Collecting data to streamline operations and enhance member engagement is an all-important goal, but big data builds up and can dump too much disparate information — supersaturating an otherwise well-intended process.
"When it comes to data management and analytics, the order in which you introduce new tools is extremely important," said Austin Wentzlaff, director of Business Development for the Plymouth, MN-based OnApproach. "Data management may be the most difficult step in the process of becoming an analytically-driven credit union."
Among OnApproach's 45 CU clients is the $550 million Lewis McChord, Wash-based America Credit Union, which signed on to its data integration and analytics solution, M360 Enterprise, in mid-June.
"We reviewed vendors who provided industry specific solutions. We also evaluated a solution offered by our core system vendor," said Gary Schminkey, who referenced its Symitar Epysis core processor. "We chose the solution due to its flexibility in storing the data from our core system as well as other data related to financial, lending, marketing and other sources we may need or acquire in the future," said Schminkey.
Mining the Data Dump
Operating as a CUSO, OnApproach's M360 Enterprise, which was patented in early June, was designed to work with any credit union regardless of size or system configuration. And while data visualization tools, such as Tableau, can be affective, Wentzlaff said these types of approach don't always solve an organization's analytics problems. To this end, a data warehouse or data model is required that supports the following four foundational blocks: reporting, forecasting, predictive modeling and optimization.
"Without a solid data infrastructure, analytics is incredibly difficult or near impossible. The data model is the middleware that drives all of the analytics," said Wentzlaff. "Although it may not always be visible to the end users, it is definitely the most important part of data analytics — just as an engine on a car is the most important part of driving the car."
The ways in which data can be harvested and used are varied. One of OnApproach's CU clients, for example, was able to enhance its VIP Member Rewards Program, which increased member satisfaction and VIP member base profitability by more than 16%.
Another client used the solution to price loans accurately and in-step with economic market conditions. Wentzlaff explained that this CU realized that FICO scores had the potential to over price loans and used predictive analytics to price loans more competitively.
"This allowed the credit union to significantly increase its net interest margin while factoring in for risk which allowed the credit union to more accurately predict its allowance for loan loss," he said.
While new to the M360 Enterprise solution, Schminkey is confident the solution will improve America CU's ability to not only capture data, but use it in meaningful, forward-leaning ways.
"We anticipate the methods, complexity and flexibility of retrieving information from OnApproach to be a welcome change from our current system environment, which requires a comprehensive understanding of rules and coding to extract data," said Schminkey.
