New KlariVis Report: When It Comes to Utilizing Data, Credit Unions Outshine Banks

By W.B. King

After surveying 124 executives from banks and credit unions to measure their “Data IQ,” KlariVis, in association with Cornerstone Advisors, assessed how well banks and credit unions are using data (data execution quality, or Data EQ) in four functional areas—strategic planning, sales and marketing, credit analysis, and operational delivery.

“Community banks and credit unions stand to benefit significantly from artificial intelligence (AI)—but only when their data infrastructure is up to the task,” the report, entitled Improving Your Financial Institution’s Data Execution Quality (EQ), noted.

Ron Shevlin, report author and chief research officer at Cornerstone, also cited Cornerstone’s 2025 What’s Going on in Banking study. “In 2025, 35% of community-based financial institutions (FIs) had already deployed chatbots, 26% had implemented machine learning, and 25% were using generative AI tools. Looking to 2025, 21% planned to deploy chatbots, 20% expected to introduce machine learning models, and 28% said they would be using generative AI, all for the first time.”

What’s the Score?

Seven in 10 survey respondents were either senior vice presidents or executive vice presidents or had “chief” in their titles. Half of the respondents were from institutions with $1 billion to $5 billion in assets, 11% were from FIs with $5 billion to $10 billion in assets, and 29% were from FIs with $250 million to $1 billion in assets, Shevlin explained.

The total average Data EQ score (out of a possible 500) was 241. According to Shevlin, the larger the FI, the higher the Data EQ score. And while he didn’t find that surprising, there is one important outlier.

“FIs in the $500 million to $1 billion asset range didn’t score as highly as FIs below $500 million in assets. Our explanation for this is organizational complexity: FIs in the sub-$500 million asset range typically deal with a smaller set of products and a more limited geographical footprint than FIs in the $500 million to $1 billion asset range,” he continued. “As FIs grow from the sub-$500 million level to more than $500 million, the organizational processes relating to Data EQ don’t keep pace. They don't scale effectively to match the new scope of the organization.”

Pack Leaders

In the areas of credit analysis, data access and analysis, operational delivery, strategic planning, and sales and marketing, high performers distinguish themselves in cultural and procedural (or “non-technical”) in the following ways:

• Information is considered a strategic asset. Nearly three-quarters of high performers consider information a strategic asset, in contrast to 30% of middle of the pack institutions, and just 3% of low performers.

• Data is a key driver of strategic decision-making. More than half of high performers said data is a key driver of strategic decision-making, in contrast to 21% of middle of the pack institutions, and none of the low performers.

• A culture is fostered around data usage. Almost half of the high performers said their organizations foster a data usage culture, in contrast to 9% of middle of the pack institutions, and none of the low performers.

• Data strategy is reviewed regularly. Four in 10 high performers regularly review data strategy, in contrast to 21% of middle of the pack institutions, and none of the low performers.

  
  

“Everybody wants to be an ‘A’ student and get a perfect grade. In the case of Data EQ, a perfect score of 500 is virtually impossible, and not even practical. A financial institution can’t be ‘optimized and strategic’ on all 50 capabilities,” Shevlin said, noting the total category list in the survey. “Instead, a score of 400 is the top score an institution can reasonably expect to achieve.”

He further explained that platitudes like “break down data silos” and “define data governance policies” won’t help an FI achieve an “A” grade. FIs, he added, that are serious about improving their Data EQ will need to make some difficult decisions and choices.

“Across the 50 capabilities assessed, business user access to analytics tools received the highest rating—23% of executives rated their organizations at the ‘optimized and strategic’ level. Also scoring highly were data governance and stewardship policies, which many executives believe are established and operational (or better),” the report noted. “At the other end of the spectrum, data quality and integrity monitoring is a shortcoming, as is the integration of structured and unstructured data.”

The Personal Touch Still Wins Out

When surveyed on the five noted categories—credit analysis, data access and analysis, operational delivery, strategic planning, and sales and marketing—credit unions outranked banks 271 to 211.

“Splitting the survey sample by type of financial institutions reveals that credit unions far outscore banks in every functional area,” the report noted. “One possible reason for this: Compared to credit unions, community banks are often more focused on the commercial versus the retail side of the business, and they rely more on personal relationships than on data-driven processes.”