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Up Close and Personal - Banking on Big Data

Up Close and Personal - Banking on Big Data

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Many people ask the question “where is banking going next?” Facing a down market, legacy issues and a growing volume of regulation the traditional business is under threat. Plus a new generation of alternative providers – particularly in payments – is eating away at the core franchise.

What’s to be done?

Bank’s today are sitting on a ton of data, much of it about you and me. This data is incredibly valuable. But years of acquisitions have led to a patchwork of systems and technology solutions that don’t sit well together. Plus many database systems are designed around standard predictable questions we always needed answers to not the new questions that are driving our business more and more today.

The result is data is hard to access let alone analyze. Even though it is valuable, it’s really hard to get.

Plus even more data is coming down the pike – and unstructured data at that which bank systems are ill equipped to handle after years of investing in structured, relationship based systems. Plus what is data today? Is it words, numbers, images, voices, gestures, presence?

Classifying the data is a great place to start. There is high-value data we need to run the business now and lower value data we may need in the future (or may not – we just don’t know yet). Data we need now should be close to the line of business. Not far away in some remote  central repository. We have more flexibility over lower value data. As long as we can get it when we need it.

This suggests a federated approach to data management. A centralized, single database model assumes all data is equal in value which, of course, it isn’t.

Once we have our data story in place, the technology and security story we need to support it falls more easily into place. Tools like Hadoop, Hive, SQL, analytics and the Cloud each have a clearer role to play and data will flow more easily to where it’s most needed.

And what’s the prize for getting this right?

Many banks have had to cut back and grow smaller since the financial crisis of 2008. One exception is Great Western Bank that has grown 300% since then mainly by harnessing data Big Data tools to understand their customers better and service them individually.(1)