artificial Intelligence (AI) might play a bigger role in organizations. But that means AI will also need to have high levels of usability as well, since users will need it to augment their analyses and business decisions. – Dan Sommer, Senior Director and Market Intelligence Lead, Qlik

Big Data

Many companies have ideas and initiatives around big data, but not a solid understanding of how it, along with the subsequent insights, will help them better the business or develop new solutions. Technology suddenly gave organizations the ability to process large amounts of data at a high frequency. That together with the move to mobile (as every consumer has one or more devices that they are constantly online with) drives a lot of data – whether through social networks, search engines or more. You have the information but it needs to be taken one step further – you need to analyze it. The question for big data is “what can I learn from it? Where can I make meaningful insights? – Dr. Werner Hopf, CEO and Archiving Principal, Dolphin Enterprise Solutions Corporation

Big data becomes fast and approachable. Sure, you can perform machine learning and conduct sentiment analysis on Hadoop, but the first question people often ask is: “How fast is the interactive SQL?” SQL, after all, is the conduit to business users who want to use Hadoop data for faster, more repeatable KPI dashboards as well as exploratory analysis. In 2017, options will expand to speed up Hadoop. This shift has already started, as evidenced by the adoption of faster databases like Exasol and MemSQL, Hadoop-based stores like Kudu, and technologies that enable faster queries. – Dan Kogan, director of product marketing at Tableau

Big Data, More Data, Fragmented Data – As we amass more enterprise data and blend third-party data, we create greater opportunity for insight and impact. However, let’s be honest. All companies are not created equal when it comes to their Big Data learning curves and sophistication. We will continue to see companies investing in, yet struggling with building their data layers.  Opera Solutions expects to see more attention and focused on data flow, data layers, and the emergence of the insights layer. – Georges Smine, VP Project Marketing, Opera Solutions

Moving into SMB – I see the advent of the big data analytics and discovery for SMB to start taking root in 2017. Big, rich, data environments such as pharma, healthcare, life sciences, financial services, insurance are the current industries leading big data analytics but graph-based databases can also be used by small companies, where you don’t want to spend your time coding and recoding every time you change your mind about what it is you want to look for. –  Chuck Pieper, CEO, Cambridge Semantics

Despite the hype and promise of big data and AI, few clear examples exist today where these technologies impact our lives on a daily basis. Serving relevant ads to website visitors and detecting fraud in credit card transactions come to mind. These companies have invested in big data and machine learning for years, which has allowed them to develop solid data architectures. Companies that have lived with NoSQL databases for more than a year know that ignoring data model design and instead leaning too heavily on the flexible, schema-free capabilities of these databases leads to poorly performing applications, difficult maintainability, and ultimately rework. In 2017, I predict the discipline of data modeling will gain strength as a sought-after skill set and project activity, particularly for companies dedicated to building impactful data strategies. Tools, such as well-designed industry clouds provide the professional data model design necessary for long-term success.” – J.J. Jakubik, Chief Architect, Vlocity

The sheer volume of data generated by applications and infrastructure will only increase, resulting in data overload. For the first time, IT Operations teams will embrace an algorithmic approach – also known as Algorithmic IT Operations, or AIOps – to detect signal from noise to ensure successful service delivery. AIOps platforms will provide IT Operations teams with situational awareness and diagnostic capabilities that were not previously possible using manual, non-algorithmic techniques.” – Michael Butt, Senior Product Marketing Manager at BigPanda

We’re living in a big data glut. But in 2017, we’ll see data become more intelligent, more useable, and more relevant than ever. The cloud has opened the doors to more affordable, smart data solutions that make it possible for non-technical users to explore, through visualization tools, the power of predictive analytics. We’re also seeing the increasing democratization of artificial intelligence which is driving more sophisticated consumer insights and decision-making. Forward-thinking organizations need to approach predictive analytics with the future and extensibility in mind. Today’s tools may not be the best for tomorrow’s needs. Cloud solutions are still evolving and haven’t reached functionality maturity yet, but by merging cloud, open source, and agile development methodologies into their predictive analytics stack, organizations will be able to easily adopt as technology advances.  – Slava Koltovich, CEO, EastBanc Technologies

One Team, One Platform – Data is the common thread within the enterprise, regardless of where the source might be. In the past data handlers have relied on disparate systems for data needs. Next year, the goal will be to move data into the future by providing a one-stop shop to access, develop and explore data. Companies will now look to one data platform for integrated cloud services with easy access and consistent behavior that is equipped to satisfy the needs of diverse data-hungry professionals across the organization. Just as you can easily access a variety of apps on your smartphone, business users and data professionals will look to deploy one platform that allows their organization to tap into the rich capabilities of data. – Derek Schoettle, General Manager, Cloud Data Services, IBM Watson and Cloud Platform

Next year will bring about another deluge of data brought on by advancements in the way we capture it. As more hardware and software is instrumented especially for this purpose, such as IoT devices, it will become easier and cheaper to capture data. Organizations will continue to feed on the increased data volume while the big data industry struggles through a shortage of data scientists and the boundaries imposed by non-scalable legacy software that can’t perform analytics at a granular level on big data data. Healthcare will especially be hard hit in this regard. Sources of huge healthcare data sets are becoming more abundant, ranging from macro-level sources like surveys by the World Health Organization, to micro-level sources like next-generation Genomics technologies. Healthcare professionals are leveraging these data to improve the quality and speed of their services. Even traditional technology companies are venturing into this field. For example, Google is ploughing money into its healthcare initiatives like Calico, its “life-expansion” project, and Verily, which is aimed at disease prevention. We expect the demand for innovative technical solutions in all industries, particularly healthcare to explode in popularity next year. – Michael Upchurch, COO, Fuzzy Logix

Data lakes will finally become useful — Many companies who took the data lake plunge in the early days have spent a significant amount of money not only buying into the promise of low cost storage and process, but a plethora of services in order to aggregate and make available significant pools of big data to be correlated and uncovered for better insights. The challenge has been finding skilled data scientists that are able to make sense of the information, while also guaranteeing the reliability of data upon which data is being aligned and correlated to (although noted expert Tom Davenport recently claimed it’s a myth that data scientists are hard to find). Data lakes have also fallen short in providing input into and receiving real-time updates from operational applications. Fortunately, the gap is narrowing between what has traditionally been the discipline and set of technologies known as master data management (MDM), and the world of operational applications, analytical data warehouses and data lakes. With existing big data projects recognizing the need for a reliable data foundation, and new projects being combined into a holistic data management strategy, data lakes may finally fulfill their promise in 2017. – Ramon Chen, CMO, Reltio

I believe customers will choose solutions in Big Data that deliver faster time to value, simple deployment with ease of management, interoperability with open source tools and solutions that help bridge the skills gap. I predict that Big Data technologies like Hadoop will be adopted at an accelerated rate because customers must get smarter about data. Based on customer conversations, they understand they could be disrupted by a new competitor with a data driven business model. Hadoop will be at the core of a data driven business allowing organizations to be more agile, know more about their customers, and offer new services ahead of the competition. I believe the strength of the community, the work of Cloudera and Hortonworks along with maturing ecosystem tools, as well as interoperability with analytical tools, will provide a secure, enterprise ready data platform. – Armando Acosta, Hadoop Product Manager and Data Analytics SME, Dell EMC

Open source and faux-pen source data technology choices will continue to proliferate, but the new model will redistribute rather than purely reduce costs for enterprises. Vendors are walking away from traditional database and data warehouse business models. Prime examples of this are Pivotal open sourcing Greenplum, Hewlett Packard Enterprise (HPE) spinning off Vertica and other assets, and Actian stopping support for Matrix (formerly ParAccel). Open source projects – or in many cases, vendor sponsored faux-pen sources – are becoming the new model for data processing technology. But while open source reduces the costs of vendor licensing, it also shifts responsibility to the enterprise to sort through the options, assemble stacks and productionize open source projects. This increase in complexity and consumption challenges requires new hiring and/or partnering with as-a-Service cloud vendors. – Prat Moghe, Founder and CEO, Cazena

In 2017 organizations will shift from the “build it and they will come” data lake approach to a business-driven data approach. Use case orientation drives the combination of analytics and operations. Approaching a data lake as “Imagine what your business could do if all your data were collected in one centralized, secure, fully-governed place that any department can access anytime, anywhere.” could sound attractive at a high level, but too frequently results in a data swamp that looks like a data warehouse rebuild and can’t address real-time and operational use case requirements. Once in place the concept is to “ask questions”. In reality, the world moves faster today. Today’s world requires analytics and operational capabilities to address customers, process claims and interface to devices in real time at an individual level. For example any ecommerce site must provide individualized recommendations and price checks in real time. Healthcare organizations must process valid claims and block fraudulent claims by combining analytics with operational systems. Media companies are now personalizing content served though set top boxes. Auto manufacturers and ride sharing companies are interoperating at scale with cars and the drivers. Delivering these use cases requires an agile platform that can provide both analytical and operational processing to increase value from additional use cases that span from back office analytics to front office operations. In 2017, organizations will push aggressively beyond an “asking questions” approach and architect to drive initial and long term business value. – John Schroeder, Chairman and Founder, MapR

Big data goes self-service. Organizations that have realized the value of big data now face a new problem: IT and data teams are being flooded with requests from users to pull data. To address this, we’ll see more organizations opt for a self-service data model so that anyone in the company can easily pull data to uncover new insights to make business decisions. A self-service infrastructure allows any employee to easily access and analyze data, saving IT and data teams precious time and resources. To make this a reality, all types of data in every department will need to be published so that users can self-serve. – Ashish Thusoo, CEO, Qubole

