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Data Quality· Enterprise sales organisation· 2023· ~10 weeks

CRM Data Quality Framework

'The reports are wrong' was always a data problem, never a reporting one. Built the framework to prove it and fix it.

  • Salesforce
  • Power BI
  • Power Query
  • Python (pandas)
  • Duplicate accounts
    −60%
    on the core book of business
  • Key-field completeness
    +28pp
    on industry, segment, country
  • DQI
    built + adopted
    reviewed weekly by the team

Business problem

Every reporting issue the commercial team raised ended up in the same place: duplicates, empty fields, inconsistent picklists. Leadership was losing trust in the numbers but kept aiming at the dashboards.

Data available

  • Salesforce Account, Contact, Opportunity full snapshot
  • Third-party enrichment sample for validation
  • Field usage statistics per team and region

Solution implemented

  1. 1Profiled the CRM with reproducible SQL + pandas, quantified duplicates, completeness and conformity per field, per team.
  2. 2Defined a Data Quality Index (DQI) weighted by the impact each field has on revenue workflows, not by how 'full' it looks.
  3. 3Built dedup and standardisation rules (industry, country, phone) partly automated in Salesforce, partly in a clean-up playbook for sales ops.
  4. 4Published the DQI in Power BI and made it the opening slide of the weekly ops meeting with owners per failing dimension.

Impact

Duplicate accounts
−60%
on the core book of business
Key-field completeness
+28pp
on industry, segment, country
DQI
built + adopted
reviewed weekly by the team

What I learned

The report is never the problem. It is always the field upstream and you only fix it once there is a scoreboard.
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