The four dimensions worth scoring
| Dimension | Question it answers | Example check |
|---|---|---|
| Completeness | Are the required fields populated? | Share of open opportunities carrying an amount, stage, and close date |
| Uniqueness | Is each real company stored once? | Duplicate account rate on normalized email domain |
| Consistency | Is the same fact recorded the same way everywhere? | Industry values matching the picklist instead of free text |
| Recency | Has the record been touched recently enough to trust? | Share of open pipeline with no field change in 90 days |
Consistency beats perfection
Most revenue teams believe their data is uniquely bad and that this is the reason they cannot run the business the way they want. ORM's position is that everyone has bad data and it matters less than people assume. Garbage in does not have to mean garbage out. As long as the data is wrong in a consistent way, a model can learn the pattern and still predict accurately.
That reframes the whole exercise. A stage every rep skips is learnable. A stage half the team skips and half the team uses correctly is not. Chasing full completeness on every field is expensive and rarely moves a number. Chasing consistency on the small set of fields that drive sales forecasting moves it immediately.
Score the fields that carry revenue weight
ORM counts meaningful activity on an opportunity as a change in stage, close date, or amount. Those three fields decide which period revenue lands in and how much of it is counted, so they belong at the top of any weighting scheme. Everything else is administrative.
Recency deserves its own line in the score because it degrades quietly. ORM data shows that more than 10% of a typical customer's pipeline has not been touched in 12 months. Those records still appear in totals, still inflate pipeline coverage, and still get reported to a board as though they were live.
Reading the score honestly
Publish the score with its components visible, never as a lone headline number. A composite that rose because five thousand contacts got a phone number appended, while open pipeline aged another month, is worse data wearing a better score. Tie each component to the decision it protects, and treat a falling recency component as the early warning it is. Data quality only earns attention when someone can trace it to forecast accuracy, and that trace has to run through specific fields rather than an average of everything.
Frequently Asked Questions
What goes into a CRM data quality score?
Four dimensions cover almost every practical version of the score. Completeness measures whether required fields are populated, uniqueness measures whether each real company or person is stored once, consistency measures whether the same fact is recorded the same way across records, and recency measures whether the record has been touched recently enough to be believed. Weight each dimension by how much your reporting actually depends on it.
What is a good CRM data quality score?
There is no cross-industry standard to hit, and a borrowed target would be meaningless because every team scores a different field set. Baseline your own score, then judge the trend. A score that moves from 61 to 74 over two quarters is useful information. A score of 74 with no history behind it is a number on a slide.
How often should the score be recalculated?
Monthly is enough for the trend and matches the pace at which contact and account data decays. Recalculate weekly only for the fields tied to the current quarter's pipeline, since those are the ones a forecast reads before anyone has time to fix them.
Does a higher data quality score produce a better forecast?
Only if the score weights the fields the forecast reads. Amount, stage, close date, and owner drive the model. Perfect completeness on fields nothing consumes raises the score without changing a single prediction, which is how hygiene programs end up looking successful while forecast accuracy stays flat.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like crm data quality score into prescriptive action for your team.
Schedule a Demo