Measuring Data Quality

You cannot improve what you do not measure. Data quality has four dimensions: completeness (are all required fields populated?), accuracy (do the values reflect reality?), consistency (do related records agree with each other?), and freshness (when was the data last verified or updated?). Each dimension produces a score, and the composite score is your pipeline's data quality index — the number that tells you how much you can trust your analytics.

Data Quality Index (DQI) — Scorecard Template

Dimension       Weight   Score   Weighted
──────────────  ──────   ─────   ────────
Completeness    30%      82%     24.6
Accuracy        30%      71%     21.3
Consistency     20%      88%     17.6
Freshness       20%      65%     13.0
                                 ────────
Data Quality Index:              76.5 / 100

Interpretation:
  90-100: Analytics-ready. Forecast with confidence.
  75-89:  Usable with caveats. Identify gaps.
  60-74:  Unreliable. Prioritize remediation.
  Below 60: Rebuild. Decisions based on this data are guesses.

Start measuring by running four queries against your CRM: what percentage of open deals have all required fields populated (completeness), what percentage of close dates are in the future (a proxy for accuracy), what percentage of company records have consistent industry and size data across associated deals (consistency), and what percentage of deals have been updated in the last 14 days (freshness). These four numbers are your baseline.