2017 will be the year organizations begin to rekindle trust in their data lakes. The “dump it in the data lake” mentality compromises analysis and sows distrust in the data. With so many new and evolving data sources like sensors and connected devices, organizations must be vigilant about the integrity of their data and expect and plan for regular, unanticipated changes to the format of their incoming data. Next year, organizations will begin to change their mindset and look for ways to constantly monitor and sanitize data as it arrives, before it reaches its destination. – Girish Pancha, CEO and Founder, StreamSets

Companies have been collecting data for awhile, so the data lake is well-stocked with fish. But the people who needed data most couldn’t generally find the right fish. I support the notion of a data lake, dumping all your raw data into one data warehouse. But it doesn’t work if you don’t have a way to make it cohesive when you query it. There have been great innovations by companies like Segment, Fivetran and Stitch, which make moving data into the lake easier. Modeling data is the final step that brings it all together and helps some of the best companies in the world see through data.
Companies like Docker, Amazon Prime Now and BuzzFeed are using all their data to create comprehensive views of their customers and of their businesses. When these final two steps are added, the data lake can finally be a powerful way to get all your data into the hands of every decision-maker to make companies more successful. – Lloyd Tabb, Founder, Chairman & CTO, Looker

In 2017, organizations will stop letting data lakes be their proverbial ball and chain. Centralized data stores still have a place in initiatives of the future: How else can you compare current data with historical data to identify trends and patterns? Yet, relying solely on a centralized data strategy will ensure data weighs you down. Rather than a data lake-focused approach, organizations will begin to shift the bulk of their investments to implementing solutions that enable data to be utilized where it’s generated and where business process occur – at the edge. In years to come, this shift will be understood as especially prescient, now that edge analytics and distributed strategies are becoming increasingly important parts of deriving value from data. – Adam Wray, CEO, Basho Technologies

In 2017, the reports of Big Data’s death will be greatly exaggerated, as will the hype around IoT and AI. In reality, all of these disciplines focus on data capture, curation, analysis and modeling. The importance of that suite of activities won’t go away unless all businesses cease operation. – Andrew Brust, Senior Director, Market Strategy and Intelligence, Datameer

Big data or bust in 2017? Big data is an example of something that didn’t get as far along as people predicted. Of course, it wasn’t stagnant. But nearly everyone involved in the enterprise sector would like it to move faster. The problem is that companies struggle, in general, to make sense of big data because of its sheer volume, the speed in which it is collected and the great variety of content it encompasses. Looking ahead, we can expect to see newer tools and procedures that will help companies house and examine these massive amounts of data and help them move toward truly making data-driven decisions. – Bob DeSantis, COO, Conga

In the new world of data, DBMS is really the management of a collection of data systems. This deserves a new thinking or approach to how we manage these systems and the applications that leverage them. The enterprise has long relied on raw logs and systems monitoring solutions to optimize their Big Data applications—and as companies continue to adopt numerous disparate Big Data technologies to help meet their business needs, complexity is only increasing while the time required to diagnose and resolve issues grows exponentially, all of which is underlined by an acute shortage of talent capable of effectively running and maintaining these intricate Big Data systems. The primary challenge faced by the enterprise is finding a single full-stack platform capable of analyzing, optimizing and resolving any issues that exists with Big Data applications and the infrastructure supporting them. In the year ahead, the enterprise will search for a solution that addresses the unmet challenges of data teams that find themselves spending much of their day digging through machine logs in order to identify the root cause of problems on a Big Data stack. These problems, if not eradicated, will continue to reduce application performance and divert teams from their real mission of deriving the full value from their Big Data. Ideal solutions will be ones that resolve problems automatically, detecting and pinpointing performance and reliability issues with Big Data applications running on clusters; solutions that open up the doors to data equality across the enterprise, that with just the click of a button, drastically accelerate the time-to-value of Big Data investments. – Unravel Data

Big data wanes – Big data will continue to wane as a term. The focus now turns from infrastructure to applications with specific purposes. Companies will look to applications and new business models for concrete value, rather than the more general idea that data can be useful at scale. – Satyen Sangani, CEO, Alation

Business Intelligence

Self-service extends to data prep. While self-service data discovery has become the standard, data prep has remained in the realm of IT and data experts. This will change in 2017. Common data-prep tasks like data parsing, JSON and HTML imports, and data wrangling will no longer be delegated to specialists. With new innovations in this transforming space, everyone will be able to tackle these tasks as part of their analytics flow. – Francois Ajenstat, Chief Product Officer at Tableau

Many Big Data systems are lacking simple UI’s for data input and classification. This usually requires highly technical staff and costs for the configuration, ongoing use, and for the interpretation of Big Data. This produces a high cost of entry and ongoing expenses. To add insult to injury, even once deployed, if the tool cannot be completely adopted by all necessary end users due to complexity, all BI efforts may be for naught. Successful User Interfaces (UI’s) are simple and flexible and modify to the needs of a variety of users and any changes to fluid data sets. This is the future of Big Data. Making Big Data even more accessible accurate, and therefore indispensable. Just as other technologies have evolved, BI is evolving to be more accessible than ever to today’s business. This will only continue in the future. – Dave Bethers, Chief Operations Officer, TCN

Digital transformation will be a CIO imperative for greater than 50% of all institutions. As such, IT will no longer be pushing Big Data technologies to the business owners. Instead, IT will need to respond to the demands for faster and more predicative analytics. Data scientists will be embedded into the business units in larger companies and in the smaller firms, everyone will be considered a citizen data scientist. Regardless, business intelligence will no longer be considered a department but an attitude. A way of life. At least for those who plan to be in business by 2019. – Anthony Dina, Director Data Analytics, Dell EMC

In 2017, business people will become ‘data mixologists’, capable of blending data from any combination of systems – centralized and decentralized – to produce new insights on their own, share them with others, and make better, more trusted business decisions. Historically, mixing together data from spreadsheets, databases, or applications like Marketo, Salesforce and Google Analytics has been an inaccessible capability for business people, as well as a data governance nightmare. Until now, self-service data prep tools have been designed for data scientists who work in silos of disconnected data – a phenomenon known as “data discovery sprawl”. These silos produce inaccurate and unreliable insights, and they don’t put those insights in the hands of business decision-makers. In the coming year, we will see business users choose modern tools that help them become data mixologists, making empowered decisions from trustworthy data sets. – Pedro Arellano, VP of Product Strategy, Birst

Cloud

The move to serverless architectures will become more widespread in the coming years, and will impact how applications are deployed and managed. Serverless architectures allow users to deploy code and run applications without managing the supporting infrastructure. Instead, the supporting infrastructure is managed by a third party. AWS’ cloud service Amazon Lambda is an example, and we anticipate growth in the number of providers and the breadth of enterprise-ready applications. As use of serverless architectures begin to rise, the overall application development and deployment strategy will begin to shift away from operations and more towards business logic. More cloud providers will also begin migrating to this form of architecture, allowing for a more competitive market with more expansive application support. As such, it will be important for database solution providers to be ‘cloud-ready.’ – Patrick McFadin, Chief Evangelist for Apache Cassandra, DataStax

The conversation around vendor lock-in is becoming much more prominent in senior level meetings, spurred on by many enterprises’ decision to move to the public cloud. To this point, the issue of vendor lock-in was initially discussed as a black or white situation. However, in 2017 we are going to see this conversation shift to acknowledge the many shades of gray, as executives realize and consider the varying degrees of lock-in and how it impacts various departments and levels of management. Examining the potential consequences of using proprietary technology on the different levels of the hardware and software stack will be an important issue within companies this year as more enterprises implement digital transformation initiatives. – Bob Wiederhold, CEO, Couchbase

Big data and the cloud will go hand-in-hand. Five years ago concerns over security and compliance kept enterprises from embracing big data in the cloud. Now, best practices and advancements in technology have allayed those concerns while the cloud’s agility and ease of use are becoming must-have’s for processing big data. As big data moves from an experiment to an organization-wide endeavor, the cost, time and resources needed to manage a massive data center don’t make sense. As a result, more and more companies will look to the cloud to help with the costs of data management. In 2017, expect enterprises to move their big data projects to the cloud in droves. – Ashish Thusoo, CEO, Qubole

2017 will be the year big data platforms go operational with the rise of hybrid clouds. We will see more customer cloud apps, such as Salesforce CRM and Oracle CX, accessing big data insights directly from on-premises big data platforms, which are the foundations of enterprises’ digital transformation and omni-channel marketing strategies. Examples of big data insights that support additional functional areas, such as sales and marketing, include predictive models, lead scoring or personalization. This typically starts with the ingestion of customer and marketing data into a data lake, where the source data is commonly stored in hybrid cloud and on-premises systems. And to operationalize those insights, we’ll see greater demand for standard REST interfaces to big data sets primarily accessible from SQL (such as Hive, Impala or Hawq) for hybrid connectivity from SaaS applications or cloud and mobile application development. For on-premises consumers of hybrid data, we expect hosted big data platforms such as IBM BigInsights on Cloud, Amazon EMR, Azure HDInsights or SAP Altiscale to run more big data workloads, not suitable for local data centers, in the cloud and sending only the insights to on-premises systems for core business operations. – Sumit Sarkar’s, Chief Data Evangelist, Progress

