Editor’s note: This is the first of three articles by Linda Straub Jones and Jeff Jones on the use of big data by credit unions.
There are many opinions and much ambiguity surrounding credit unions’ need for data, including how to use it and the best sources for acquiring that data. Some experts suggest that for smaller institutions, going down a big data path is taking on too big a challenge, whether it be because of cost, staffing or expertise. Further, they suggest that credit unions can achieve the same results using “little data,” or smaller data sets which are generally more readily accessible and easier to analyze. They focus on narrower data sets and ideas, and can be more targeted and manageable, all for a smaller investment.
Further, some commenting on this subject have suggested that gaining access to and using big data could be an overwhelming burden for small- and medium-sized institutions, including acquiring analytical tools and reporting software that mine the data, onboarding staff to do the analysis and hiring consultants to train them.
This may make some sense if the only use case for this argument is to market additional products to existing members, but for most credit union operational and marketing objectives, we’d respectfully disagree. Although many CUs may not realize it, they are actually already using big data. For example, if they are using credit bureau skip trace software for debt recovery or any database of public record data, what is this if not big data?
There have been a few examples where credit unions were able to use smaller data sets to inform successful marketing campaigns for far smaller an investment than a big data project would require. That’s fair, as far as it goes. But there are a vast number of use cases where credit unions would not only benefit, but would realize a significant increase in ROI or even substantially reduce their compliance burden. We’ll get to those use cases in a follow-up article, but first, it would perhaps behoove us to define big data and clear up any misinformation about the nature of it.
A big decade for big data
Thanks to technological advances, storing massive quantities of data has become far more economically feasible within the last decade. Big data can simply be defined as any large accumulation of data that needs specific management, database or processing tools because of its size and complexity.
Today, external providers can make big data available to companies in order to analyze trends, develop new products and more, and at a far lower cost than what would be required if an organization was to attempt to store and manage that information in-house.
Big data offers possibilities beyond what “little data” can provide. For example, it can help assess creditworthiness, providing lines of credit and a means of entry into the financial industry for creditworthy small businesses, almost half of which are either “thin file” or “no file.” These are companies that would, under traditional credit-scoring practices, be denied loans by traditional financial institutions. Financial inclusion along these lines not only helps those businesses but also helps boost economic activity while reducing risk to the lending institutions.
Analyzing big data can also help collections departments prioritize receivables management, for example, before they invest in debt-recovery lawsuits. In this case, big data can provide much more accurate guidelines around debtors’ assets and avoid the cost of initiating a lawsuit in instances where a debtor has no assets.
Moreover, use of small data and big data are not mutually exclusive. Analysts can trace a path from one to the other, such as using big data to identify trends and then pinpointing nuances with more targeted analysis, or finding a small trend and then extrapolating out using big data which, because of its larger representative sample, can provide a more accurate picture as outliers are not as statistically significant.
There is a way for credit unions to easily and cost-effectively collect, analyze and apply big data. By combining internal member data with external data from an analytics company that already has vast amounts of information, CUs can avoid the start-up costs associated with integrating a new division into the business and collecting data themselves.
Big changes, but necessary
We don’t intend to minimize what it would take for credit unions to use big data. Even with an external data provider, the institution would still need to restructure operations to make sure there are employees who can analyze, interpret and, most importantly, apply this new information in a relevant and timely manner. While this is a big change, it is a necessary one if a business is to continue to prosper — consumers expect an experience that reflects their desires and needs, and if one credit union doesn’t take advantage of the insights big data provides regarding those desires and needs, another surely will.
Big data has many uses cases and benefits, and should not be passed off lightly. While there is a both a financial and a cultural commitment that comes with using big data, some of the structural costs can be avoided by partnering with an external provider, and ultimately, big data will produce big returns on the investment.
It’s true that your own in-house data is a valuable resource and may provide the information needed for very specific business purposes. However, it is important to realize that it may not be in your or your members’ best interests to rely on that data as your only source.
