One Small Step for Data, One Giant Leap for Credit Unions
By Blesson Abraham, Director, AdvantEdge Analytics, CUNA Mutual Group
Optimizing data results in improved performance, opens new business opportunities, and reduces costs
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.”
Some may be surprised to learn that data analytics is nothing new! It’s built on many long-standing technologies. Components, such as machine learning, have been available since the 1950s, artificial intelligence has been around since the 1980s, and deep learning has existed for a decade. What is currently driving the rush to data analytics is the plummeting costs of data storage, down from $15/GB in 2010 to $.03/GB today. The inexpensiveness has compelled even large banks to increase the number of customer data points they store.
Five years ago, only 200-300 data points were being stored, while that number has grown to 4,000-5,000 data points per customer, Majumder shared. Banks now are spending approximately 20% of their budgets in analytics. Their investment is resulting in performance improvements, new business opportunities, or reduced costs in other areas.
When early stages of your credit union’s data mining are done correctly, we can move toward more automated reporting, allowing for employee self-service of actionable data. Shifting from data clean-up to automated data analytics to predictive analytics will not make money or customers rain from the sky. That relies upon your credit union’s execution on the insights provided.
This is the first in a three-part series of the what, why and how of data analytics for credit unions.
CUNA Mutual Group is the NAFCU Services Preferred Partner for Analytics, TruStage® Auto & Home and Life Insurance Products. More information and educational materials are available at nafcu.org/CUNAMutualGroup