Big-Data-as-a-Service. Big Data continued to see rising adoption throughout 2016, and we’ve observed an increasing number of organizations that are transitioning from experimental projects to large-scale deployments in production. However, the complexity and cost associated with traditional Big Data infrastructure has also prevented a number of enterprises from moving forward. Until recently, most enterprise Hadoop deployments were implemented the traditional way: on bare-metal physical servers with direct attached storage. Big-Data-as-a-Service (BDaaS) has emerged as a simpler and more cost-effective option for deploying Hadoop as well as Spark, Kafka, Cassandra, and other Big Data frameworks. As the public cloud becomes a more common deployment model for Big Data, we anticipate many of these deployments shifting to BDaaS offerings in 2017. In addition to solutions offered by newer BDaaS vendors like BlueData and Qubole, we’ll see more initiatives from established public cloud players like AWS, Google, IBM, and Microsoft. We can also expect a range of other announcements that will further validate the trend toward BDaaS, including both major partnerships (such as VMware’s recent embrace of AWS) and acquisitions (SAP buying Altiscale). As the ecosystem expands, customers will have the flexibility to choose from a range of BDaaS solutions, including public cloud as well as on-premises and even hybrid options (e.g. compute in the cloud and data stored on-premises). – BlueData

Data Governance

The Chief Data Officer Moves to New Heights – In this past year, we’ve seen the Chief Data Officer emerge as an instrumental part of the organization’s plan to harness the full value of data for competitive advantage. In 2017 we will see this role evolve further with the acceleration of CDO hires across industries to help with competitive pressures, aggressive global regulations (things like GDPR and BCBS 239) and the general increasing speed of business. Gartner predicts that by 2019, 90% of large organizations will have a CDO. We see this happening much quicker with the CDO rising as data hero within the organization when faced with the new challenges of managing the big data overload dispersed in separate systems and data silos among specific groups and users enterprise-wide. Wearing a super cape, CDOs will figure out a way to break down the data unrest that likely exists today by implementing business-focused governance processes and platforms and enabling and empowering every user across the enterprise to use and capitalize on data for competitive advantage. – Stan Christiaens, co-founder and CTO of data governance leader Collibra

In 2017, the governance vs. data value tug of war will be front and center. Enterprises have a wealth of information about their customers and partners. Leaders are transforming their companies from industry sector leaders to data driven companies. Organizations are now facing an escalating tug of war between governance required for compliance, and the use of data to provide business value and implement security to avoid damaging data leaks and breeches. Financial services and heath care are the most obvious industries with customers counting in the millions with heavy governance requirements. Leading organizations will manage their data between regulated and non-regulated use cases. Regulated use cases data require governance; data quality and lineage so a regulatory body can report and track data through all transformations to originating source. This is mandatory and necessary but limiting for non-regulatory use cases like customer 360 or offer serving where higher cardinality, real-time and a mix of structured and unstructured yields more effective results. – John Schroeder, Chairman and Founder, MapR

Moore’s Law holds true for databases. Per Moore’s law, CPUs are always getting faster and cheaper. Of late, databases have been following the same pattern. In 2013, Amazon changed the game when they introduced Redshift, a massively parallel processing database that allowed companies to store and analyze all their data for a reasonable price. Since then however, companies who saw products like Redshift as datastores with effectively limitless capacity have hit a wall. They have hundreds of terabytes or even petabytes of data and are stuck between paying more for the speed they had become accustomed to, or waiting five minutes for a query to return. Enter (or reenter) Moore’s law. Redshift has become the industry standard for cloud MPP databases, and we don’t see that changing anytime soon. With that said, our prediction for 2017 is that on-demand MPP databases like Google BigQuery and Snowflake will see a huge uptick in popularity. On-demand databases charge pennies for storage, allowing companies to store data without worrying about cost. When users want to run queries or pull data, it spins up the hardware it needs and gets the job done in seconds. They’re fast, scalable, and we expect to see a lot of companies using them in 2017. – Lloyd Tabb, Founder, Chairman & CTO, Looker

The rise of “applied governance” to unstructured data. Earlier this year, more than 20,000 pages of top-secret Indian Navy data, including schematics on the their Scorpene-class submarines, were leaked. It’s been a huge setback for the Indian government. It’s also an unfortunate case study for what happens when you lack controls over unstructured information, such as blueprints that might be sitting in some legacy engineering software system. Now, replace the Indian Navy scenario with a situation involving the schematics for a Nuclear power plant or consumer IoT device, and the value of secure content curation becomes even more immeasurable. If unstructured blueprints and files are being physically printed or copied, or digitally transferred, how will you even know that content now exists? Tracking this ‘dark data’ – particularly in industrial environments – will be a top security priority in 2017. – Ankur Laroia, Leader – Solutions Strategy, Alfresco

Organizations have viewed data governance as a tax. It’s something you had to do for compliance or regulatory reasons, but it wasn’t adding value to the business. In reality, governance is crucial to driving business value. Think about the enormous amount of time and money being spent these days to harness the value of data – the whole Big Data movement. Organizations know there is tremendous value to be had, but many of them aren’t actually getting the value despite their investment. Gartner says: Through 2018, 80% of data lakes will not include effective metadata management capabilities, making them inefficient. Why? Two reasons: First, they don’t have the lineage and provenance of the data they’re analyzing. When they put bad or misleading data into their analysis, they’re going to get unreliable results back out. That’s a lack of data governance. Second, and perhaps even worse, organizations are afraid to share the data they’ve gone to great expense to create. They can’t answer questions such as: Under what agreements was the data collected? Which pieces are personal information? Who’s allowed to see it? In which geographies? With what redistribution rights? If you can’t answer these questions, you can’t share the data. Your data lake is fenced off. This is another failure of governance. Businesses will realize that governance gives them the highest quality results, that can be shared with the right audiences, and drive the greatest business value. – Joe Pasqua – EVP Products, MarkLogic

The Chief Data Officer position will pick up steam significantly. This is a sure sign of the pendulum swinging back: A company officer centrally managing the value of data. And a CDO’s job isn’t to empower analysts per se, although that will often be part of what they do. If that were all it was, companies could save a lot of money by handing out tools and not creating the CDO position. The CDO’s job is to extract maximum value from data. That can be done in many ways, including customer-facing portals, large-scale analytical apps, data feeds that stem from unified views of business entities, embedded BI inside other enterprise applications, and so on.So as the CDO position picks up steam, we can expect to see larger data-focused projects where information is managed and shared across divisional and even company boundaries, leading to better data monetization, lower per-user cost of data, and higher business value per unit of data. – Jake Freivald, Vice President, Information Builders

Data Science

In 2017 we will see an increased valuation of the critical thinking in the workplace, as people realize that there is not a deficit of data in the enterprise, but a deficit of insight. Companies will realize that data without additional tenets of knowledge or value, is both polarizing and damaging. The role of data scientist will evolve to become “the knowledge engineer.” We will see fewer “alchemists” – promising magic from data patterns alone, and more “chemists” — combining the elements of knowledge, data, context, and insight to deliver productivity enhancements that we have yet to imagine. – Donal Daly, CEO, Altify

We spend a lot of time thinking about what developers want & need in a tool, both right now and in the future. In software development, complexity is inevitable – tech stack, libraries, formats, protocols – and that complexity won’t be decreasing any time soon. The most successful tool is one that is simple, but not dumbed down or less powerful. I believe that tools will need to become even more powerful in 2017, and the successful tools will be ones that work for the developer rather than the other way around. Tools will need to be smarter to learn from the user automatically, proactive to inform the user automatically, collaborative to connect users with others, and visual and tangible to show and manipulate. This meta-increase in toolsets is possible now for a number of reasons. Memory, processing power, and connectivity speed continue to explode, while at the same time visual tools (like 4K screens) get better and better. Plus, the continued rise of social coding increases the need to powerful collaborative tools to support the developer. – Abhinav Asthana, CEO of Postman

2017 will be the “Year of the Data Scientist.” According to the McKinsey Global Institute, demand for data scientists is growing by as much as 12 percent a year and the US economy could be short by as many as 250,000 data scientists by 2024. Thanks to advances driven by AI companies in 2017, however, 2018 is when AI will become buildable – not just usable – but buildable by non-data scientists. This is not to say that data science will become less useful or in-demand post-2017, rather that some of the simpler problems will be solvable through a hyper-personalized AI built by someone who is not a data scientist. This will open up capabilities for coders and data scientists that will be mind-blowing. – Jeff Catlin, CEO, Lexalytics

SQL will have another extraordinary year. SQL has been around for decades, but from the late-1990s to mid 2000s, it went out of style as people started exploring NoSQL and Hadoop alternatives. SQL however, has come back with a vengeance. The renaissance of SQL has been beautiful to behold and I don’t even think it’s near it’s peak yet. The innovations we’re seeing are blowing our minds. BigQuery has created a product that is essentially infinitely scalable, the original goal of Hadoop, AND practical for analytics, the original goal of relational databases. Additionally, Google recently announced that the new version, BigQuery Standard SQL is fully ANSI compliant. Prior to this release, BigQuery’s Legacy SQL was peculiar and so presented a steep learning curve. BigQuery’s implementation of Standard SQL is amazing, with really advanced features like Arrays, Structures, and user-defined functions that can be written in both SQL and Javascript. SQL engines for Hadoop have continued to gain traction. Products like SparkSQL and Presto are popping up in enterprises and as cloud services because they allow companies to leverage their existing Hadoop clusters and cloud storage for speedy analytics. What’s not to love? To top it all off, companies like Snowflake, and now Amazon Athena, are building giant SQL data engines that query directly on S3 buckets, a source that was previously only accessible via command line. 2016 was the best year SQL has ever had — 2017 will be even better. – Lloyd Tabb, Founder, Chairman & CTO, Looker

