When it comes to managing data, smaller financial institutions like regional banks and credit unions are not lacking the resources of larger banks, they are often drowning in data.

For many, the challenge in utilizing data lies in knowing where to start, said Anne Legg, author of the book “Big Data/Big Climb: A Credit Union Playbook for Leveraging Data and Talent.”

“What credit unions are really struggling with is: What do I need to know? So ground one is they’ve got a lot of systems to pull from, and that does cause them a whole kind of paralysis — like, ‘I don’t know where to start,’” Legg told Bank Automation News. “They shouldn’t be judging themselves against a ginormous fintech or anybody. It’s your own journey and most important is [to] please start.”

4 challenges

Legg advises credit unions on their data strategy as founder of THRIVE Strategic Services and she teaches on the topic at the Fort Worth, Texas-based Southwest CUNA Management School. The best place to start is by mapping your customers’ journey and identifying friction points, she said. There are four types of problems members turn to credit union to solve, Legg noted:

1. The transportation problem, which falls into auto loans;
2. The shelter problem, which boils down to a mortgage or home equity line of credit (HELOC);
3. The travel problem, which is likely a credit card; and
4. The rainy day problem, which can be alleviated by short- and long-term deposits.

“With a credit union trying to figure out where to start, pick one of those, and then start thinking about the friction that the member has getting that problem resolved,” Legg said. “Start lessening the friction in that overall engagement process and then you’re on your way.”

Moving beyond the ‘childhood stage’ of analytics

Typically, financial institutions fall into one of three tiers with data maturity, she said:

1. Descriptive analytics, where the data is essentially looking backward at what has been done;
2. Predictive analytics, which includes financial forecasts or other data use cases that predict where the credit union or member is going to be; and
3. Prescriptive analytics, or data that can tell the credit union where the member needs to be before the member has identified it. “How do I now say, ‘Here’s your auto loan,’ before you even know that your car was going to fall apart?” she said.

Typically, before organizations can perform prescriptive analytics, the data is cleaned, organized and there is a data strategy, Legg added.

Many regional banks and credit unions are still in what she calls the “childhood stage” of data analytics, where they are “rocking” Microsoft Excel, which is great for descriptive analytics. But to move forward, credit unions will need to embrace more advanced tools that can pull in multiple data sources and help visualize the data, such as Microsoft’s Power BI and Salesforce’s Tableau.

Once banks and credit unions can move forward to prescriptive data, then automations such as automating marketing messages becomes a possibility, she added.

But first, it’s important that smaller FIs avoid the trap of thinking they lack the technology or talent to support data analytics, she said.

“The most important thing: Start with your strategy before you start with the people,” Legg said. “But if you don’t start, you’re never going be able to move forward.”