Over the past few years, there has been a lot of discussion, and disagreement, as to the exact definition of “Big Data”; a term most often used to loosely describe large and/or complex data sets that cannot adequately be addressed by traditional data management solutions.

Gartner tried to simplify the definition: “Big data” as high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” (Although it must be mentioned that the exact origin of the “3 V” concept has also been debated). Regardless of the ongoing debate, a simple Google search of the term “What is Big Data” quickly demonstrates the ambiguity on the subject.

For banks looking to take advantage of Big Data, there are several opportunities identified as to where these technologies can improve customer overall satisfaction, reduce operating costs, and increase product revenues.  Just within the past few weeks, Forbes wrote an article describing four ways banks can utilize big data to improve their businesses in 2015, ranging from “creating a customized, consistent customer experience” to “dissolving internal silos to have one view of the customer”. It’s difficult to argue against any of these objectives as they each can have a positive impact on the organization and customers.  As we look a little deeper, it’s also helpful to understand the pitfalls that lurk as an organization tries to take on these objectives.

All too often companies, financial institutions being no different, are promised as to the ease by which results can be achieved with a particular software and/or hardware solution.  Organizations will make the decision that they are going to “Do Big Data”, then look first to the right technology solution to help them achieve their goal.  This quickly turns into sales meetings, product demonstrations, and ultimately purchases of expensive hardware and software packages without first understanding the business problem that these new purchases will be tasked in solving. If Gartner has been accused of over-simplifying the definition of what is Big Data, then Big Data technology vendors can just as easily be accused of over-simplifying the execution necessary to achieve the desired results.

A Sense Corp Financial Services Blog recently addressed the exact issue at hand. Technology is part of a more comprehensive solution vs. the sole solution. With regard to Big Data, one of the most important aspects is an effective data governance program. If we take the second opportunity Forbes outlines: “dissolving internal silos to have one view of the customer”, data governance is absolutely critical to this effort.

For example, as departmental data is merged and transformed to create a single customer view, data governance ensures data is leveraged in a consistent and transparent manner across the organization. Data governance also ensure the necessary business process and change management activities occur, such as data owners being clearly defined and a consistent usage of the new consolidated data. One of the most critical aspects data governance also provides is ensuring strong executive management support exists to champion this effort. Otherwise, a banking institution may find themselves with nothing more than a cluttered, untrusted, and expensive data management tool.

Financial institutions looking to utilize Big Data solutions to advance their organization should look at the opportunity with a critical lens and ensure their path to success is clearly defined. By understanding all the components required to enable Big Data, only then will banks be able to achieve their objective.

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