Well, 2016 is all history and it is time to focus on the near future. That’s why we decided to share our point of view on what is going to be hot in data analytics world in 2017.
Self-service data analytics applications are going mainstream
The ability of business users to run analytics of their own is going more and more viable. A lot of tools are hitting the market and they are compatible with different systems which make such integration opportunity a reasonable business case. On top of this, it magically erases the cost of the in-house analytics team.
This is what A4E is doing with its marketplace of data analytics apps.
When your car is shot speeding by a traffic camera, remember that it was possible because of computer vision. Complex algorithms are used to give deep information on what is shown in a particular picture or video. It is greatly utilized to Facebook just released tool which is describing the objects it identifies on a particular picture. Face recognition is also a result of the computer vision. Modern solutions are already capable of determining the emotions which particular face is expressing. Well, sometimes this is a hard task even for an unbiased human eye. We bet that this field of data analytics is going to be rapidly growing in 2017.
In case you`ve spent the past year in an isolated bubble, you have to know that the next-big-thing in the automotive industry is autonomous driving. There were a lot of advanced cruise control systems capable of following lanes and other vehicles in traffic but Elon Musk and his Tesla vehicles were the first ones that delivered fully autonomous driving, thanks to complicated software backed up with computer vision empowered by multiple cameras, sensors, and radars. Sure the technology is relatively new and a lot more should be done, especially in terms of communication between vehicles for collision prevention. We are going to see this during this year.
Sales forecasting is far from new trend but in 2017 it is going to became more and more popular among small and medium enterprises. It is crucial for businesses which have seasonality in their customer behavior and even more if they are selling products which quickly can go bad as fresh food for instance. It is true that a good retail manager is aware of the tomorrow`s customer demand but only data analytics and sales forecasting are capable of quantifying those expectations with a proper accuracy.
Automation is already big and our team of data scientists predicts that it is going to be even bigger in 2017. Automated solutions are anywhere in the modern industry and their significance is constantly increasing within data analytics. Systems are rapidly evolving in order to enable dynamic learning from streaming content. Automated analytics is a concept for the next stage of analytics maturity. And this will develop further in 2017 because of so many reasons, including faster and lower cost execution.
We already shared dedicated to chat bots blog post powered by Artificial Intellect. Machine learning, natural language processing, and automation give us today the ability to communicate with machines more than effectively. Well, there were some epic fails as the Microsoft`s A.I. twitter chat bot Tay which surprisingly fast went from “humans are cool” mode to full nazi in less than 24 hours. Even though, there is no doubt chat bots has not just huge potential but also are going to be more and more efficient in the tasks they are planted in. Just take a look at Slack! And this trend is going to be developed even further in 2017.
Old school way to storage and access data is to have dedicated machines, to rent capacity or even build an entire data center. Cloud solutions became not just more flexible but also more affordable. In some business cases, they are capable of giving value at lower costs. It also makes possible to benefit from Big Data with a minimum investment. This way companies are not forced to lock their data analytics applications to existing equipment, they can be plugged here and there and last but not least – it doesn’t force businesses to create their own data processing teams. That’s why cloud-based systems backed with expertise are going to flourish in 2017.
Linking Data from Different Sources
Mixing data sources is inevitable in modern data analytics. It might be a challenging task regarding data preparation but undoubtedly, it worth it. Credit scoring, for instance, became much more effective by linking data from social media accounts and bank records. So if the analytics algorithm detects that you and a significant amount of your friends are into a healthy lifestyle, combined with trustworthy credit past and reasonable cash flow in the bank accounts, it will give you a good credit score. If the data analytics problem is to forecast sales it will link data from past sales, existing products, weather forecast for the particular location, the lack of or the presence of holidays of any kind, and looking for correlation between all of them – its performance is effectively based on linked data coming from different sources.
New Data Points
Data analysts are going to utilize a lot more data delivered by unused sources. Wearables were a big hit for the past couple of years and they have to keep an eye on what would be the next. Drones, cars, video surveillance, just name it. For sure, the new data points are going to be relevant to the life which is going to be more and more digitalized and packed with a lot of sensors.