As we have successfully made it past the midpoint of 2015, the year is shaping up to offer several new enhancements in big data and analytics. Much of the improvements that come in the BI community comes in the form of either simpler, faster or more — and this improvement trickles down to businesses of all sizes across the globe.
This is why it’s exciting to see what new trends will emerge, what traditional strategies will be flipped on its head and who will be the pioneers in implementing these new improvements. Here, we’ll break down four major changes that are soon coming our way in the BI community and how to implement these strategies within your system to benefit your growing business.
Datafication
Datafication is what you get when you’re able to display, track and analyze individual processes with your technology. While this isn’t necessarily revolutionary in terms of business intelligence, datafication is picking up steam with more real-time capabilities being used and valued.
A perfect example of this technology is Fitbit, the wireless, wearable activity tracker that “measures data such as the number of steps walked, quality of sleep and other personal metrics.”
It’s datafication that makes this information visible all of a sudden via a device that’s made to specifically designed to display it. This data goes a long way in tracking movements and monitoring health concerns for those trying to lose weight, stay fit or simply desire to monitor their personal health.
And this is just the base level. Datafication opens the doors for businesses to optimize tracking. The use of sensors, real-time technology and a full-fledged analytical display can serve as a tool that businesses can use to monitor their own processes with ease.
Community
Long gone are the days were one person would control all the analytics capabilities. Instead, we’ve evolved to the point where IT spending has become more of a collaborative effort where each analyst’s input should be valued and respected as there can be several paths to a solution.
Analytic organizations are coming to a point where community is in charge, not the individual. With scattered sources, educational backgrounds and data perspectives, this means data teams will need to implement internal social networks where collaboration is the more effective method of data analysis, as opposed to disparate ideas taking longer to come together.
Analytics Ecosystems
One of the first steps in establishing a comprehensive big data infrastructure entails using the varying inter-dependencies of your company and synchronizing them. Viewing your company’s BI solutions as an ecosystem goes a long way in collaborative efforts to make use of the big data that you collect.
Take it from IBM Data Magazine:
“A successful big data analytics program requires many interacting elements … data, which has to be integrated from many sources, different types of analysis and skills to generate insights and active stakeholders who need to collaborate effectively to act on insights generated.”
As a “network of interconnected and interdependent entities,” having a well-defined ecosystem helps to alleviate the path of big data while making it useful for every element of the system. There are three interacting spheres that define the analytic ecosystem including the core, extended and external.
Data Privacy
The subject of data privacy, while evolving, is still trailing the evolution of our technology. With the necessary involvement of governments and politicians, it’s likely that the laws that need to be stringent will become so far after they’re initially needed.
The private sector also has a responsibility in maintaining transparency with their customers and partners as to where their information is being stored and if it’s being distributed to a third-party.
As more conversations are had surrounding the subject, the coming years should offer more solutions that protect consumers while holding companies and governments accountable for their actions.
