23 Predictions About The Future Of Big Data

23 Predictions About The Future Of Big Data

23 Predictions About The Future Of Big Data
In the past, I have published on the value of information, big data, advanced analytics and the Abate Information Triangle and have recently been asked to give my humble opinion on the future of Big Data.

I have been fortunate to have been on three panels recently at industry conferences which discussed this very question with such industry thought leaders as: Bill Franks (CTO, Teradata), Louis DiModugno (CDAO, AXA US), Zhongcai Zhang, (CAO, NY Community Bank), Dewey Murdick, (CAO, Department Of Homeland Security), Dr. Pamela Bonifay Peele (CAO, UPMC Insurance Services), Dr. Len Usvyat (VP Integrated Care Analytics, FMCNA), Jeffrey Bohn (Chief Science Officer, State Street), Kenneth Viciana (Business Analytics Leader, Equifax) and others.

Each brought their unique perspective to the challenges of Big Data and their insights into their “premonitions” as to the future of the field. I would like to surmise their thoughts adding in color to the discussion.

If you haven’t had the opportunity, I believe that a recent article published by Bernard Marr entitled: 17 Predictions About Big Data was a great start (original version posted here). Many of the industry thought leaders that I mentioned above had hit on these points.

Read Also:
Big Data investment is up, for how long?

I agree with all of Bernard’s listing but I believe that he missed some predictions that the industry has called out. I would like to add the following:

18. Data Governance and Stewardship around Master Data and Reference Data is rapidly becoming the key area where focus is required as data volumes and in turn insights grow.

19. Data Visualization is the key to understanding the overwhelming V’s of Big Data (IBM data scientists break big data into four dimensions: volume, variety, velocity and veracity) and in turn the advanced analytics and is an area where much progress is being made with new toolsets.

20. Data Fabrics will become the key delivery mechanism to the enterprise by providing a “single source of the truth” with regard to the right data source. Today the enterprise is full of “spreadmarts” where people get their “trusted information” and this will have to change.

21. More than one human sensory input source (multiple screens, 3D, sound, etc.) is required to truly capture the information that is being conveyed by big data today. The human mind has so many ways to compare information sources that it requires more feeds today in order to find correlations and find clusters of knowledge.

Read Also:
How to make big data more manageable

22. Empowerment of business partners is the key to getting information into the hands of decision makers and self-service cleansed and governed data sources and visualization toolsets (such as provided by Tableau, ClickView, etc.) will become the norm of delivery. We have to provide a “single source of the truth” and eliminate the pervasive sharing of information from untrusted sources.

23. Considering Moore’s Law (our computing power is increasing rapidly) and the technologies to look thought vast quantities of data is improving with each passing year, our analytical capabilities and in turn insights are starting to grow exponentially andwill soon change organizations to become more data driven and less “business instinct” driven.

IBM uses a slide that discussed the myriad of challenges when facing Big Data – it is mostly self explanatory and hits many of the points that were mentioned in Bernard’s article. The Four V’s Of Big Data.

What this infographic exemplifies is that there is a barrage of data coming at businesses today and this has changed the information landscape for good.  No longer are enterprises (or even small businesses for that matter) living with mostly internal data, the shift has happened where data is now primarily coming from external sources and at a pace that would make any organizations head spin.

Read Also:
How is Big Data Changing the World?

Today, external data sources (SFDC, POS, Market-share, Consumer demographics, psychographics, Census data, CDC, Bureau of labor, etc.

Read Full Story…

 

Leave a Reply

Your email address will not be published. Required fields are marked *