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Teamwork might make the dream work, but it’s data that drives collaborative effectiveness across credit unions. The rapid advancement of analytical tools for benchmarking, market tracing, next best product predictions, and more has deepened the need for forging real connections across the cooperative to make the most of artificial intelligence.
As member-owned collaboratives, credit unions are an ideal setting to bring together individual people and data points to accomplish more for the majority. Here, managers in key business intelligence roles at six cooperatives share how they build and sustain those connections.
GROW FINANCIAL FCU
Daniel Hirschlein has been with GROW Financial Federal Credit Union ($3.3B, Tampa, FL) since 2008, including the past seven years as assistant vice president of enterprise analytics.
How do you forge productive working relationships with the end-users of your business intelligence?
Daniel Hirschlein: The end-users of our business intelligence — our data consumers — need data that supports decision-making in their line of business. The best way to forge productive working relationships is to collaborate with them as partners, understand their pain points, and look for ways to align the tactical nature of their data use to overall strategic goals. We take the approach of “teaching our data consumers to fish,” meaning we will supply the tools and provide a level of training to enable them to access the data themselves, helping to empower them to be even better subject matter experts in their areas.
What is the most effective internal partnership you have with other departments, and why?
DH: We recently staffed our credit union to support three areas of our data strategy: infrastructure, architecture, and consumption. Infrastructure is supported by our database administrator, whose responsibility is to prepare, aggregate, and store the data from all the credit union’s data source catalog in addition to supporting our operational database systems.
Architecture is supported by our data architect, who designs and loads the enterprise data warehouse using the prepped data from the infrastructure area.
Lastly, consumption is supported by the enterprise analytics team along with the data power users within each line of business. Our responsibility is to not only surface the data within the enterprise data warehouse in reporting and dashboards but also provide a feedback loop to the architecture so it continues to support the data use cases of the business.
What project is the biggest accomplishment for BI work at your credit union? What impact did it have on the credit union’s ability to improve member service?
DH: Our analytics strategy has been to gain a better understanding of how Grow Financial operates, with the goal of achieving predictable financial performance. Developing analytics and insights around our lending portfolio has allowed us to understand lifetime financial performance of each product. Looking at our lending practices to understand the number of applications we receive, our approval rate, and the funding rate combined with the ongoing performance and disposition of the asset has allowed us to provide more competitive rates to our members and has allowed us to take on additional risk.