Drowning in data and not a drop of information: Making sense of Big Data

When it comes to managing data, small financial institutions like regional banks and credit unions don’t lack the resources of large banks, they are often drowning in data.

For many, the challenge of using data is knowing where to start, said Anne Legg, author of Big Data/Big Climb: A Credit Union Playbook for Ledging Data and Talent.

“What credit unions really face is, what do I need to know? So the first point is that they have a lot of systems to rely on, and that’s causing them a whole kind of paralysis — like, ‘I don’t know where to start,’” Legg said. Banking Automation News, a sister publication of Auto Finance News. “They shouldn’t be judging themselves against a ginormous fintech or anyone. It’s your own journey and the most important thing 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 subject in Fort Worth, Texas CUNA Southwest School of Management. The best place to start is to map your customer’s journey and identify the pain points, she said. There are four types of problems members turn to the credit union to solve, Legg noted:

  1. The problem of transport, which comes under car loans;
  2. The housing problem, which boils down to a mortgage or home equity line of credit (HELOC);
  3. The travel problem, which is probably a credit card; and
  4. The problem of rainy days, which can be alleviated by short-term and long-term deposits.

“With a credit union trying to figure out where to start, pick one and then start thinking about the frictions the member has in solving that problem,” Legg said. “Start reducing friction in this overall engagement process, and then you’re on the right track.”

Go beyond the “childhood stage” of analysis

Typically, financial institutions belong to one of three levels of data maturity, she said:

  1. Descriptive analysis, where the data essentially looks back at what has been done;
  2. Predictive analytics, which includes financial forecasting 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 identifies them. “How can I say now, ‘Here’s your car loan,’ before you even know your car is going to crash?” she says.

Typically, before organizations can perform prescriptive analytics, the data is cleansed, 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 analysis, where they are “rocking” Microsoft Excel, which is ideal for descriptive analysis. But to move forward, credit unions will need to adopt more advanced tools that can pull multiple data sources and help visualize data, such as Microsoft’s Power BI and Salesforce’s Tableau.

Once banks and credit unions can move to prescriptive data, automations like marketing automation messages becomes a possibility, she added.

But first, it’s important that smaller FIs avoid the trap of thinking they don’t have the technology or the talent to support Data analysisshe says.

“The most important thing: start with your strategy before you start with people,” Legg said. “But if you don’t start, you can never move forward.”

Editor’s Note: A version of this article first appearance in Banking Automation Newsa sister publication of Auto finance news.

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