The data skills gap widens. Problem: The demand for data scientists and data engineers continues to challenge enterprises who need to make the most of their data. And even when there are the right skillsets at play, the New York Times recently reported that these critical personnel are often spending more time cleaning the data than actually mining it. Prediction: Businesses will seek any tool that help to put more data in the hands of business analysts with the minimum data scientist intervention. In addition, new machine learning tools will emerge to help automate some of these data-focused tasks to scale the models that data scientists create. – SnapLogic

There will continue to be a shortage of qualified data scientists. I don’t expect the market to be in equilibrium until 2019 at the earliest. Every major university will have a data science program in place by 2017. – Michael Stonebraker, Ph.D., co-founder and CTO, Tamr

Data Scientists failed to predict the election—will they fail to predict your business? The other day I was giving a talk on ‘What is Machine Learning?’ and, barely two minutes in, someone said, ‘You’re saying we can do all these amazing things with big data and algorithms, but you had all the data in the world for the election, and you got it wrong. Why should we trust you?’ There are plenty of important takeaways from the election: First, Nate Silver and enterprise data scientists both try to learn from historical events to predict future events, and the margins of error can behigh in both. But in predicting an election you only get one chance. In business, you make predictions constantly, and the cost of error tends to be low. Also, there are fewer curve-balls in business. Customers and businesses tend to be pretty predictable. Voters and politicians are not. Second, the media committed the same sin we see business people make every day: falling too hard for the analytic ‘black box’ that does seemingly magical number crunching. Without a basic understanding of what types of analyses have been done on different types of data and why, the end users will never know the true value of the information they have at their disposal or how they should use it. There’s no better illustration of this than the little needle on The New York Times’ election ‘dial’ which bounced violently from Clinton to Trump in the middle of the evening and had me screaming at my phone. – Steven Hillion, Chief Product Officer, Alpine Data

GPUs and HPC

2017 will be the year when “accelerated compute” becomes known just simply as “compute”. This is a direct response to the use cases driving up utilization the most, and the explosion of accelerator availability in both the data center and the public cloud. As these use cases continue to ramp up in the Enterprise (particularly machine learning), we’ll see even more demand for computational accelerators. CPUs have been king for decades, and serve the general purpose quite well. But what we’re seeing now is an emphasis on deriving insight from data, versus just indexing it, and this requires orders of magnitude faster (and more specialized) resource in order to deliver feasible economics. It’s not that computational accelerators are necessarily “faster” than CPUs, but rather, they can be deployed as coprocessors and therefore take on very specialized identities. Because of this specialization, they can be programmed to do certain very discrete computations much quicker and at lower aggregate power consumption. Application developers and ISVs are pouncing on these capabilities (and their increasing availability) to create amazing new products and services. A good example of a red-hot technology in this space are GPU-accelerated databases, such as GPUdb from Kinetica (available as a turnkey workflow on the Nimbix Cloud). Rather than focusing on indexing massive amounts of information like a traditional RDBMS, it’s used to ingest fragments into memory for tremendously fast queries. In fact the queries are so fast that it blurs the line between analytics and machine learning (after all, machine learning involves processing massive data sets very quickly in order to create “models” that operate somewhat like human brains). Despite the advanced computing underneath, these tools serve traditional enterprise markets, not just “research labs”. Not only does its product name imply it, but the use case simply would be impossible without GPUs. This is a very real example of mainstream technology that demands computational accelerators. In talking with customers and business partners, the one common thread they all seek is more accelerated computational power (at reasonable economics) to do even more advanced things. I don’t see this trend slowing down anytime soon, which is why I’m predicting that we’ll drop the “accelerated” in front of “compute” as it will become a given. – Leo Reiter, CTO, Nimbix

Graphical Processing Units (GPUs) are capable of delivering up to 100-times better performance than even the most advanced in-memory databases that use CPUs alone. The reason is their massively parallel processing, with some GPUs containing over 4,000 cores, compared to the 16-32 cores typical in today’s most powerful CPUs. The small, efficient cores are also better suited to performing similar, repeated instructions in parallel, making GPUs ideal for accelerating the compute-intensive workloads required for analyzing large streaming data sets in real-time. – Eric Mizell, Vice President, Global Solutions Engineering, Kinetica

Amazon has already begun deploying GPUs, and Microsoft and Google have announced plans. These cloud service providers are all deploying GPUs for the same reason: to gain a competitive advantage. Given the dramatic improvements in performance offered by GPUs, other cloud service providers can also be expected to begin deploying GPUs in 2017. – Eric Mizell, Vice President, Global Solutions Engineering, Kinetica

Hadoop

As I predicted last year, 2016 was not a good year for Hadoop and specifically for Hadoop distribution vendors. Hortonworks is trading at one-third its IPO price and the open source projects are wandering off. IaaS cloud vendors are offering their own implementations of the open source compute engines – Hive, Presto, Impala and Spark. HDFS is legacy in the cloud and is rapidly being replaced by blob storage such as S3. Hadoop demonstrates the perils of being an open source vendor in a cloud-centric world. IaaS vendors incorporate the open source technology and leave the open source service vendor high and dry. Open source data analysis remains a complicated and confusing world. Wouldn’t it be nice if there were one database that could do it all? Wait, there is one, it’s called Snowflake. – Bob Muglia, CEO, Snowflake Computing Inc.

Don’t be a Ha-dope! For all those folks running around saying Hadoop is dead – they’re dead wrong. In 2017, we’re going to see an increased adoption of Hadoop. So far this year, I haven’t talked to a single organization with a digital data platform who doesn’t see Hadoop at the center of their infrastructure. Hadoop is an assumed part of every modern data architecture and nobody can question the value it brings with its flexibility of data ingestion and its scalable computational power. Hadoop is not going to replace other databases but it will be an essential part of data ingestion in the IoT/digital world. – George Corugedo, CTO, RedPoint Global

Hadoop distribution vendors will have crossed the chasm — unstructured data in Hadoop is a reality. But, since the open source problem has not been addressed, they aren’t making much money. As such, there will be an acquisition of many of these vendors by bigger players. As well as the idea that bigger ISV Hadoop vendors will band together and create larger entities in hopes of capitalizing on the economy of scale. – Joanna Schloss, Director of Product Marketing, Datameer

The Failure (and future) of Hadoop. Problem: Fifty percent of Hadoop deployments have failed. While it’s commanded the lion’s-share of attention, it’s suffered from product overload. Because new projects are added every month and the nature of the data in the Hadoop cluster is ever-growing, it’s created a complex, multidimensional environment that’s difficult to maintain in production. Prediction: To actually make Hadoop work beyond a test environment, enterprises will shift it to the cloud in 2017, and abstract storage from compute. This enables enterprises to select the tools they want to use (Spark, Flink or others) instead of being forced to carry excessive Hadoop baggage with them. – SnapLogic

In-Memory Computing

In 2017, in-memory computing will enter the mainstream as the enabling technology for adding operational intelligence to live systems, and it will supplant legacy streaming technologies. In 2017, the adoption of in-memory computing technologies, such as in-memory data grids (IMDGs), will provide the enabling technology to capture perishable opportunities and make mission-critical decisions on live data. Driven by the need for real-time analytics, the IMDG market alone – currently estimated at $600 million – will exceed $1 billion by 2018, according to Gartner. Unlike big data technologies, such as Spark, created for the data warehouse and legacy streaming technologies, in-memory computing enables the straightforward modeling and tracking of a live system by analyzing and correlating persistent data with live fast-changing data in real time, and it provides immediate feedback to that system for automated decision making. Gartner has recently elevated the term “digital twin” in its recent Top 10 strategic technology trends for 2017 to describe the shift in focus from data streams to the data sources which produce those streams. In-memory computing technology enables applications to easily create and manage digital representations of real-world devices, such as Industrial Internet of Things (IIoT) sensors and actuators, and this enables real-time introspection for operational intelligence. – Dr. William Bain, CEO and founder, ScaleOut Software

In-Memory and Temporary Storage become more important as new sources of data growth such as augmented and virtual reality, AI and machine learning become popular: While analyzing these new sources of data is becoming critical to long-term business goals, storing the data long term is both impractical and unnecessary when the results of analysis are more important than the data itself. Although 2017 will see plenty of data growth that will require permanent storage, most net new data generated next year will be ephemeral; it will quickly outlive its usefulness and be discarded. So despite exponential data growth, there won’t be as much storage growth as we might otherwise have expected. – Avinash Lakshman, CEO, Hedvig

IoT

The future of IoT will be focused on security. Recently, a major DDoS attack caused outages at major organizations. This is going to be a growing issue in the near future, and the concern at the forefront of IoT will be safeguarding networks and connected devices. – Dr. Werner Hopf, CEO and Archiving Principal, Dolphin Enterprise Solutions Corporation

IOT grows up – The enterprise has paid attention to IOT for some time, though this year will be the year we move past the “wow” phase and into the “how do we do we securely and effectively bring IOT to the enterprise, how do we handle the high speed data ingest, and how do we optimize analytics and decisions based on IOT data.” Those will be the questions enterprises will need to solve in 2017. – Leena Joshi, VP of Product Marketing, Redis Labs

IoT continues to pose a major threat. In late 2016, all eyes were on IoT-borne attacks. Threat actors were using Internet of Things devices to build botnets to launch massive distrubted denial of service (DDoS) attacks. In two instances, these botnets collected unsecured “smart” cameras. As IoT devices proliferate, and everything has a Web connection — refrigerators, medical devices, cameras, cars, tires, you name it — this problem will continue to grow unless proper precautions like two-factor authentication, strong password protection and others are taken. Device manufactures must also change behavior. They must scrap default passwords and either assign unique credentials to each device or apply modern password configuration techinques for the end user during setup. – A10 Networks

