Data analytics: ‘Look for quick wins’
Data-driven solutions are ‘within your walls.’
Data-driven consumer insights are driving today’s marketplace, led by online stalwarts such as Amazon and Walmart.
But data, and the insights it provides, doesn’t have to be the domain of these monoliths, says Emily Engstrom, director of client success for AdvantEdge Analytics, a CUNA Strategic Services alliance provider.
During the CUNA Financial Council Virtual Conference Conference, she provided insights on using data to gain insight on member trends.
“These large companies think they know your members, but they don’t have access to a fraction of the data and insights about your members that you do,” Engstrom says. “Within your credit union walls, you know where your members are shopping. You know their patterns, their transactions, where they have other financial relationships, and so much more.”
She advises credit unions to develop internal centers of excellence to drive a data-forward culture, focusing on specific needs within each department. “This can allow everyone from the credit union to come up with ideas and pull together a cross-functional team from throughout your organization.”
First, what problem you want to solve for members through the use of data and artificial intelligence, and build a road map from that point.
“Look for some quick wins,” Engstrom says. “It doesn’t have to be 10-year journey with a $1 million budget. Start small and drive from there.”
Next, determine where to find and access member data. That creates the issue of data governance, she says. “Is the data I need organized? Is it consistent? Can I easily consume it to begin analysis?”
After organizing their data, credit unions can begin reporting, including the creation of dashboards to make reporting more user friendly.
“Once you know what data you need, you can begin asking questions from your data,” Engstrom says.
That will enable credit unions to ask why certain trends are taking place and consider “future-state activities around advanced analytics,” she says.
“It’s not only about why something is happening, it’s about where you start getting into machine learning and predictive analytics to consider what will happen next and, most importantly, the next action to take.”