What Good Drilling Data Governance Actually Looks Like

Everyone says they want better data governance. Few can describe what it actually looks like in practice.

In most drilling operations, data governance gets treated like a compliance exercise — something you do because someone in IT asked for it, or because a vendor mentioned it during a software pitch. The result is a policy document that nobody reads, a data dictionary that's six months out of date, and three different definitions of "spud date" floating around the organization.

Good data governance looks nothing like that. Here's what it actually looks like when it's working.

1. Everyone Knows What the Data Means

The most basic sign of functional data governance is definitional alignment. When your drilling superintendent says "flat time," your data engineer's system captures the same thing. When a geologist references "formation top," it maps to the same field in every database.

This sounds obvious. It isn't. On most multi-well programs, the same data point has between two and five competing definitions depending on which department created it, which software captured it, and when. Good governance resolves this — not with a committee that argues about it for six months, but with a working data dictionary that's maintained, versioned, and enforced at the point of entry.

2. Data Has a Clear Owner

Good governance doesn't mean centralized control of data. It means clear accountability. Every critical dataset has someone responsible for its accuracy, completeness, and timeliness — and that person has the authority to enforce standards.

In practice, this looks like a drilling engineer who owns the daily drilling report data, with defined sign-off procedures and correction workflows. It looks like a data steward who can flag anomalies, escalate issues, and update records without submitting a help desk ticket. Ownership without authority isn't governance — it's just blame assignment.

. Problems Are Caught at the Source

The difference between mature and immature data governance shows up most clearly in where problems are discovered. In immature environments, bad data is found weeks later — when an engineer pulls a report, builds an AFE comparison, or tries to reconcile actuals against plan. By then, the data is hard to fix and the decisions it affected can't be undone.

Good governance catches problems upstream. Validation rules at the point of data entry. Automated QC checks that flag outliers before they're committed. Alert workflows that notify the right person when a value falls outside expected range. The goal is to make bad data visible immediately — not after it's already been used.

4. Data Governance Is Embedded in Workflows, Not Separate from Them

The reason most governance initiatives fail is that they're designed as a separate process layered on top of existing work. Engineers are asked to fill out governance forms in addition to doing their jobs. Data quality reviews happen in separate meetings. Compliance is checked after the fact.

Good governance is invisible. It's built into the systems people already use — the EDR, the WITSML feed, the daily reporting tool. The governance controls are part of the workflow, not alongside it. When the drilling engineer closes out their shift report, the governance happens automatically. They don't even know they're participating in a data governance program.

5. Historical Data Is Trustworthy

One of the clearest signals of good data governance is how your team talks about historical data. In operations with poor governance, engineers qualify every reference to historical wells with disclaimers: "I think this is right," "the data from that campaign is a bit suspect," "we had a different system back then so I'm not sure."

In operations with good governance, historical data is used without apology. Engineers cite offset well performance with confidence. Benchmarking is reliable. Lessons learned are actually learned, because the data that underpins them is accurate and complete. That confidence compounds over time — every well you drill well-governed adds to an asset that makes your next well better.

here to Start

If your operation doesn't have functioning data governance yet, don't try to build a framework. Start smaller:

Pick your five most critical data types — the ones that show up in every AFE, every post-well review, every performance conversation. Write down what each one means. Get three people from different departments to agree on the definition. That's your first governance act.

Then assign an owner. One person per data type. Not a team — a person. Give them visibility into data quality and the authority to fix it.

Then identify where your worst data quality problems are occurring. Find the process step where bad data enters the system and put a check there.

That's not a governance program. But it's what governance actually looks like when it starts working.

WillCo helps drilling operations design and implement practical data governance frameworks — ones that fit existing workflows, don't require a team of data scientists, and actually get used. If you're ready to move beyond the policy document, we should talk.