2016 saw momentum in many areas – DevOps, cloud technologies and big data, being at the thrust of innovation. So, what tech predictions will define 2017?
Here are some of the emerging trends that we expect to see more of in 2017, plus some tips on how you can take advantage of them.
Imagine if your HR team could predict employee attrition or your marketing team could anticipate consumer behavior and serve up customer experiences based on those insights? Predictive analytics makes this possible.
As more and more businesses adopt a data-driven approach, predictive analytics will increasingly become the norm and a core basis of decision-making across a variety of sectors. Gartner predicts that by 2018, more than half of global enterprises will leverage analytics as they begin to wrap their arms around their big data and channel it to compete more effectively. Spurred by the cloud, the cost base for predictive analytics solutions has dropped significantly in the past few years, making smart data and self-service data solutions more accessible.
In addition to cloud developments, machine learning and artificial intelligence (AI) applied to predictive analytics have made a huge leap over the past few years as well, both in terms of capability and ease of application and deployment – making predictive analytics even smarter.
But with so many opportunities, arguably the hardest part of predictive analytics is knowing where to start. What should you predict? How do you find the data? What processes do you need in place to start using a predictive analytics engine?
In the past, predictive analytics initiatives have been approached as huge fixed-price projects. Consultants are brought in with the mindset of achieving as many insights as they can within a limited window. We anticipate this approach to shift in 2017, with forward-thinking organizations leveraging a smart approach that treats predictive analytics as a continuous effort – with business need at its core.
Instead of focusing on a product, smart organizations will begin their journey to predictive analytics by focusing on the minimal viable prediction (MVP) - whether it’s predicting stock fluctuations, future revenues, or hospital patient readmissions, whatever it may be, finding ways to get to that in the fastest way possible.
With this nucleus in place, organizations can build from there. Iteratively tweaking their data, adding new data, or incorporating new technology. The magic of predictive analytics is that once your data is integrated interlinkages and prediction scenarios emerge and the next steps intuitively emerge organically from there.
In 2015, Gartner predicted that DevOps would evolve from a niche to a mainstream strategy employed by 25% of global organizations in 2016—but did it? Not quite, and we don’t anticipate it to do so in 2017 either. However, what we are observing is another step in the evolution of DevOps that is about to abstract away the complexities of DevOps management.
To understand where DevOps is going next year, let’s first look at how far it’s come.
DevOps started life as an idea or philosophy. The promise of a cultural shift that introduces transparent relationships between developers and operations, and fast and stable workflows that improve IT efficiency, business agility, and bring unprecedented speed to market.
This sparked a dialog and the theory of DevOps began to be conceptualized. The market responded accordingly. DevOps-ready tools, such as Kubernetes and Docker, complete with out-of-the-box functionality to support DevOps characteristics and functionality flooded the market.
This brings us to today.
No one has been able to package the DevOps concept. Yet, the imperative to deliver services faster has transformed DevOps into a real business need and will drive adoption in 2017.
This growing DevOps demand is leading to the development of new comprehensive solutions that will help organizations adopt DevOps faster and cheaper. In 2017, we’ll start seeing solutions that loosely couple DevOps tools together and abstract them out from the organizational layer making DevOps nirvana – the faster delivery of services – more realizable and cost-effective spurring a faster adoption of DevOps than we’ve seen to date. However, it’s not IT that’s championing this shift, it’s the business. The need for greater agility and faster times to market is disrupting the old way of doing things and steering innovation.
Get ready to engage and chat with virtual assistants, apps, and other voice-controlled artificial intelligence. 2017 will be the year when AI and voice-based user experiences will provoke a dramatic shift in how we interact with applications.
Factors driving this boom in AI include the emergence of APIs and pre-existing code that give developers access to the building blocks they need to develop a specific app or build their own AI system without the need for a PhD. Just look at what you can do already with Amazon’s Alexa API.
More pioneering work is also being done by Google, Microsoft, Netflix, Facebook, and others as they break ground with new technologies such as speech recognition, autonomous cars, recommendation engines, and smart chat bots (often used to take over sales and customer service functions), all of which bring AI and voice-recognition to the masses.
So, anticipate a lot of noise around AI in 2017. While adoption may not catch up with excitement yet, 2017 will likely be the last year that you interact with your mobile bank app by touch alone; expect to have a conversation with it very soon!
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