The Internet of Things (IoT) is widely acknowledged as a big growth area for 2017. More connected devices will create more data, which has to be securely shared, stored, managed and analyzed. As a result, databases will become more complex and the management burden will increase. Those organizations which can most effectively monitor their database layer to optimize peak performance and resolve bottlenecks will be more strongly placed in a better position to exploit the opportunities the IoT will bring. – Mike Kelly, CTO, Blue Medora

The future of retirement is gearing up for a major shift and Internet of Things (IoT) along with it. Baby boomers are retiring, and there are many economic and lifestyle reasons for them to live in their homes longer. This means changes for insurance companies, healthcare, medical devices, and appliance manufacturers. The proliferation of the IoT or “the connected life” allows for monitoring the elderly in their homes, from monitoring blood pressure to typical daily habits such as whether or not they turned on the TV or opened the refrigerator. Elderly parents want autonomy and their children want them to be safe – connected technology can bridge the gap between the two. Basic monitoring as well as more advanced medical monitoring is shifting the way we will live out our retirement. – Kevin Petrie, Attunity

The Internet of Things (IoT) is still a popular buzzword, but adoption will continue to be slow. Analyzing data from IoT and sensors clearly has the potential for massive impact, but most companies are far (FAR!) from ready. IoT will continue to get lots of lip service, but actual deployments will remain low. Complexity will continue to plague early adopters that find it a major challenge to integrate that many moving parts. Companies will instead focus resources on other low-hanging fruit data and analytics projects first. – Prat Moghe, Founder and CEO, Cazena

The Internet of Things is delivering on the promise of big data. IoT will deliver on the promise of big data. Increasingly, big data projects are going through multiple updates in a single year – and the Internet of Things (IoT) is largely the reason. That’s because IoT makes it possible to examine specific patterns that deliver specific business outcomes, and this has to increasingly be done in realtime. This will drive a healthier investment, and faster return in big data projects. – Ettienne Reinecke, Chief Technology Officer, Dimension Data

Next year, organizations will stop putting IoT data on a pedestal, or, if you like, in a silo. IoT data needs to be correlated with other data streams, tied to historical or master data or run through artificial intelligence algorithms in order to provide business-driving value. Despite the heralded arrival of shiny new tools that can handle IoT’s massive, moving workloads, organizations will realize they need to integrate these new data streams into their existing data management and governance disciplines to gain operational leverage and ensure application trust. – Girish Pancha, CEO and Founder, StreamSets

The Internet of Things Architect role will eclipse the data scientist as the most valuable unicorn for HR departments. The surge in IoT will produce a surge in edge computing and IoT operational design. 1000s of resumes will be updated overnight. Additionally, fewer than 10% of companies realize they need an IoT Analytics Architect, a distinct species from IoT System Architect. Software architects who can design both distributed and central analytics for IoT will soar in value. – Dan Graham, Internet of Things Technical Marketing Specialist, Teradata

At Least one Major Manufacturing Company will go belly up by not utilizing IoT/big data: The average lifespan of an S&P 500 company has dramatically decreased over the last century, from 67 years in the 1920s to just 15 years today. The average lifespan will continue to decrease as companies ignore or lag behind changing business models ushered in by technological evolutions. It is imperative that organizations find effective ways to harness big data to remain competitive. Those that have not already begun their digital transformations, or have no clear vision for how to do so, have likely already missed the boat—meaning they will soon be a footnote in a long line of once-great S&P 500 players. – Ashley Stirrup, CMO, Talend

Machine Learning

In-memory computing techniques will leverage the power of machine learning to enhance the value of operational intelligence. The year 2017 will see an accelerated adoption of scenarios that integrate machine learning with the power of in-memory computing, especially in e-commerce systems and the Internet of Things (IoT). E-commerce applications benefit by offering highly personalized experiences created by tracking and analyzing dynamic shopping behavior. IoT applications, such as those associated with windmills and solar arrays, benefit by delivering predictive feedback based on rapidly emerging patterns. In both of these applications, machine learning techniques can dramatically deepen the introspection and enhance operational intelligence. Once only practical only on supercomputers, machine learning techniques have evolved to become increasingly available on standard, commodity hardware. This enables IMDGs to apply them to the analysis of fast changing data and specifically to dynamic digital models of live systems. The ability of IMDGs to perform iterative computation in real-time and at extreme scale enables machine learning techniques to be easily integrated into stream processing which provides operational intelligence. – Chris Villinger, Vice President, Business Development and Marketing, ScaleOut Software

Machine learning will change the fabric of the enterprise – Machine learning will enable the adaptive enterprise, one that aligns business outcomes and customer needs in new and different ways. – Leena Joshi, VP of Product Marketing, Redis Labs

In 2017, I expect to see an increased emphasis on democratization of machine learning and artificial intelligence (AI). We’ve seen machine learning evolve from IBM Watson a few years ago to most recently with Salesforce and Oracle. While many think machine learning has gone mainstream, there is the potential for much more, such as performance monitoring and intelligent alerting. While companies might face false starts and initial mishaps while trying to crack the code, the increased number of organizations turning to AI and machine learning will lead to more successes next year. This increased adoption will help bring innovations faster to market, especially from a wide range of industries. – Mike Kelly, CTO, Blue Medora

There has been a lot of hype around machine learning for some time now, but in most cases it hasn’t been used very effectively. As we move forward, organizations are learning how to bring together all the ingredients needed to leverage machine learning – and I think that’s the story for 2017. We’ll see machine learning move from a mystical, over-hyped holy grail, to seeing more real-world, successful applications. Those who dismiss it as hocus-pocus will finally understand it’s real; those who distrust it will come to see its potential; and companies that are poised to leverage this capability for appropriate, practical applications will be able to ride the swell. It will still be a few years before machine learning becomes a tidal wave, but in 2017 it will be clear that it has a credible place in the business toolkit. – Jeff Evernham, Director of Consulting, North America, Sinequa

In 2017, ‘centralized-only’ monolithic software and silos of data disappear from the enterprise. Smart devices will collaborate and analyze what one another is saying. Real time machine-learning algorithms within modern distributed data applications will come into play – algorithms that are able to adjudicate ‘peer-to-peer’ decisions in real time. Data has gravity; it’s still expensive to move versus store in relative terms. This will spur the notion of processing analytics out at the edge, where the data was born and exists, and in real-time (versus moving everything into the cloud or back to a central location). – Scott Gnau, Chief Technology Officer, Hortonworks

Machine Learning will become de rigeur in the enterprise without many even noticing: What’s unique to today’s machine learning technology is that much of it originated and continues to be open source. This means that many different products and services are going to build machine learning into their platforms as a matter of course. As a result, more enterprises will be adopting machine learning in 2017 without even knowing they’re doing it because vendors are actively using ML to make their products smarter. Even existing products will soon use some variety of machine learning that will be delivered via an update or as an extra perk. – Avinash Lakshman, CEO, Hedvig

The Future of Machine Learning. We will finally deliver on the promise of machine learning: building models that can directly suggest or take action for large audiences. When we effectively scale machine learning, we can greatly increase the action-taking bandwidth of an enterprise. Instead of presenting a small number of business users in the enterprise with historical statistics à la business intelligence, companies can bring specific recommendations to thousands of front-line individuals responsible for taking action on behalf of the business. – Josh Lewis, VP of Product, Alpine Data

Machine learning-washing – Expect the market to be flooded with solutions that promise machine learning capabilities and grab headlines, but deliver no substance. – Toufic Boubez, VP Engineering, Machine Learning, Splunk

NoSQL

In 2017, NoSQL’s coming of age will be marked by a shift to workload-focused data strategies, meaning executives will answer questions about their business processes by examining the data workloads, use cases and end results they’re looking for. This mindset is in contrast to prior years when many decisions were driven from the bottom up by a technology-first approach, where executives would initiate projects by asking what types of tools best serve their purposes. This shift has been instigated by data technology, such as NoSQL databases, becoming increasingly accessible. – Adam Wray, CEO, Basho Technologies

Security

Cloud and data security agility will gain further importance — This is a rather obvious prediction, given the phobia of data breaches and the reticence of industries such as the financial sector to use public cloud technologies. Meanwhile, life sciences and retail, to name two industries, continue to forge ahead, realizing efficiencies while adhering to some of the strictest privacy and governance requirements set forth by regulators. With requirements such as the General Data Protection Regulation (GDPR) now in effect, companies not only have to ensure that their data is physically housed in the right geographic centers, but that the access complies with the most stringent regulations related to personal access and approvals for use of that data. Many vendors are now taking steps to provide the most secure, validated and agile infrastructure possible. Partnerships and use of Amazon Web Services, Google Cloud, and Microsoft Azure go a long way to providing the confidence and flexibility that many companies are looking for. In 2017, vendors offering Platform as a Service (PaaS) and tools themselves must also do their part in complying to Service Organization Control (SOC) types, as well as in the case of healthcare data, HITRUST (Health Information Trust Alliance), that provides an established security framework that can be used by all organizations that create, access, store or exchange sensitive and regulated data. – Ramon Chen, CMO, Reltio

Under the covers, machine learning is already becoming ubiquitous as it is embedded in many services that consumers take for granted. Increasingly, machine learning is becoming embedded in enterprise software and tooling for integrating and preparing data. Machine learning is placing a stress on enterprises to make data science a team sport; a big area for growth in 2017 will be solutions that spur collaboration, so the models and hypotheses that data scientists develop do not get bottled up on their desktops. – Ovum

