Big data just keeps getting bigger, impacting virtually every industry – from filmmaking to finance – on a global level. It’s already a revolutionary concept, and some technology experts predict that in just five more years, global data production will be 44 times what it was in 2009. Still, for all its massive potential, financial institutions have a big problem with big data – it’s decidedly hard for them to make sense of.
That’s true, it seems, whether you’re a mega-bank managing billions in assets or a small credit union with a few hundred members. Lest you think that’s overstating the matter, consider research by Capco (reported in the Financial Brand) that found that while 62% of surveyed banks believed big data was vital to their success, only 29% said they were currently getting sufficient value from their data. That’s a big gap to account for!
From my conversations I’ve had, I think that percentage is even lower in credit unions.
There’s no doubt that effectively mining big data could be as much a boon for credit unions as it is for some larger financial institutions. Insightful analysis of big data from both internal and external sources could help credit unions increase the number of transactions by current members, reduce fraud risks, identify and optimize cross-selling opportunities, recognize prospects with the highest probability of becoming members, and craft and implement relevant marketing campaigns to turn those prospects into loyal members.
Sounds great, right? So how can credit unions use big data to their advantage? Four key factors come into play. Credit unions must:
- Understand what big data is.
- Draw on internal and external data sources
- Employ effective analytics tools
- Take small steps
Defining ‘big data’
To qualify as “big data,” information must meet the “three Vs” criteria – volume, variety and velocity.
Volume clearly refers to the amount of data now produced – an incredible amount that’s going to continue increasing exponentially for the foreseeable future. The variety of data encompasses internal and external sources, information about consumer behaviors, social media activity, etc. The velocity – near or real-time assimilation – is the final defining component of big data.
Credit unions may be unaware of the sheer volume of data they already have in their proprietary information systems, from core information to account activity, loan portfolios and more. Even those that have been actively analyzing internal data may lag in incorporating external sources that are equally valuable in gaining a bigger picture of customer behaviors.
To effectively make use of big data, credit unions need to gather and analyze data from external sources including social media, the Web, geo-locational sources and more.
It’s probably best to get some help as you learn how to harness this data and use it correctly to target both your prospects and members. There are great services out there to help you target the right opportunities for things like auto loans, small business loans, and mortgages.
Effective analytic tools
I’ve said it before and it bears repeating here – vast quantities of information are useless if a financial institution is unable to effectively mine the insights hidden within the data. It’s essential for credit unions to wrap their arms around the data and find optimum ways to slice, dice and segment the information they have at their disposal.
Long gone are the days when only mega-banks could afford to do this. Analytics software, deployed in conjunction with a personal financial management platform, can help credit unions glean useful insights from big data and use those insights to generate actionable conclusions.
I am not alone in thinking this. Recently I was flying home from Deluxe Exchange 2015 and read a very telling quote from Mark Sievewright, president of credit union solutions at Fiserv:
“I really believe we’re going to see a profound change in the way credit unions do business, and technology will enable it. The next 10 years are going to make the past 10 years look trivial.”
Moving forward with small steps
For credit unions, jumping into big data with both feet may result in getting sucked under by the sheer volume of data. A better approach may be to dip a toe in first to test the water. First, drill down on exactly what your financial institution needs to accomplish through use of big data. Perhaps your focus is on optimizing the profitability of existing members, or maybe increasing membership is a priority. Start with a single objective and then consider the ways in which big data can support your movement toward that goal.
When your goal is clear, examine the data sources that could contribute relevant information toward that objective. Refine your data set to encompass only the relevant information. You need to learn to walk before you can run; add complexity to your data analysis only after you’ve mastered the fundamentals.
Finally, your big data is only as good as your analytics. Employ performance management software that integrates important metrics into an easy-to-use system that updates continuously. Such tools will provide managers with the relevant information they need to act in a timely manner and create effective initiatives.