The Metadata Problem Nobody Wants to Admit Is a People Problem

Every few years, an organization buys a new data catalog. The old one became a graveyard of half-filled descriptions and owners who left in 2022, so surely a better tool, with automation and a nicer interface, will finally get the metadata right. Eighteen months later, the new catalog is also a graveyard. The vendor changed. The outcome didn’t.

At some point it’s worth naming why, and the answer is uncomfortable because it isn’t technical. Metadata initiatives fail for the same reason gym memberships fail: not because the equipment is wrong, but because nobody sustains the behavior the equipment assumes. The metadata problem is a people problem, and the industry keeps buying software to avoid admitting it.

The Number That Should End the Argument

Gartner has predicted that 80% of data and analytics governance initiatives will fail through 2027, and the reason it gives is not inadequate tooling. It’s the absence of a real business driver, and governance programs that don’t enable prioritized business outcomes. Metadata management is the load-bearing wall of governance, and it fails in the same way, for the same reason: the work has no owner whose success depends on it.

DATAVERSITY blogger Stanyslas Matayo’s analysis of why governance fails put a sharp name to the pattern, calling it “governance theater”: committees that identify problems but can’t solve them, with stewardship responsibilities left ambiguous about who produces, validates, and consolidates. A catalog full of stale entries is that same theater with a search bar bolted on. The tool made the emptiness browsable. It did nothing about the emptiness.

The Tool Vendors Already Conceded the Point

Here’s the quiet admission hiding in plain sight. The industry’s biggest recent push is toward “active metadata,” which automatically updates whenever an important aspect of the data changes, in explicit contrast to passive metadata that depends on manual definition, curation, and documentation and is “prone to human error.”

Read that framing as what it is: a confession. The entire value proposition of active metadata is that manual curation always decays, because humans won’t reliably maintain documentation for data they’ll never personally query again. The vendors know the people problem is real. They’ve built a product category around routing around it. Automation genuinely helps, and technical lineage should absolutely be captured by machines. But automation can capture that a column exists, was populated at 3 a.m., and descends from these three tables. It cannot supply what the column meansto the business, whether it’s trustworthy, or who to call when it’s wrong. That’s the metadata that actually decides whether anyone trusts the catalog, and it’s exactly the metadata that requires a human who is accountable for it.

Follow the Incentives, Find the Failure

If you want to predict whether a metadata program will succeed, don’t audit the tool. Audit the incentives.

Ask a simple question of your own organization: Who is rewarded for documenting a dataset well? In most companies, nobody. The data engineer who built the pipeline is measured on shipping the next one, not on annotating the last. The analyst who understands what a metric really means has no incentive to write it down, and a mild disincentive, since their fluency is a form of job security. The business owner who could define the term authoritatively considers the catalog someone else’s software. Curation is a tax paid by volunteers, and volunteer taxes go uncollected.

That is the whole mechanism. A catalog degrades to shelfware not because the ingestion connectors failed but because maintaining it is nobody’s actual job, measured nowhere, rewarded never. You can install the most sophisticated metadata platform on the market on top of that incentive structure and you will get a more expensive graveyard. The technology was never the variable.

AI Just Sent the Invoice

For years, organizations got away with deferring this, because bad metadata mostly hurt at the margins: a slow analyst, a duplicated report, a dashboard nobody quite trusted. In 2026 the bill came due, because AI can’t operate on tribal knowledge.

An LLM or an autonomous agent pointed at your data has no colleague to ask what “active customer” really means, no institutional memory of which of the four revenue tables is the real one. It needs the semantic layer, the definitions, the ownership, the trust signals, that humans skipped documenting for a decade. This is why governance debt is suddenly urgent in a way it wasn’t when only humans read the data: The AI ambition every executive now has runs directly into the metadata every organization deferred. The people problem became a machine-blocking problem, and machine-blocking problems get budget.

What Actually Fixes It

The fix is not a better catalog UI, and pretending otherwise is how organizations end up on their third graveyard. It’s a redesign of who owns the work and why.

Assign ownership to the business domains that create the data, through data product owners accountable for their products’ metadata the way an engineer is accountable for uptime. Embed curation in the workflow that produces the data, so defining a field is part of shipping it rather than a documentation sprint that never comes. And, the part everyone flinches at, tie stewardship to how people are actually evaluated, because a responsibility that appears in no one’s objectives is a responsibility that exists in no one’s calendar.

If I had to reduce it to a single test, it’s this: Name the person whose performance review gets worse when a critical dataset is undocumented. If you can’t, no tool will save you, and if you can, you may not need a new one. The metadata problem was always a people problem. The sooner an organization admits that out loud, the sooner it can stop buying software to avoid the conversation.