Best Practices: Analyzing Analytics
'Big data' may be hot, but 'small data' may be the right path for now.
The financial services industry is abuzz with “big data,” in large part because vendors continue to promote it. Should credit unions be paying attention to the buzz or sitting back and waiting for tangible results?
That’s a good question because the cost to pursue a big data strategy can be high and the value is not completely proven.
Big data, according to today’s definitions, is data so massive that traditional systems and infrastructure can’t effectively handle it. The goal of big data is to provide significantly more information about members’ habits and transactions than has ever been available before.
But big data requires more data storage space, more processing power and more overall technology capacity than most credit unions have. It typically requires expensive analytic software that is beyond the financial reach of many credit unions.
Gaining the most value requires dedicated data gurus who are experts (translation: expensive) in seeing the spending patterns, potential fraud and sales triggers in the masses of data and extracting them for someone to take action.
If a credit union does find a way to support investing in big data, realizing the benefit requires acting on the information, which can place additional—and sometimes unrealistic—loads on the CU’s already burdened staff.
Some credit unions—albeit mostly larger ones—have invested in big data strategies with high expectations for success. They are engaging various systems integrators or even core processors, sometimes cloud computing and other resources, to implement big data without burdening their infrastructures. They have overcome the real security concerns of having member data in the cloud and working with third parties on these undertakings. Notably, these implementations are too recent to claim victory.
Small Steps Toward Big Data
Most credit unions have not yet mastered data warehouses, business analytics, database marketing or other “small data” challenges, so biting off big data would be a stretch for them. Small data is the information CUs probably already have in their core systems, card transactions and other data sources. So much can be accomplished from small data initiatives. And small data initiatives can be seen as stepping stones toward the big data goal.
Here are a few considerations for credit unions interested in making incremental big data investments.
Has the credit union mastered “small data” yet? If not, work on that first. For most credit unions, small data offers attainable, tangible value with systems that may already be in place. By starting with a reduced scope, a credit union can learn what it has capacity for in data analytics and retail prospecting. It can discover the wisdom that lies in its existing data and identify any existing gaps. Many of these gaps can be filled without a big data strategy.
Can the credit union afford big data? Not only is big data expensive, but it could be disruptive to a technology environment that is fragile or outdated. It may require new and costly software, subscriptions and vendors to fully build it out. Credit unions should carefully weigh the cost of big data. It may not yet be worth the investment.
What about the risk of big data? If a credit union can justify the expense of big data, it shouldn’t overlook the associated risk. Many big data implementations at credit unions involve integration and hosting services from outsourced core providers or cloud computing providers. This means third parties will host, process and transmit more data about members, which represents a cybersecurity risk that is not lost on regulators.
What will the credit union do with the result? Big data reveals opportunities among the massive amounts of member and transaction data. Lenders and retail sales forces can equip themselves with more intelligence than they’ve ever had. But what will they do with it?
Branch blitzes, calling campaigns, mailing campaigns, incentive promotions, door to door sales? Without some program to maximize the value of this new wisdom, it will go stale in a matter of weeks—perhaps even days—negating the value of the investment.
Big data requires a significant investment of dollars and commitment. Wise credit unions will carefully weigh the pros and cons before taking on this hot new initiative.
Jim Trautwein is a senior director with Cornerstone Advisors, a CUES Supplier member and strategic provider based in Scottsdale, Ariz.