IBM Thinks Big on Data Unification
December 7, 2016
So far, the big data phenomenon has underwhelmed. We have developed several good ways to collect, store, and analyze data. However, those several ways have resulted in separate, individually developed systems that do not play well together. IBM hopes to fix that, we learn from “IBM Announces a Universal Platform for Data Science” at Forbes. They call the project the Data Science Experience. Writer Greg Satell explains:
Consider a typical retail enterprise, which has separate operations for purchasing, point-of-sale, inventory, marketing and other functions. All of these are continually generating and storing data as they interact with the real world in real time. Ideally, these systems would be tightly integrated, so that data generated in one area could influence decisions in another.
The reality, unfortunately, is that things rarely work together so seamlessly. Each of these systems stores information differently, which makes it very difficult to get full value from data. To understand how, for example, a marketing campaign is affecting traffic on the web site and in the stores, you often need to pull it out of separate systems and load it into excel sheets.
That, essentially, has been what’s been holding data science back. We have the tools to analyze mountains of data and derive amazing insights in real time. New advanced cognitive systems, like Watson, can then take that data, learn from it and help guide our actions. But for all that to work, the information has to be accessible.”
The article acknowledges that progress that has been made in this area, citing the open-source Hadoop and its OS, Spark, for their ability to tap into clusters of data around the world and analyze that data as a single set. Incompatible systems, however, still vex many organizations.
The article closes with an interesting observation—that many business people’s mindsets are stuck in the past. Planning far ahead is considered prudent, as is taking ample time to make any big decision. Technology has moved past that, though, and now such caution can render the basis for any decision obsolete as soon as it is made. As Satell puts it, we need “a more Bayesian approach to strategy, where we don’t expect to predict things and be right, but rather allow data streams to help us become less wrong over time.” Can the humans adapt to this way of thinking? It is reassuring to have a plan; I suspect only the most adaptable among us will feel comfortable flying by the seat of our pants.
Cynthia Murrell, December 7, 2016
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