Getting Out from Behind The Curve
Chapter 1 of this series considered the importance in establishing a specific goal to solve using data analytics and proving the ROI in order to justify automation and decisioning using business intelligence in a credit union. Chapter 1 also highlighted two real use cases of success credit unions have had using data analytics to solve real-world problems. According to a recent study conducted by Best Innovation Group (BIG) and OnApproach (now Trellance), 45 percent of credit unions don’t currently have a strategy in place, and those that do have a strategy still say it will take three to five years to implement. Credit unions that aren’t making the most of data analytics today could be in even bigger trouble if an economic downturn occurs, as some economists are forecasting. “As we go forward there will be a significant performance difference between those that have invested and those that have not,” says Kirk Kordeleski, senior managing partner at BIG. “We think any downturn in the economy will highlight the advantage that data-oriented FIs will have over their competitors.”
How Much Will It Cost
The survey revealed that more than half of the 85 credit unions surveyed have budgets in place for data analytics. Of those, one-third plan to spend more than $200,000, the other two-thirds plan to spend between $50k to $200k. In addition, credit unions need to consider on-going costs. A rough rule of thumb is that a CU with $500 million in assets should budget between $150,000 and $300,000 per year for three years to cover software/hardware, analytic applications, and strategy. Smaller credit unions can find some savings by relying on a CUSO to provide the analytics and associated services.
The following paragraphs are real use cases that credit unions have shown to prove out their investments.