Expect IoT to be even more vulnerable. Previous hacks into connected devices can be deemed as minor or inconvenient. But the recent DDoS attack involving Dyn shows IoT hacks are taking place on a larger and more disruptive scale. Hacking lightbulbs or setting off fire alarms is on the more mischievous side of the spectrum, but having the ability to override a car’s brake system or a “smart” pacemaker, for example, can turn connected devices into deadly weapons. Even worse, the lack of one standard for IoT (unlike Wi-Fi) will just make our devices more susceptible to large-scale breaches. Vendors have to recognize the parallels between security issues when Wi-Fi hit the mass market, and what’s happening with IoT. If they don’t move quickly to address the vulnerabilities, government regulations will need to come into play. Still, it would take something disastrous to galvanize government into action. – Richard Walters, SVP of Security Products, Intermedia

Over the past year there has been increased focused on data privacy, especially with the passing of the GDPR which represented one of the most comprehensive and refined set of standards put forth to date. In 2017, the trend line will to continue to move in the same direction and there will be a higher premium on data protection. With increased sensitivity around personal data, software vendors and enterprises will need to focus on what is being done to protect and manage personal data within the enterprise. To be successful companies must embrace privacy by design for themselves and the service providers they work with.” – Anthony West, CTO, Actiance

Spark

Spark and machine learning light up big data. In a survey of data architects, IT managers, and BI analysts, nearly 70% of the respondents favored Apache Spark over incumbent MapReduce, which is batch-oriented and doesn’t lend itself to interactive applications or real-time stream processing. These big-compute-on-big-data capabilities have elevated platforms featuring computation-intensive machine learning, AI, and graph algorithms. Microsoft Azure ML in particular has taken off thanks to its beginner-friendliness and easy integration with existing Microsoft platforms. Opening up ML to the masses will lead to the creation of more models and applications generating petabytes of data. In turn, all eyes will be on self-service software providers to see how they make this data approachable to the end user. – Dan Kogan, director of product marketing at Tableau

Analytics will experience a revolution in 2017. In the past, conversations about big data always included Hadoop (HDFS). But the industry today has hit a wall with its limitations to back up and preserve big data. As a result big data has become a black hole in the HDSFS cluster with no one managing it. In 2017, the Spark operating model – through ‘in memory analytics’ – will become a popular Big Data analytics option due to its ability to significantly reduce data movement and allow analytics to occur much earlier and faster in the process. – Vincent Hsu, VP, IBM Fellow, CTO for Storage and Software Defined Environment, IBM

Storage

People may think backup and recovery is dead, but they are sorely misunderstood and the move to the cloud actually makes backup and recovery more important than ever to safeguard data. Relying on the cloud won’t take care of everything! The need for backup and recovery will become very real as organizations continue betting on enterprise applications. Moreover, backup and recovery will take center stage as IT Ops and others in organizations have never stopped worrying about recovery, particularly as companies aggressively move toward modernized application and data delivery and consumption architectures. The likelihood of not knowing how to address or who to turn to in the event of an outage is just too great a risk. – Tarun Thakur, Co-founder and CEO at Datos IO

The Rise of the JBOD. In 2017, more users will come to understand that the storage for their scale-out nodes — whether you call it software-defined, “server SAN,” DAS, hyperconverged, whatever — can be attached externally to servers instead of buying servers with lots of disks and SSDs, without losing any of the performance or ease-of-use of internal DAS. Using simple, dumb, industry standard SAS JBODs (Just a Bunch Of Disks) means not having to throw away your storage when you upgrade your servers and vice-versa. It also gives you better flexibility and density in your deployments. – Tom Lyon, Chief Scientist, DriveScale

Verticals

One of the ongoing challenges in using big data to improve outcomes in healthcare has been its siloed natured. Healthcare providers have detailed clinical (patient) data within their organizations, while health insurers (payers) have more general claims data that goes across many providers. That is beginning to change, though, as the move to value-based care is encouraging providers and health payers to share their data to create a more complete picture of the patient. The latest trend is to bring in additional behavioral data, such as socio-economic and attitudinal data, to create more of a 360 degree view of not only what patients do but also what drives them to do it. Much as Facebook and Amazon.com use behavioral data to match users to relevant content. By applying next-generation analytics to this larger dataset, providers and payers can work together to help patients become healthier and stay healthy, reducing costs while helping them lead happier, more productive lives. – Rose Higgins, President, SCIO Health Analytics

We’ll usher in the next iteration of personalized care. Increased self-tracking, preventative care efforts, and advances in data science will give us more information on patients than ever before. We’ll use this data to create highly individual portraits of patients, that in turn, enable us to match physicians to patients in a very specific way. We can assign physicians based on their past success in treating similar patients and enable patients to have more informed and personal care. – Mark Scott, Chief Marketing Officer, Apixio

Data Analytics will go vertical (financial, medical, etc), and companies that build vertical solutions will dominate the market. General-purpose data analytics companies will start disappearing. Vertical data analytics startups will develop their own full-stack solutions to data collection, preparation and analytics. – Ihab Ilyas, co-founder of Tamr and Professor of Computer Science at the University of Waterloo

Big Data Will Transform Every Element of the Healthcare Supply Chain: The entire healthcare supply chain has been being digitized for the last several years. We’ve already witnessed the use of big data to improve not only patient care, but also payer-provider systems, reducing wasted overhead, predict epidemics, cure diseases, improve the quality of life and avoid preventable deaths. Combine this with the mass adoption of edge technologies to improve patient care and wellbeing such as wearables, mobile imaging devices, mobile health apps, etc. However, the use of data across the entire healthcare supply chain is about to reach a critical inflection point where the payoff from these initial big data investments will be bigger and come more quickly than ever before. As we move into 2017, healthcare leaders will find new ways to harness the power of big data to identify and uncover new areas for business process improvement, diagnose patients faster as well as drive better more personalized, preventative programs by integrating personally generated data with broader healthcare provider systems. – Ashley Stirrup, CMO, Talend

Author:  Daniel Gutierrez

Source:  http://insidebigdata.com/2016/12/21/big-data-industry-predictions-2017

Categorized in Internet Technology

If there's a word that describes the retail space in 2016, it's change. Change in technology, tools and best practices. And, (no surprise), 2017 promises more of same.

Here are five trends destined to make retailing more effective and profitable in 2017.

Multi-channel data integration

After using data analytics for several years, retailers are getting a clear idea of the benefits that high-volume, high-speed data analytics can provide. Unlimited computing capacity in the cloud and advanced analytics enable retailers to overcome a familiar challenge: collecting and analyzing huge volumes of different types of data (databases, social media and instant messages, reports).

More recent developments show by using data analytics software, retailers can unify online and offline data by:

  • Extracting data from different places such as legacy systems and database platforms on-premises or in the cloud.
  • Using new sources of data from commerce, supply chain and customer channels.
  • Integrating conventional retail information and data from new channels with company ERP, order management and warehousing software.
  • Delivering useful operations suggestions quickly enough to capture business opportunities as they occur. Modern data analytics software can cut the time from weeks to minutes.

Modern retail analytics software packages customer and supply chain data and trends in a single view of what's going on. Putting all relevant data into a form that's easy to understand and use helps business users set up operational and promotional strategies and continue to improve efficiency and performance.

Predictive data analytics

Every retailer wants to have the right products available to customers at the right place and time. Making this happen, however, is not an easy matter.

Data analytics provides retailers with a better understanding of their current business.Predictive analytics provides retailers with a look into the future.

Until recently, retailers had to rely on insights gained from their own experience and retailing skill, analyst forecasts and customer feedback. But it all added up to high-quality educated guessing.

Predictive analytics uses mountains of data, which retailers already have, and a wide array of technologies and approaches (statistical modeling, data mining and other techniques) to analyze and project the likely outcome of future events and consumer behavior.

The biggest business value of predictive analytics is its ability to help retailers stay ahead of the expectations of discerning, tech-savvy consumers. This includes:

  • Delivering a better shopping experience. That is, enabling customers to shop whenever and wherever they want in an attractive, no-worries environment, in the store or online.
  • Getting a clearer view of customers. This includes a 360-degree view of customers and click-stream analysis.
  • Merchandizing and planning. Add real-time promotions, demand forecasting, pricing and markdown optimization and out-of-stock analysis and management.

One of the biggest changes in retail analytics lies in where all this data comes from.

Internet of Things in retail

Pioneering major retailers are scrambling to collect and analyze data from the Internet of Things. Customers provide useful IoT data by using and connecting to smartphones, tablets and wearables. Brick-and-mortar stores use IoT data generated by digital signage and other in-store sensors and devices.

Together, these sources generate massive data stores that describe customer behavior. Retailers use this data to make decisions and create sales strategies for their brick and mortar stores and distribution centers.

Innovative uses of IoT data and technology enable retailers to:

  • Customize a shopper's in-store experience. Increasingly, customers expect personalized service. Data collected from in-store IoT devices and the shopping history of connected consumers enable retailers to create a shopping profile of each customer. IoT data analysis discovers shopping patterns that help retailers deliver a more customized shopping experience.
  • Make in-store operations more efficient. Data harvested from in-store, IoT-enabled smart cameras, beacons, and sensors provide store managers and employees with a deeper understanding of what does and doesn’t work well on the floor. For example, analysis of real-time location datafrom smartphone apps can be transformed into customer traffic patterns and buying behaviors. With this information, employees can be alerted to bottlenecks immediately and reduce customer wait times at the cashiers.
  • Improve inventory and supply chain management. Smart transportation management applications and demand-aware warehouse fulfillment are two ways to transform IoT data to into an understanding of what’s underperforming, overstocked or running out of stock at your store.
  • Take advantage of new revenue opportunities: Leading-edge retailers are using the IoT to find new methods of acquiring customers and increasing revenues. For example, beacons and Wi-Fi can create an in-store environment, in which customers engage in contests, meet-and-greet events and social media product reviews.

