10 top strategic technology trends for 2016

For any organisation, big or small, investments in technology seem like a gamble. Get it right, and you seem like a visionary in touch with the times and will likely be looked to as a leader in your field. Get it wrong, and you’ll be pilloried and spend years paying for your mistakes.

It’s not easy, and even the biggest players can get it wrong, but with the right kind of research you can radically increase your chances of getting it right.

At the Gartner Symposium, currently underway in Cape Town, Research VP Brian Baker, on Monday laid out the technology research company’s top 10 strategic technology trends for 2016.

It’s not guaranteed that these are the trends that will determine the course tech takes in the next year or so, but it is a useful guide.

1. Device Mesh

Thanks to embedded sensors, we interact with an increasingly wide array of devices. But as we move around during the day, the devices we interact with constantly change too, from your smart office systems, to the technology you have in your house, to your Saturday morning bike ride. The way we interact with those devices in and between those scenarios is what Gartner calls the “Device Mesh”.

Right now, the technology research house says, those technologies have single application uses, but in the future they’re more likely to start talking to each other and working in unison.

2. Ambient UX

That ability to gather contextual information and collaborate with other devices is what Gartner calls AMbient UX. It’s an exciting idea, and will give users a much better all round experience, but it also increases the complexity of application design.

According to Gartner, the philosophy Ambient UX will reach its peak when we’re no longer aware that we’re using an app, with virtual assistants not only running helping us with queries but actively organising aspects of our lives.

3. 3D-printing materials

3D printing has been a hyped technology for a number of years now, but Gartner says what’s for more exciting than the concept of 3D-printing as a whole are the materials we can create 3D-printed objects with. These range from biological materials, to carbon-fibre infused plastics and food.

The advances we’ve made in the 3D-printing material space have, for instance, allowed Tesla to build engine parts it couldn’t otherwise have done, as well as to make massive advances in bone, skin, and organ transplants.

4. Information of Everything

Gartner predicts that by 2020, 25-billion devices will be generating information about everything. That means there’s plenty of data for people to use, but it also creates massive headaches

In a given period, for instance a contemporary car can produce 10TB of data, but just 25GB of that data is stored. You have to be absolutely certain about which data is worth keeping.

That need for certainty applies just as much to external information as it does to information generated within an organisation.

5. Machine Learning

The ability to learn and “appear to understand” is ultimately what sets smart machines apart from their non-smart cousins. There are plenty of competing technoligies in this space, but most of them have the simple aim of detecting and learning from patterns.

A contemporary example of this is the Eko Core Stethoscope. The device takes the ordinary stethoscope and, adds in the ability to see, record, and share heart sounds. That’s incredibly useful for GPs because it allows them to provide precise data to specialists, effectively allowing them to overcome their lack of experience in the cardiatric space.

6. Autonomous agents and things

There’s actually a fairly wide spectrum of autonomous smart machines from obvious, physical ones such as drones and robots to less obvious ones such as natural language processors.

These autonomous agents can be incredibly disruptive. Perhaps the most obvious, but least visible, examples of this are in the customer support space. There are a number of companies working on these virtual customer support agents. One that’s grabbed plenty of headlines is Amelia.

Amelia learns the same written instructions as her human colleagues but the developers claim that she absorbs the information at a fraction of the time it takes a human being. On top of that, not only is she reading but she understands what she is reading.

When exposed to the same information as any new employee in a company, Amelia can quickly grasp it and then apply her knowledge to solve the queries in a wide range of business processes. Further more, Amelia, by observing a colleague work, she can learn from them and continually build her own cognitive understanding.

Amelia speaks more than 20 languages. She interacts like a human being. And to fully grasp a process she needs only to be taught once and she is able to communicate with customers in their language.

As technologies like Amelia become more ubiquitous, they’re going to start running into the same problems as other, more visible technologies are.

Take autonomous vehicles for instance. As Burke notes, mines have been using autonomous trucks for years now, with other industries following suit.

“The limitation of autonomous vehicles today is that they only work in a controlled environment,” he says.

According to Burke, it’s not necessarily down to the tech either. In fact, he says, “the regulatory constraints today are much larger than the technological constraints”

The same holds true of the building industry, where some companies are using semi-autonomous brick-laying robots, which can work much faster than even the most skilled human bricklayers.

7. Adaptive security architecture

Security in the tech space is changing all the time, but it looks set to become even more adaptive. According to Gartner, businesses have to change the way they think about architecture from block and defend to predictive model where we enable apps to protect themselves. The research house also says companies are going to have to integrate security into every part of the business process, including customer analysis.

8. Advanced Customer Architecture

Much of the work driving this trend revolves around making hardware mimic biological brains. There are a number of ways that mimicry is happening, including the scalability provided by GPUs, even outside of the graphics spaces, field programmable gate arrays such as Facebook’s Deepface facial recognition software and Bing’s deep learning technologies, and Neuromorphic chips.

9. Mesh App and Service Architecture

This trend ultimately boils down to not being afraid to mesh other apps and services with your own, especially when they’re complimentary. Take a look at the work Zomato’s done integrating Uber into its app offering for instance. It’s been a massive success, gone global, and took a fraction of the work it would’ve for either company to build out their own offerings.

As Gartner points out, it’s a mindshift from building to monoliths to technologically-driven, modular microservices.

10. IoT Architecture and Platforms

The Internet of Things is no longer a far-off dream. It’s here. We all interact with a plethora of connected devices. the problem though is that right now the space still has a Wild West kind of feel to it.

According to Baker, the problem right now lies with the providers of IoT platforms. What IoT platforms do is provide the gateways and information for things, from streetlights to smartwatches. Right now those platforms are incredibly fragmented, with no clear winners in sight. Gartner doesn’t see that changing through 2018 and reckons that IT leaders will still need to compose IoT solutions from multiple providers for a little while still.

After that period of churn though, you can expect the market to move towards more general IoT platforms.



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