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The 5 V’s of Big Data: Value

The last few weeks have certainly flown by. It’s hard to believe I’m up to the fifth and final “V” of big data: value.

To recap, in the previous four weeks I’ve covered the other four big challenges (and some solutions to those challenges) of big data: volume, variety, velocity and veracity. And for all the tactical considerations that come with these challenges, deriving value from big data is often the most difficult of the five V’s. After all, having thousands of lines of data is pointless unless you use them for something purposeful, such as measuring marketing efficacy or improving compliance programs.

However you choose to process big data, each application requires its own analysis and personnel to implement, manage and mine it for actionable items. With analysis of big data still being a new field, it can be hard to find talent — and harder still to develop an analytics program that truly hits at the heart of what your organization is trying to do.

Some typical uses that financial institutions have found for big data include:

Leveraging big data to create profiles of account holder spending habits on the internet. With this knowledge, banks and credit unions can create targeted credit card marketing campaigns to reach this customer segment.

Banks and credit unions have used big data to assess the risk of consumers applying for credit — rendering a credit decision in minutes versus days.

Big data has helped financial institutions increase revenue through targeted cross-selling and promotions. Instead of hoping a mortgage promotion reaches someone who happens to be in the market for a home, banks and credit unions can know based on web and financial activity. In the old days, vast amounts of data dissipated throughout the enterprise, siloed into separate systems, servers, apps, etc., preventing it from being used across the enterprise. It was impossible, for example, for the mortgage department to use data that was hidden inside the savings and loan department. Using big data architecture to combine this data into one repository, where algorithms can be used to identify potential sales, has unearthed many cross-sell and other revenue-increasing opportunities.

Big data gives visibility on spending habits and online inquiries, which financial institutions are using to improve the customer experience and deepen account holder relationships.

Let’s also not underestimate the impact big data has on preventing fraud, ensuring regulatory compliance, and internal operations as well. If you’ve ever used your credit card in another city or state and instantly received an email, text or call from your financial institution, then you’ve experienced big data in action.

Fraud protection, as we all know, is a very big deal. It’s a $9 billion industry that affects more than 30 million Americans each year. Luckily, today’s technology can analyze hundreds of transactions, emails and purchases, and grade them for potential risk. This allows auditors and fraud detection departments to do their jobs faster and more accurately. As technology continues to evolve, we’re reaching the point of predicting fraud before it can occur.

It’s no secret that regulatory compliance is a big burden on financial institutions. Unfortunately, the smaller the bank or credit union, the bigger this burden can be. The power of big data, if correctly applied, can help financial institutions reduce regulatory compliance risks and avoid potential problems in real-time. The key ways it can do this are through building new compliance reports and performing regulatory stress tests. By aggregating data that is typically scattered across systems, servers, apps, lines of business, etc., it can be updated and sourced more quickly and easily. Just in regulatory compliance alone, a business case can be made for updating IT infrastructure to facilitate big data aggregation, analysis and use. (I explained some of these technologies in the Velocity and Variety blog posts.)

I hope you’ve found this blog series on big data as useful and enjoyable as I have in writing it. If you have questions about how your financial institution can harness big data to impact business results, Harland Clarke can help.

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