This piece is based off of a roundtable discussion at the CU 2.0 Brainstorm Event in July 2021. It is not intended to be comprehensive—rather, it will provide a quick look at emerging trends in data analytics for credit unions.
An increasing number of credit unions are working with data analytics. And it makes sense:
Data is just another way to know your members.
But as more solutions hit the market and the cost of entry falls, new trends are showing up. See what other credit unions and vendors are working with as they dive deeper into the world of data.
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1. Credit Unions Trying to Do Too Much
As credit unions develop new solutions and work with new fintechs and vendors, they increase the amount of data in their ecosystem. And with more data comes the desire to do more things with it.
The biggest challenge for credit unions is organizing all their data before working with it.
But they shouldn’t do that, says Anne Legg, Founder of THRIVE Strategic Services. Instead, she advises credit unions to prioritize member needs over data organization. “focus on the most important thing… the member.”
Find out where your members experience friction. Then, find the data that relates to that friction. Work with that data and don’t worry about organizing the rest.
2. Composable Data
Composable data ties together data from various sources. It allows for low-code and no-code solutions to work and is generally user-friendly.
COVID-19 brought big technology, behavior, and strategy changes. There’s a good chance that data analytics for credit unions will be fundamentally different going forward. Composable data allows for those changes—and the changes that are sure to come from AI and other disruptive trends.
3. Better Knowledge About the Community
The best thing about credit unions is that they serve communities. And the best way to understand the community you serve is through their data. Data is starting to help credit unions with:
- Seeing where to put (or remove) branches
- Broad community trends (e.g. demographics or industry changes)
- Tracking shifts in member needs
For example, let’s say a credit union serves a SEG. What happens if a huge number of layoffs occur? How do credit unions retain those members and support them through their transition?
Credit unions already have a lot of information about individual members. Learning more about the community is the next big step.
4. Data Powers Vendor Solutions
Customer Relationship Management (CRM) platforms are gaining traction with credit unions. Data is fueling the effectiveness of CRMs, allowing credit unions to gain deeper insights about members. It also pushes better content workflows and nurturing sequences in marketing efforts.
Additionally, data also powers AI products, which are increasingly prevalent in credit union ecosystems. There’s an AI version of almost every tech product out there. And if something doesn’t have AI yet, it probably will soon.
Data will increase the efficiency and accuracy of AI. And, because AI helps to scale, data will be key to the success of new solutions.
5. Warehouses Preferred
There are two general strategies when it comes to data analytics for credit unions:
With a warehouse or without.
As more credit unions get comfortable with data—and as it gets easier to work with—credit unions are leaning towards solutions that include warehousing over those that don’t. The most “holistic” approach to data provides more options for the future.
More About Data Analytics for Credit Unions
CU 2.0 produced a credit union data analytics vendor guide to help with evaluation. And we’re working on a new one shortly, which you will be able to find with our other 2.0 Guides here.
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