Self-service analytics software  

Not long ago, data analytics software users had to wait for reports designed and delivered by data analyst middlemen. When customers lobbied vendors for change, they got results. Business users got self-service applications that included easy-to-use dashboards and enabled direct queries. The software empowered business users to ask relevant questions and get answers—quickly—without data science degrees.

Specialized retail analytics software enables store managers and retail decision makers to:

  • Use easy-to-understand analytics methods on data relevant to their store.
  • Easily access, explore, and analyze data with just a few clicks
  • Quickly and easily engage with supply chain data.
  • Make decisions by analyzing products and merchandising methods.
  • Identify spending patterns and gain insight into customer behavior by choosing from a library of interactive visualizations.

Mobile to the rescue

We’ve all heard the complaint that customers enter brick-and-mortar stores with more product information than the staff. Equipping staff members with mobile devices linked to key internal applications and databases enables associates to personalize customer services and perform "save the sale" rescues with pricing, promotion and product information.

Author:  Ilan Hertz

Source:  http://www.retailcustomerexperience.com/blogs/data-analytics-and-the-changing-world-of-retail-in-2017

Categorized in Search Engine

Against 2016’s difficult backdrop of Brexit debates, political commotion and muted economic expansion, it would be easy to assume 2017 would be a slow start in terms of growth and innovation.  But the outlook for technology promises to be anything but. 

As managing director of one of the fastest growing tech firms in the UK, I’ve experienced first-hand how the pace of technological development has taken place at breakneck speed.  And while some may be taking a step back, cautious of what’s to come, technology is one industry that shows no signs of slowing down.

The fluid workforce

Technology has clearly accelerated a shift in our society towards a faster-moving, temporary, project based workforce. This promises to be a trend that continues into 2017. 

For many businesses, skill-shortages will drive technology investment towards more sustainable, intelligent, intuitive and integrated solutions.  

This shift in employment will also drive further specialism between consumer brands delivering either high-end, bespoke products (to the few) or high automated, low-cost, self-service products (mass-market). It will be even more competitive, with a focus on seamless customer experiences.

Productivity and unification

Productivity isn’t necessarily about adding more functionality. 2017 will be the year for choosing unified apps – applications that work seamlessly across a variety of different channels and devices. In will be critical that unified apps work in the same way whether using an iPad, Microsoft Surface, PC, MAC, iPhone or smartphone. 

This must reduce deployment overheads for companies investing in technology - as employees will already know how to navigate and use the systems on the devices of their choice, this removes complexity and improves productivity.

Biometrics

Biometrics is one of the hottest topics in technology and cybersecurity markets today - the use of biometrics for user authentication and identity is essential in tomorrow’s world. We are likely to see an increasing variety of industries making use of biometrics, as it becomes more reliable and more affordable.  For example, in healthcare, biometric technology can be used to ensure patient identification. 

In the leisure industry, fingerprint identification is now being used in gyms to ensure that only members can access the facilities. In industry, biometrics are being used for tracking time and attendance along with access control.  What’s of real interest here is the intelligence that can be harnessed, thanks to certain identification. Joined-up intelligence, internal and bureau sourced, means businesses can understand more about their customers’ needs and preferences. 

It can enable them to make faster, better decisions based on greater evidence. This will help them achieve the best possible outcome for their business (efficiencies) and the end customer (customer experience). Technology will finally guarantee businesses can accurately identify customers, real-time, as individuals - across any channel or device. 

Personalisation techniques, in marketing, will no longer be considered a dream but essential to performance and loyalty.  It also means an era of real-time, intelligent programmatic advertising. This refers to the process of using software to buy digital advertising - most common in real-time bidding - where no human would be able to handle the auction quick enough. 

Applying intelligence to programmatic advertising, across channels, will not only reduce wasted advertising spend but also ‘spam’ advertising techniques.  

 ePayments unbound

The PSD2 (Payments Services Directive 2) and Open API (application programming interface) standards in Banking will come into force in the UK (and the wider EU) soon. Implementing technologies that comply with PSD2 will bring exciting innovations in security and app development as well as other products or services, in order to stay ahead of the curve.

Intelligence not data

Cloud computing and big data are no longer just buzz words, they are driving transformation even for small and medium-sized businesses. We are about to enter the era of powerful tools that can interpret big data, thanks to the emergence of real machine learning.  

Better reporting obviously leads to better decision making. Artificial intelligence and machine learning have been around for a while, but they are more advanced and prominent. Autonomous systems that can process information, alter their behaviour, predict actions, understand conversation or trends are being developed thanks to advanced algorithms, parallel processing and massive data sets.  

Machine learning will be taking on big data - taking historical data and projecting forward, for real-world applications. Microsoft Dynamics NAV already has machine learning capability for sales forecasting, stock forecasting and cash flow forecasting.  

Human empowerment

Intelligent apps such as VPAs (Virtual Purchasing Assistants) can now perform some of the functions of a human assistant, making everyday tasks easier (by prioritising emails, for example) and its users more effective (by highlighting the most important connections).  

You’ll soon be hard pressed to find a business application without AI, whether it’s for marketing, resource planning or security. Empowered millennials are starting to catch onto the fact that empowering experiences are worth so much more than material things alone.  From smart vehicles to devices as innocuous as light bulbs, intelligence is being used to enhance the experience we have with our things. The more intelligent things there are, the bigger intelligent networks and network applications will become. 

There are more and more devices where you can ask a question and you’ll get an instant answer. Computer adaptability is boosted by faster processing and internet connectivity.

Content is still king

Nearly two decades ago, Bill Gates declared “content is king!” Since then we’ve experienced a seismic content revolution: social media, user generated content and augmented reality. However, I believe that content will now have to cater for information overload and even shorter attention spans such as personalised, dynamically built video or animated content presentations. 

Virtual and augmented reality will continue to blend the digital and physical worlds. Graphic overlays and visual immersion are just a couple of examples of how virtual reality will also be applied and tailored.

Author:  Craig Such

Source:  http://www.itproportal.com/features/whats-next-for-tech-in-2017

Categorized in Internet Technology

In his predictions for 2017, John Kennedy forecasts how blockchain will be about more than money, IT will move to the clouds and bots will become humanity’s new best friends.

Predicting the future in tech is never an easy business, mainly because tech companies are, by nature, secretive and like to have the last word. Any time I predict what Apple is up to, for example, I always end on the line: “But only Apple really knows.” Because that is simply the truth.

But no one could have foreseen the events of 2016. We witnessed the election of Donald Trump to the US presidency, the loss of so many stars who wrote the soundtracks to our lives, the tragic killings in Nice and the bloody endgame in Aleppo, which will always be a shame for the world to remember.

Predictions for 2017 build on a crazy 2016

In tech, it was business as usual with very few real surprises; except maybe for Apple killing off the headphone jack in its iPhones; fake news infecting Facebook and allegedly influencing the US elections; Putin’s government hacking America; exploding Samsung Galaxy Note7s; hacking getting out of control, especially with ransomware and leaks to Wikileaks; Apple taking on the FBI; no one wanting to buy Twitter; Vine dying on the leaf; and mega acquisitions, such as Facebook buying LinkedIn and Verizon buying Yahoo. It all sounds like a rousing verse from R.E.M.’s It’s the End of the World as We Know It…

On the home front in Ireland, the biggest news was the European Commission lobbying a €13bn tax levy against Apple to the chagrin of the latter and the Irish Government; Britain’s decision to Brexit the EU; the stalling and stalling of the National Broadband Plan; and of course, mega acquisitions such as Verizon’s decision to buy Fleetmatics for $2.4bn and Intel’s acquisition of Movidius for an alleged sum $300m.

So, dear reader, what will 2017 hold for us through the tech lens?

Blockchain will be about more than just payments

If there was one breakthrough technology of 2016, it had to be blockchain: the enabling smart ledger technology that was fundamental to the rise of cryptocurrencies like bitcoin and a whole slew of new fintech start-ups and platforms.

But more and more experts are coming to the conclusion that blockchain technology could be very useful in ways that go beyond fintech or cryptocurrencies.

The ingenious automated technology could end up being an enabling force for a panoply of platforms and uses, such as network and systems management. The key is the digital trail of crumbs: blockchain technology – which underpins emerging digital, virtual or cryptocurrencies – consists of blocks that hold timestamped batches of recent valid transactions, which form a chain with each block reinforcing those preceding it.

Pay close attention to an interview I did with Seamus Cushley, PwC’s expert on blockchain who runs the company’s blockchain lab in Belfast. Cushley indicated that in the last nine months of 2016, some $1.4bn of investment went into blockchain start-ups.

According to Cushley, blockchain is being investigated not only as a way to enable the viable exchange of contracts for value in everything from FX trading to property acquisitions and more, it foretells the future structure of the internet as we know it.

The future of work

If, like me, you witnessed the onset of the internet being heralded as a revolution in how we work, leading to all kinds of newfangled ways of working, such as teleworking, e-working or nearshoring… you were had. Our lives were meant to get easier, there would be more quality time with loved ones, more time to be creative… wrong.

The digital world has created a noose that means people are working longer hours. Countries like France have even passed laws preventing employers from emailing workers after certain hours.

