Turning Customer Data into a Banking Goldmine with Big Data

Yesterday I sat in on a Big Thinkers in Big Data tweet chat hosted by Intel and Forrester’s Mike Gualtieri. With our Big Data in Financial Services webinar just around the corner I wanted to catch the latest on what others were saying about the topic and its relevancy in banking.  As a marketer it is imperative I stay up-to-date on the latest news around financial services technology.  With Big Data it’s hard not to bump into daily news on the latest trends and ongoing skepticism. I’ve seen a lot of articles with similar titles like, “Big Data, Big Ruse”, “The Big Cost of Big Data”, and “Big Data: Revolution or Overhyped Fad”. These combative titles have made it difficult for banking CIOs and CMOs to build a business case for adapting strategies to include Big Data solutions as part of their data analytics initiative.

One of the first questions during the Big Thinkers chat was, “what is the biggest roadblock for Big Data?” Gualtiere hit the nail on the head with his response. “The biggest roadblock is denial. Many firms think it is just a buzzword.” The fact of the matter is – Big Data is real. Sure organizations may have been doing traditional data analytics for years, but as we’re learning with the proliferation of mobile devices and social media, the sheer volume and velocity of data in banking is making it very hard for the industry to ignore. Not to mention we’re seeing banks like ING Direct and BNY Mellon looking to capitalize on the opportunity to turn customer and unstructured data into valuable insight.

Big Data for Banking Innovation

While I won’t share any easy “how to’s” for undertaking Big Data projects (I’ll save those for our webinar), I will share some benefits for the financial services industry and some staggering statistics on why Big Data is such a big deal for banking innovation.

The graphic below from an InformationWeek study shows a number of relevant sources where banks can find value in data.


Now what can banks do these “undiscovered” and exisitng data sources? Here are some value propositions and use cases for Big Data in banking:

  • Gain market intelligence for a competitive advantage
  • Understand customer behavior and buying patterns
  • Help reduce customer churn
  • Run more targeted location-based offers and campaigns
  • Effectively implement loyalty and rewards programs
  • Improve customer service and feedback
  • Develop new revenue streams and service channels
  • Build new technology and measure customer sentiment

Lastly, I’ll leave you with some startling statistics on why the time is now for Big Data.

The use of big data has improved business performances by an average of 26% and the impact will grow to 41% over the next few years. (Capgemini)

73% of financial services management decisions are based on hard analytic information. (Capgemini)

Big data will drive $232 billion in spending by 2016. (Gartner)

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Original Post: http://blogs.perficient.com/financialservices/2012/11/14/turning-customer-data-into-a-banking-goldmine-with-big-data/?utm_source=rss&utm_medium=rss&utm_campaign=turning-customer-data-into-a-banking-goldmine-with-big-data


  1. Good article Elizabeth. It’s apparent companies need to face the challenges that arose with the era of big data, especially in the area of getting value from that data. HPCC Systems released its open source Machine Learning and Matrix processing algorithms to assist data scientists and developers with business intelligence and predictive analytics in big data related problems. Their integration with R and Pentaho extends further capabilities providing a complete solution for data ingestion, processing and delivery. http://hpccsystems.com

  2. Well written article capturing the essence and need to Big data…It is high time Banks and Financial institutions recognize the need for Big data… it is not something that one unit or a part of the Organisation will use rather it is an investment in the right direction. Till today most Banks and FI use data in an offline or batch mode. But what we really need are streams of real-time data for analysis…These areas include credit ratings for loans and mortgages whereby Credit appraisals for thousands of customers can be done in a few minutes.

    Today is the time for real time processing rather than offline positioning and most banks have moved into the Social Media and started using it effectively. This vitiates the needs for analysis on real-time basis for the demo and physiographic aspects of the customers to analyse them and position the right product.

    In other words big data is like a Precision Bombing rather than the regular carpet bombing that is done by bank, thereby reducing costs and increasing profitability

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