Nobody Owns the Field

I once sat in a quarterly business review where three VPs spent 40 minutes arguing about revenue numbers. They were all looking at the same CRM. The problem was that "Industry" meant something different to each of their teams, and nobody had noticed until the board asked a question nobody could answer.

The question that breaks every meeting

Pick a field in your CRM. Any field. "Industry." "Lead Source." "Close Reason." "ARR."

Now ask: who is responsible for this field being accurate?

If the answer is "everyone" or "the rep" or "it should auto-populate," the field is broken. You just do not know it yet.

What happens without ownership

When nobody owns a field, predictable things happen:

Reps fill it in to pass validation, not to be accurate. If "Industry" is required on a deal record, reps pick whatever is fastest. "Other." "Technology." Whatever clears the screen. The field populates. The data is garbage.

Different teams define it differently. Marketing calls it "vertical." Sales calls it "industry." Finance calls it "segment." They all map to the same CRM field, but each team interprets the values differently. The board report says 40% of revenue comes from "Technology" but nobody agrees on what "Technology" includes.

Nobody cleans it. Dirty data compounds. Every month that passes without someone reviewing and correcting the values, the field drifts further from reality. By the time someone notices, it takes a cleanup project just to establish a baseline.

Automation breaks. Lead routing rules that depend on "Industry" route leads to the wrong team. Reporting dashboards that segment by "Lead Source" show meaningless distributions. Forecasting models that weight by "Close Reason" train on noise.

The fix is boring but essential

For every field that matters, assign three things:

An owner. One person who is accountable for the field's accuracy. Not "the data team." A name. This person defines the valid values, sets the validation rules, and reviews the data quarterly.

A definition. Written, not assumed. What does this field mean? What are the valid values? When should it be populated? What should a rep enter when they are unsure? This goes in a shared glossary, not in someone's head.

A quality check. A monthly or quarterly review where someone looks at the actual data and flags anomalies. 80% "Other" on a picklist field means the options are wrong. 30% blank on a required field means the validation is not working. These are signals, not stats.

Why this matters more than it looks

Every decision your revenue team makes is downstream of field data. Forecast accuracy depends on stage definitions being consistent. Pipeline coverage depends on deal values being real. Territory planning depends on industry segmentation being meaningful.

When the fields are wrong, everything built on top of them is wrong. And the people making decisions never see the root cause because they are looking at dashboards, not field-level data.

The diagnostic sprint starts here. Not with the dashboard. With the fields.