MeridianLink’s solutions help financial institutions “deliver faster, smarter, and more personalized account opening and lending experiences.” A key contributor to this effort is Chris Eldredge, the company’s Vice President of Data, whose career spans more than three decades in analytics, warehousing, strategy, and governance.

In a recent video interview with Spencer Hogan, Business Development Manager at Afidence, Eldredge discussed the traits that distinguish mature data organizations from those still developing — from breaking down silos and fostering data communities to establishing common definitions that keep teams aligned.

Building a data community

Moving ahead, Eldredge describes the concept of a data community as an essential structure for fostering collaboration and shared understanding within an organization.

“Data flows throughout an entire organization, and a data community is a virtual organization of people who have an affinity for data or who primarily work in data.”

According to Eldredge, such a community can include a variety of roles:

• Data analysts who work directly with information assets
• Key leaders with specific, high-value needs for data

The core idea, he explains, is about uniting these individuals: “It’s bringing people across organizational boundaries together to talk about, align, and agree on data so that we can build that whole dialogue across the organization and people can talk intelligently about data, what it means, and how they should use it.”

Breaking down data silos and aligning definitions

Speaking of data silos, Eldredge states that he has seen firsthand how companies struggle when data is trapped in departmental silos.

“In a lot of companies, data happens in silos, and you have individual departments, maybe of sales, marketing, and finance, and each area has specific needs in terms of how it wants to measure success.”

He explains that, in many organizations, departments apply different interpretations to shared concepts, which the data world calls data domains, such as customers, products, or employees.

When this happens without cross-departmental communication, Eldredge warns, the result is often misaligned definitions.

For example, he says, finance might measure the number of customers one way. Sales may use a different calculation, and product teams could have an entirely separate definition.

This disconnect leads to inconsistent reporting and confusion. Eldredge points out that these issues often develop naturally over time, especially in long-standing organizations.

To address these challenges, Eldredge emphasizes documentation and transparency: “The best way to help people understand how to get around these challenges is to just simply document and show them what these definitions in each area are, what things are conflicting, and what definitions could change.”

He notes that change can happen in several ways:

• Agreeing on a single company-wide standard
• Having one department adjust its definition
• Renaming a metric, calculation, or attribute to differentiate it clearly

Ultimately, setting standards for terms like “customer” is critical. This includes defining customer attributes, establishing customer hierarchies, and agreeing on how many customers an organization has.

Eldredge underscores that this matters because “a lot of times those foundational pieces are the denominator in a lot of metrics.” For instance, he says, when calculating market penetration, one would divide the number of people using a particular product by the total number of customers. If different departments such as finance and marketing use varying customer counts, the resulting figures will not align.

Signs of an immature data organization and the need for common definitions

One of the clearest indicators of an immature data organization is the inability to provide standardized definitions for key metrics, says Eldredge. He notes that when data requests lead to a chain of referrals, rather than a single authoritative source, it often means that gatekeeping is at play.

“The reason why is because there are people who have made careers out of being the gatekeeper for numbers in an area, and to your point, when they have something to lose, something to defend in their minds, it becomes a challenge.”

To overcome this, Eldredge emphasizes the need to show them the benefits of adopting common definitions. Key points from his perspective are

• Common definitions make it far easier to share data across the organization.
• Differentiation can still be important for specific use cases, but it should not prevent alignment.
• Irrelevance risk—if data cannot be shared in a common format, it loses organizational value.

He uses a relatable example to highlight the issue. Taking the definition of a week, for example, Eldredge points out that definitions can shift based on operational context. In a call center with hours from 8:00 AM Eastern to 5:00 PM Pacific, Monday through Friday, a “week” may differ from the typical calendar week.

This creates the need for terminology clarity—for example, labeling it a service week or customer support week. This distinction becomes critical when comparing with other areas, such as finance, which might use a strict calendar week.

“So you really have to think about how we use this data. How do we let it interconnect? And which pieces of data are relevant?”

Wrapping up, Eldredge recalls encountering challenges during his time at Paycor, where questions arose not only about counting 1099 workers but also interns, temporary staff, partners, and consultants working under statements of work. He notes that rehires add another layer of complexity.

Such variations, Eldredge concludes, can significantly impact reporting, making it essential to plan ahead and clearly define the rules, both from a business and technical standpoint, while keeping them aligned.

CDO Magazine appreciates Chris Eldredge for sharing his insights with our global community.