With 2016 in the rear view mirror, we look back at a mixed year for data analytics in terms of adoption and optimization. This past year, companies opened up to advanced analytics techniques yet still struggle to act on them. 2016 saw a large shift towards productized analytics solutions over service-based delivery models, as both providers and companies sought to drive results.
Undoubtedly, 2017 will be an exciting time for data analytics, as more and more companies jump on board with emerging technologies, and larger power players innovate with increasingly advanced initiatives. Here are some of the most promising technologies on our radar:
Intelligent apps will become increasingly useful as virtual personal assistants (VPAs), with the ability to not just find resources and conversations, but to prioritize items ahead of time. The aim is for users to become productive and effective with VPAs, and 2017 will take us closer to that goal.
Gartner predicts that the majority of the world’s largest 200 companies will use intelligent apps by 2018. The result will be not only greater productivity, but refined marketing efforts and improved customer experiences.
Bots are on the rise. The number of bots, the number of people encountering bots on a daily basis and the comfort level with bots will all soar in 2017 as more companies use them for decision making, marketing and assistance on the job.
Since the spring of 2016, Facebook has been at the helm of proliferating customer-facing bots, starting with its April announcement that bots are welcome on Messenger. This year companies such as Verizon, Domino’s Pizza and several large US news outlets have created chatbots. These chatbots help customers get support, complete new purchases and interact with new content. This movement will continue to accelerate with new frameworks and tools that are making bots faster to develop than ever before.
Bots can also assist in making use of massive troves of data. They assist in decision making by translating user problems into algorithms and then delivering the results in actionable insights that humans can understand.
2017 will see an increase in intelligence that gets embedded in a variety of systems, so that systems can learn from existing behavior and change future behavior. Improved parallel processing power, advanced algorithms, and increasingly larger data sets have helped to usher in the era of AI.
Machines that learn from and adapt to their environments will open up a variety of use cases, from intelligent manufacturing automation to smart sleep tracking mattresses, to real time public transportation routing, hacker detection systems and predictive disease detection. The list is endless.
While AI may replace some low-skill jobs, these machines will work collaboratively with people in higher skilled jobs and will even open entire new industries. Gartner predicts that in 2017, AI will be the most disruptive force in IT.
Changes in Desired Traits of Data Scientists
A Forrester survey determined that businesses will invest 300% more in AI in 2017 than in 2016. That’s quite a jump.
Machines are now able to analyze data on a much larger scale than humans will ever be able. AI will “drive faster business decisions in marketing, e-commerce, product management, and other areas of the business by helping close the gap from insights to action,” notes Forrester.
The technology can assist professionals who aren’t formally trained in its usage. Namely, people who aren’t technologists. In Forrester’s 2015 survey, 51% of people using data to make decisions said they could do so without the help of a data scientist. But Forrester now predicts this number will grow to 66% in 2017.
In other words, data is becoming more accessible.