Bad Data: It’s Costing Your Credit Union

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Most credit unions are eager to put their data to work – and when done right, it can definitely work magic. But first, a credit union must work for data.

“Working for data” means more than integrating it into an analytics platform. In order to work its magic, steps must be taken to improve data quality – and there is a very real fundamental reason for this. Research and advisory firm Gartner says poor data quality costs the average business $12.9 million each year. In addition to the immediate revenue impacts, the combined effects of poor data quality ultimately lead to poor data analysis and then poor decision-making based on that analysis.

It pays (literally) to be proactive. With money, time, and ideas at stake, here are the common causes of most credit unions‘ data issues. Are they a factor for your credit union?

5 Common Root Causes of Bad Data in Credit Unions

Bad data happens in every organization. Although this is inevitable, there is still much to be done to improve and maintain data quality. Here are common data saboteurs and how to find the source of the problem.

1. Incomplete or incorrect member or employee data. When entering information, most members – and even employees – don’t think about how the data will be used, or how long it will last. If a member misses a detail on a loan application, for example, by forgetting to enter a postcode, a staff member can enter “11111” to fill in the required field. Although it was not done with a bad intention, it creates a data error that will propagate throughout the system. A single mistake, especially when repeated, becomes an obstacle to data accuracy, segmentation, and targeting.

Credit unions can counter this with a data-driven culture where employees understand how it’s used and why it matters. This results in better data management across the organization. When collecting data directly from members, credit unions can incorporate auto-fill features that make completed forms more accurate and convenient.

2. Poor data in supplier and third party files. Often, credit unions will obtain data from additional sources to enrich their own data. This adds more life and detail to the picture of members and financial trends, but the additional information will only be as good as the underlying data. Other parties may not have the same data quality standards as your credit union.

Investigate opportunities to build clear standards into supplier contracts. It may be possible to fix some issues up front, before the data is integrated into your system.

When extracting data from public sources, such as the US Census or the Federal Reserve, “you get what you get.” It is the responsibility of the caisse to verify and validate the information by implementing commercial data rules. If that seems overwhelming, there are tech tools available to help. You may also decide initially to focus on only a few key data fields, writing additional rules over time that continue to improve the data.

3. Lack of data standardization. I had a situation recently where I was in a service provider’s system twice – once with my initial and once without. Although both of my records contained the same social security number (which should have been a big flag!), the lack of data standardization allowed for the creation of two separate accounts. This led to a lot of confusion when I had an issue that needed to be resolved.

It’s not an experience a credit union wants its members to have. You can help avoid this by creating a cross-functional business team that develops uniform data standards and communicates them to IT. Leaving this task solely to the IT department – ​​or even to one person – can limit perspective on how the data will be used.

4. Formula Errors. If your credit union relies on a formula to generate data, such as a mortgage P&I calculation, always verify and validate the results. In addition to wreaking havoc on the accuracy of a credit union’s financial data and potential decision-making, in this particular example, even a small error can harm the relationship with members.

A QA team may be in charge of testing the calculations of others to ensure that the expected result is generated. If not, the team can investigate the problem, design a solution, and implement best practices that minimize potential future problems.

5. Natural Data Degradation. Data has a lifespan and the older a record is, the more likely it is that the data items have become stale. Over time, members change addresses, financial products they use, names and membership status. No credit union wants to make decisions based on information that is no longer true.

Make sure your members have easy ways to update their personal information when it changes and periodically invite them to confirm that your records are up to date. Set up processes to regularly validate and reconcile data, creating rules that establish the most trusted source in the event of a discrepancy. For example, if a member has submitted a change of address, this should always trump a third-party data source that may still have an old address on file.

The common thread running through all of these problems and solutions is to be proactive. A “people, process and technology” approach to data quality will make it easier to achieve and maintain better data quality, while mitigating the very real costs of bad data. Data quality is a journey, not a destination. Ongoing data quality, combined with other data management practices, will not only impact results today, but will pay dividends throughout the life of your data.

Albert Merrill Albert Merrill

Merrill Albert is director of data service delivery for Tampa, Fla.-based Trellance, a provider of business analytics solutions for credit unions and banks.

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