Credit unions possess many data points on their members’ habits, but they run on disparate systems making it difficult to manage and glean much insight. However, recent storage cost reductions are making analysis of this data easier than ever.
The average credit union has 20 to 40 systems it operates on, and data analytics is the methodology used to pull the data from these divergent systems together to provide a 360-degree view of the credit union member. This more comprehensive and accurate view, can provide credit union professionals with information to create actionable plans to improve a member’s engagement and retention.
While clean (accurate) data is important, do not wait until it is 100% to begin to perform analyses. It is highly unlikely single entity will ever have 100% clean data due to changing data sources, systems migrations and other matters affecting it.
The process of executing on insights gained from data analytics can start small, like cleaning up your member email database, according to my colleague, Ani Majumder, a McKinsey & Co. partner in the financial services division. He explained to a group of credit union professionals attending a NAFCU event, “A lot of the value is often around the lower buckets, about getting your basic data rate, getting your basic reporting right and making the right business decisions.”
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