This is the second post in a five-part series based on our ebook, From Chaos to Clarity: The Strategic Guide to Healthcare Data Catalogs. Each post addresses one of the root causes that organizations encounter when data becomes a blocker rather than an asset. Download the full ebook here.
The last post ended with a question: What data is your organization currently unable to find, trust, or explain? If your team produced a long list or couldn’t agree on the answer, the instinct is to fix it comprehensively. Document all systems. Build a governance framework. Catalog everything.
That instinct often leads cataloging initiatives to fail.
When “comprehensive” backfires
If nobody knows what data exists, the solution seems to be documenting everything. It is the right problem, but the wrong plan.
Most health systems have 50 or more source systems, thousands of tables, and hundreds of thousands of fields. Cataloging all of it takes well over a year. During that time, the business sees little usable output. By then, priorities have shifted, the team has turned over, and the catalog becomes a record of effort rather than a tool anyone uses.
Healthcare makes this worse. Clinical coding systems alone, including ICD-10, CPT, SNOMED, and LOINC, can occupy a documentation team for months without addressing a single operational problem. Trying to catalog everything is why the work fails to deliver value.
Skipping cataloging entirely does not work either. Organizations that plan to add governance after building analytics often end up rebuilding when users stop trusting the numbers. This shortcut extends timelines rather than reducing them.
Two questions indicate whether an initiative is off track: are business users actually using the catalog, and when someone asks what it has enabled, do you describe specific decisions or just the number of documented assets?
Ultimately, completeness is not the goal. The goal is to answer a real question because the right data is available.
Start with the decisions, not the data
Organizations that avoid this trap ask a different question first. Not “what data do we have?” but rather “what decisions are we unable to make because we cannot find, trust, or explain the data?” This question narrows the scope and focuses on real business needs.
For most health systems, the answer is a short list. Executive dashboards, CMS quality reports, and cost data are needed for value-based care decisions. Not 50 systems. Usually fewer than ten.
From that list, most organizations can document lineage, ownership, and quality metrics within 90 days. Slack messages asking where data lives decrease. Duplicate work stops. Dashboard numbers begin to align.
Findability is only half the problem
This addresses the inventory question: where data lives, who owns it, and how it flows. However, a deeper issue remains. Do shared terms mean the same thing across teams?
Knowing where readmission data lives does not ensure consistency in how it is calculated. Without documented, agreed-upon business definitions, numbers vary depending on who interprets them.
Why governance structures fail in healthcare
Even with proper scope and definitions, governance often fails for the same reason as cataloging: overengineering.
Data governance councils, policy documents, and role hierarchies may look strong in theory. In practice, they often result in meetings without action.
Healthcare organizations operate at full capacity. Clinicians are best suited to define clinical terms, but they cannot take on additional governance roles. When governance introduces more meetings and workflows, people bypass it.
Instead of asking who owns all clinical data, ask a simpler question: when a metric is wrong, who is responsible for fixing it?
That person owns a specific asset, commits a few hours per month, and acts when needed. This is not a framework. It is accountability. Broader governance can follow once this foundation is in place.
What this looks like in practice
Before building a single dashboard for a West Coast health system, executives were entering board meetings with conflicting answers to the same question. Operating margin varied depending on the system used. The data existed, but it was disconnected.
We started by mapping the key decisions leadership needed to make. Then we identified the supporting data sources and resolved ownership and definition gaps. This included aligning how each system calculated operating margin.
The result worked because the foundation was correct. If we had started by cataloging everything, the effort would still be ongoing.
Where to start
What are the two or three decisions your executive team most needs data to support, and what is preventing progress? Start there. Everything else follows.
The full ebook includes a 15-minute Data Catalog Readiness Assessment and a Data Chaos Cost Calculator you can use in your next leadership meeting. Download From Chaos to Clarity or reach out for a diagnostic conversation.
Next in the Clarity Series: Healthcare AI pilots are stalling before reaching production. The model is rarely the issue.
