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Financial Services Focused on Gaining Insights from Internal Data

August 15, 2013

This is our fourth post in a series of seven presenting the findings from the IBM Institute for Business Value and University of Oxford’s Big Data study, “Analytics: the real world use of big data in financial services.”

As part of this recently published global research study, my colleagues David Turner, Michael Schroeck and Rebecca Shockley found that most early big data efforts are targeted at sourcing and analyzing internal data, and this is also true within banking and financial markets companies. According to the study, more than half of the banking and financial markets respondents reported internal data as the primary source of big data within their organizations. This suggests that banking and financial markets companies are taking a pragmatic approach to adopting big data, and also that there is tremendous untapped value still locked away in these internal systems.

More than four out of five banking and financial markets respondents with active big data efforts are analyzing transactions and log data. This is machine-generated data produced to record the details of every operational transaction and automated function performed within the bank’s business or information systems – data that has outgrown the ability to be stored and analyzed by many traditional systems. As a result, in many cases this data has been collected for years, but not analyzed.

Where banks and financial markets firms lag behind their cross-industry peers is in using more varied data types within their big data pilots and implementations. Slightly more than one in five (21 percent) of these firms is analyzing audio data – often produced in abundance in retail banks’ call centers – while slightly more than one in four (27 percent) report analyzing social data (compared to 38 percent and 43 percent, respectively, of their cross-industry peers). Most industry experts attribute this lack of focus on unstructured data to the ongoing struggle to integrate the organizations’ structured data (see figure below).

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In our next blog, we’ll examine the need in big data for strong analytic capabilities and the skills to use them.

To learn more