Big Data and Debit: Now or Later?



More is Better, Except with Big Data

Retail banks are finding ways to mine big data efficiently for actionable outcomes

Author: Ronald Mazursky
Published on: July 15, 2014

Every introductory conversation about big data focuses on the numbers. Really big numbers. The next conversation goes into the variety of data formats and the speed at which data is being produced in today’s world. Somewhere along the way, value of the data is finally thrown in. But big data doesn’t stop at the “what (is big data),” but must go on to include the “why (are we collecting big data)” and the “so what (are we collecting this data for).”

Big data is being collected today, albeit in limited ways and only at a small number of financial institutions, for the purpose of better understanding the bank’s customers and their individual behaviors. The purpose of this effort is to develop actionable solutions that might not be possible otherwise without this data. The U.S. retail banking industry is in the early stages of big data usage, which is expected to grow quickly in the years ahead.

Mercator Advisory Group’s research note, Big Data and Debit: Now or Later?, answers questions asked by retail bankers and debit issuers wondering whether they can benefit from big data analytics—or their curiosity as to whether it is just a big “money pit.” The note offers definitions and use cases and practical advice on getting started benefiting from the information that analysis of big data can provide.

Big data and debit are a natural combination. Extraordinarily large volumes of data are created and available related to the debit card and its core demand deposit account (DDA). Whether this data is profile, behavioral, or miscellaneous other data, it is accumulating at ever increasing rates (velocity), and the variety of data is increasing. However, the significance of big data derives from more than just these three V’s. To make use of it effectively, it is most important to understand the value of the data—and the actionable outcomes accomplished by using predictive analytics.

This Research Note is 9 pages long.

Companies mentioned in this research note include: CapGemini, Deloitte, FICO, Fiserv, McKinsey & Company, SAS

Members of Mercator Advisory Group’s Debit Advisory Service have access to these reports as well as the upcoming research for the year ahead, presentations, analyst access, and other membership benefits.



Highlights of this Research Note include:

  • Definition of big data and lean big data 
  • Use cases, mostly from large financial institutions 
  • Solutions for small financial institutions and a case study 
  • Tips for getting started 
  • Example of using social media for sentiment analysis



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