As skills shortages rise, stress levels soar and entrepreneurship becomes more appealing to talented young executives eager to break free of the rat race, employers will be forced to reassess how they conduct relationships with workers. How do they retain talent, get the best out of enthusiastic people and ensure health levels are optimal?

‘What is the future of work?’ is a question that employers and employees alike will obsess over in 2017 and beyond. Creative companies that value human capital will examine new ways of working, pilot intrapreneurship endeavours to help sate the entrepreneurial wanderings of top talent, vent creative frustrations and ultimately find the key to a quality work/life balance.

The old mantra that work should not just be a place to go, but somewhere you actually enjoy going to, might be dusted off and given a new shine.

Time will tell, however, if questions of the future of work will be a meaningful cause or just more management consulting navel-gazing.

Fintech goes mainstream

In parallel with the arrival in Ireland of mobile wallet services like Android Pay (recently) and Apple Pay (eventually), smartphone-toting consumers are going to embrace fintech apps as a cleverer way of managing their money.

Think of these apps as the Swiss Army knives of finance.

Companies like Dublin and London-based Circle – which enables users to instantaneously transfer funds to friends and family via the app or by text message on the iPhone, using blockchain as a core enabler and Barclays as a licensed service provider – are at the forefront of this trend.

Rather than displacing banks as some had feared, this signals a gradual move by banks to employ fintech apps on the front line as an easier and more cost-effective way to deal with consumers, while enabling them to focus on more productive, higher value work as branches become fewer.

Expect banks to employ programmes to franchise fintech apps or initiate outright acquisitions in 2017.

Machine learning becomes a discipline and no longer confused with AI

For too long, artificial intelligence (AI) and machine learning have been lumped into the same conversation. That is going to change in 2017, as a broader understanding of what AI is all about pervades the tech industry.

Machine learning is remembering and AI is thinking, remembering, deciding and acting.

Quite simply, machine learning in apps and internet services is all about improving as time goes on, learning and assimilating users’ tastes and preferences – for example, for airline travel or hotels.

AI, on the other hand, powers the bots that have conversations with the users and employs machine learning as one powerful subset of a myriad of capabilities.

Start-ups and established tech players that use machine learning, which I have met on the trail from Amsterdam to Lisbon in the past year, are quite clear that it is not to be confused with full AI.

Beautiful Bots

Humankind’s friendship with bots – or automated artificial agents – will be cemented in 2017.

Facebook is currently leading the charge, creating experiences where already it is hard to decipher whether you are talking to a human or a machine.

This portends major changes for the future of customer relationship management, which no doubt Microsoft, Salesforce and fast-growing companies like Intercom are watching very closely.

Could bots be mankind’s next best friend?

Tech leaders will be the new business leaders

The digital economy is the economy. Across the world in 2016, thousands of traditional businesses went to sleep one night and awoke the next day as data businesses.

The trend will continue in 2017, as the internet, smartphone apps or other digital filters become the aperture through which consumers increasingly transact.

You are seeing this on retail floors of stores like River Island, where consumers can shop online and collect in-store, on flights with Ryanair where the digital experience continues long after you check in or check out, and the disruption that players like Airbnb and Uber are causing traditional industries like hospitality and transport, respectively.

This is signalling a major transformation in how companies deal with their customers and view their data. According to IDC, 50pc of the Global 2000 companies will be depending on digital products, services and experiences to connect with customers.

By 2021, it is forecast that a third of CEOs and COOs of Global 2000 companies will have spent at least five years in a tech leadership role.

Cloud will reign eternal

From being a mere concept in 2008 to today, where most consumers and executives rely on the cloud consistently – from Facebook and WhatsApp to Dropbox and Office 365 – cloud computing is increasingly becoming the nerve centre of IT infrastructure.

Ireland saw major data centre investments and acquisitions in 2016, from Apple building an €850m data centre in Athenry, Co Galway, to Facebook building a massive data centre in Clonee, Co Meath. Combine this with Equinix buying Telecity and its raft of data centres in and around Dublin, and it’s clear that Ireland is in the eye of the data storm.

This isn’t just about social media or e-commerce; the reality is that more and more IT infrastructure, which used to exist on premises in companies, will have moved to the cloud.

IDC predicts that by 2020, 67pc of enterprise IT infrastructure and software will be in the cloud.

By 2018, 60pc of IT will be done off premises and not only that, but 43pc will be processed at the edge by 2019.

In a nutshell, cloud won’t be an Amazonian concept (sorry AWS) but rather, a fully fledged reality that is 100pc trusted by users.

The fourth platform

As cloud’s roots grow deeper, the idea of computing as a thing that sits on our desk or in our hands will dissipate. Even as more and more of the world’s population join the mobile revolution, the golden era of the smartphone is coming to a close. That doesn’t mean the smartphone is going away any time soon, but it will become the lynchpin of a slew of new computing experiences that will draw our eyes elsewhere.

Big data, internet of things, virtual reality (VR) and augmented reality (AR), 3D printing, robotics, next-generation security, blockchain – all of these technologies will happen around us, with data being the fabric and the smartphone being the connecting device.

In other words, computing experiences will be occur without relying on a primary screen as the conduit. This is the fourth platform.

The mainstreaming of AR and VR

VR and AR have been slowly entering the fray. 2016 was a significant year that finally saw Microsoft take the wraps off HoloLens, as well as Oculus Rift arriving, along with a slew of competing devices from HTC, Samsung and Sony.

VR has been a kind of revolution and it hasn’t. The high-end experiences promised by Oculus and Microsoft are still hampered by computing power.

At the lower end, smartphone-based VR experiences from HTC and Samsung – and let’s not forget Google’s Cardboard and similar products which can be found in any supermarket or toy store – are still gimicky.

Keep your eyes and ears (no pun intended) open for what Google intends to do with its Daydream headset, which portends a merging of the VR and AR worlds, so the headset can also overlay virtual reality experiences onto the physical world before us. In a sense, this could be the future of the recently shelved Google Glass or the newly launched Snap Spectacles.

Expect the games and experiences to become more intelligent and textured. Keep an eye on what Irish firm Immersive VR Education – creators of Apollo and Titanic virtual experiences – has planned in the year ahead, as VR and AR move from novel to to natural.

Smart things and voice

Like I said, smartphones will occupy less of the stage and give way to smarter things. 2016 saw Amazon up its game with Echo, its voice-based e-commerce service, as well as its Dash buttons, which order consumables like washing powder or nappies in just one touch.

Google will be no slouch in 2017, having already revealed its Google Home speech-based product at I/O earlier this year.

This is Google’s fourth platform play and the company is closely shadowing, if not exceeding, rivals like Apple on the payments front.

2017 will see a kind of arms race, where players like Amazon and Google will endeavour to become the partner of choice for a whole range of internet of things (IoT) players who see e-commerce as a potent ingredient in their smart things.

Facebook acceleration, Oculus telepresence and Slack rivalry

Rather than being email killers (if only), most workers are up to their tonsils in additional tools and things to keep an eye on; like Slack, Trello, Wrike, and other digital platforms aimed at simplifying workflow.

Others giants like Microsoft (Teams) and Facebook (Workplace) added to the cacophony in 2016.

It is high time that someone decided to dominate this space for once and for all with tools that eradicate the need for all the others.

There is a golden opportunity for Microsoft to do more to bring Skype and Teams together, or for Facebook to finally reveal its telepresence vision for the future of work with Oculus and Workplace.

Keep an eye on other dark horses like Cork-based Teamwork or Salesforce (which almost bought Twitter). They may do something to finally get rid of the screen noise and clutter (sorry, Microsoft) that is the reality of the modern-day worker.

The iPhone hits 10, Apple revs up for its newest phase

It is hard to believe that it is nearly 10 years since Steve Jobs took to the stage at Apple World in 2006 and said “One more thing …”

That one more thing was the iPhone and, having gone through more than seven different phases of the device, Apple will no doubt do something to celebrate the iPhone at 10.

Considering the phone’s form factor has remained mostly the same for the last three generations, I expect Apple to reveal a wholly new design to the iPhone to signal its next phase. As I said, only Apple really knows what this form factor will look like, but expect the design to inform all future phone designs from rivals in the Android camp. I mean, why break with tradition?

Another next phase for Apple, however, may see the company finally break its silence on what it intends to do with cars.

Apple is revving up to be a big noise in the IoT and healthcare spaces, but the idea of an Apple car is still igniting people’s imaginations.

Will Apple build a car or just a car OS? Given that Apple has so far dashed expectations on television hardware, the car idea is one that just won’t disappear.

Codenamed Project Titan and spearheaded by some of Apple’s top talent and roughly 1,000 workers, Apple may choose the timing of the 10th anniversary of the iPhone to shed some light on the future of the company for the next decade.

Will that involve four wheels? Definitely. But will it be an Apple car or OS? We’ll have to wait and see.

The Solar revolution

Given that Elon Musk’s master plan goes beyond cars and includes trucks, buses and homes, the attractive economies of scale of solar panels are hard to ignore.

Musk recently revealed his solar roof concept that would use tiles made of glass, which look like ordinary roof tiles, to power up homes.

This might not sound as crazy or unfeasible as you would think, when you consider that Scientific American recently said the average cost of solar models per watt dropped from $22 in 1980 to under $3 today.

It suggests that soon, an average solar tile per watt will be $1.75.

That makes 2017 a lynchpin year for a whole new revolution in solar energy.

But time will tell.

Author:  John Kennedy

Source:  https://www.siliconrepublic.com/companies/tech-predictions-2017

Categorized in Internet Privacy
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