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 Transform More Functions
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.
Intelligent apps are growing beyond VPAs into other types of software categories, like security, marketing, and ERP. A broad array of business processes will become increasingly automated.
Gartner predicts that the majority ofÂ 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.
Human Relationships with Bots Will Soar
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 itsÂ April announcementÂ 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 inÂ decision makingÂ by translating user problems into algorithms and then delivering the results in actionable insights that humans can understand.
Beyond Rule-Based Algorithms: AI & Advanced Machine Learning
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 investÂ 300% more in AI in 2017 than in 2016.Â 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. But data scientists don’t need to worry about protecting their jobs. Instead, their roles will shift slightly. Reporting and simple queries won’t fall on their shoulders anymore. Instead, data scientists will be required to be more creative and hands-on in how they help departments make use of data.
Prescriptive Analytics Overpowers Predictive Models
Predictive analytics have ruled data up until now, but the resulting models can only take business decisions so far. Which is exactly why in 2017, more companies will value prescriptive analytics initiatives. Gartner predicts the market for predictive analyticsÂ to grow to $1.1 billion by 2019 â€“ 22% CAGR from 2014.
Prescriptive analytics goes one step further than the insights revealed by predictive analytics by providing actionable recommendations based on findings. These models analyze existing data patterns and then review the outcomes of various possible scenarios, allowing you to test out decisions before they’re made. The results of these hypothetical scenarios ultimately impact real decision making.
Currently, only 10% of organizations use this technique, but Gartner predicts that by 2020, that percentage will grow to 35%. Due to the buzz surrounding prescriptive analytics in 2016, it’s easy to predict that 2017 will see a huge expansion in adoption.
More Advanced Behavioral Analytics
Companies will spend aÂ predicted $77.37 billion on ads in the US next year. Better believe that companies want that money to be well spent. A deep understanding of the audience is the number one factor for success.
We’re seeing far more advanced capabilities in terms of targeting. Simply segmenting audiences by age or gender is nearly useless. Targeting personalities, interests, and desires has a much bigger payoff.
IoT has become the gateway to this high level of personalization. As the number of devices and sensors connected to the internet soars deeper into the billions, companies will clamor to make use of the data.
Unstructured Data Takes the Spotlight
In 2015, onlyÂ 43% of IT teams surveyed by IDG EnterpriseÂ said that they analyze unstructured data.
Inhibitors to enterprise IT adoption of big analytics are typically a lack of big data skills and the right technologies. With machine learning and data visualization tools, the analysis of unstructured data is now becoming available to more teams.
And, considering that unstructured data provides reasons, motivations and deeper insights that structured data can’t, this is good news.
The reality is that almost everything can be measured. Not requiring for there to be a structure around what you measure allows for the analysis of what customers really want and how they view your brand.
IT teams will be placing unstructured data in high priority, and a wealth of valuable insights will be uncovered in 2017.
Investment in Digital Twins on the Rise
Gartner’s 2017 study also predicts in increase in the use of digital twins, which is a dynamic software model of a physical object or system. These digital clones are not only able to sense the behavior of their physical counterparts but alter it. Imagine testing drugs on a complex human model instead of animals, or a pressure gauge that adjusts itself and you understand the purpose of a digital twin.
Gartner’s prediction is that within 3 to 5 years, there will be billions of things with their very own digital twin. This complex technology makes use of physics data, sensors in the physical world, and knowledge of how the object responds to its environment.
Think of a digital twin like the go-between for a human technician and a monitoring device. Based on preset criteria, it alters its own behavior, allowing for increased monitoring capabilities and time savings.
But for digital twins to increase as much as predicted, adoption must start with an improved understanding of the collaboration now possible between data and humans.
With 2017 on the horizon, most companies are looking to make better use of data and get more aggressive in its application. We expect to see new approaches in decision engineering algorithms, continued productization, and the expansion of these top analytics technologies.
Ultimately, companies who invest in combining data, analytics and technology will have a leg up in any imaginable area of their business.