Credit Union Data Analytics, Three Factors for Success

Success with credit union data analytics

The credit union industry looks very different now than it did twenty years ago. Now, just think about what credit unions will look like in twenty years. Where does the journey for the next twenty years start? Twenty years ago, it would have been hard to imagine remote deposit capture, peer-to-peer payments, or even mobile banking. It is equally hard to imagine what banking will look like twenty years from now. However, one thing is sure: the trend of digital transformation will continue. For many credit unions, credit union data analytics will play a significant role in that trend.

Credit union data analytics programs aren’t always necessary. However, credit unions that leverage their data remain better-positioned to provide individualized member experiences, stay in compliance, or identify attributing members—and that’s just the tip of the iceberg. It all comes down to the basic idea that knowledge is power. And data provides that knowledge. As credit unions continue to consolidate and disappear, those strongest come out ahead and leveraging a data analytics platform often creates competitive advantages. Here are some fundamental success factors.

1. Hiring The Right Analytics People

How many people your credit union data analytics program needs depends on the resources available to your credit union. Typically, larger credit unions can commit more personnel. The most important person is someone from the management team – every project needs an internal champion. They own the process and serve as a driving force, keeping everything moving and on track.

Supporting the manager are the technical staff. These are the IT professionals, data developers, architects, subject matter experts, and report developers who work with the data. A good credit union data analytics team also requires input from business users. These are the team members who identify the credit union’s needs. Generally, the credit union’s data analytics solutions are created for (and with input from) business users.

2. Having The Right Analytics Process


There are four necessary steps in any successful credit union data analytics journey. Here’s what they look like:

Strategic planning

This stage is all about ideas. Therefore, you should ask yourself: What issues does the credit union want to address? What does the credit union’s data analytics team need, and from who do they need it?

Analytics platform implementation

This stage is about assembly. Assemble your team. Assemble your infrastructure. Make sure you have all the hardware, software, and key players in place. Make sure that you have access to the data you need!

Analytics adoption and penetration

This stage is about continuing momentum. Once you’ve assembled your platform, your team integrates it into their activities. Analytics penetration is about getting actionable data to your business users. Essentially, this step is about follow-through—analytics is a process, not a goal! You should use your credit union’s data analytics capabilities once you have them.

Control measures and management

Finally, you’ll want to know that your analytics are doing what they’re supposed to. This stage is about metrics: can you measure the impact? Can you calculate ROI? Does your solution work?

No credit union data analytics program will succeed without these four basic processes in place. Therefore, without a plan and a solid foundation, you’re not likely to get results.

3. Acquiring The Right Analytics Tools

The last factor for a successful credit union data analytics program is the right tools. It doesn’t matter if you’re set up on-premise, hybrid or in the cloud—you’ll need capable hardware and supporting systems. Similarly, you’ll also rely heavily on software. How do you store your data? How do you move, transfer, and integrate that data? Finally, how do you report that data? You’ll need robust data analytics tools to maintain your data’s safety, quality, accessibility, and motility—otherwise, you’ll have a real tough time putting together graphical representations of that data.

Putting It All Together


More analytics options exist now than ever before. Although many credit unions haven’t yet fully adopted analytics, the entry barriers are becoming increasingly manageable and affordable. If you’re unsure where to start, check out this article about how to begin your credit union’s data analytics journey. You can also look for consultants or vendors who can help—many have years of experience helping credit unions with their programs. From implementing data warehousing to providing analytics and reporting applications, support is there for those that contact us.

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You now have more information at hand about your credit union than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.

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