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How should a CRO think about data cleanup scope for a scaled org: is it one-time project ownership (RevOps, Finance, IT) or permanent sales operations responsibility, and what does the org design look like after month three?

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How should a CRO think about data cleanup scope for a scaled org: is it one-time project ownership (RevOps, Finance, IT) or permanent sales operations responsibility, and what does the org design look like after month three? — Knowledge Library (Pulse RevOps)
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Quick take Data cleanup is not a one-time project; it's an ongoing operational discipline critical for predictable revenue. Ownership for the initial cleanup project typically falls to RevOps, collaborating with IT and Finance, but the *permanent* responsibility for data quality and governance rests squarely within RevOps.

This ensures your sales motions are built on a foundation of accurate, actionable intelligence.

The detail

Treating data cleanup as a one-off project is a fundamental error that will cost you pipeline, forecast accuracy, and rep productivity. Data decays at an alarming rate—typically 20-30% annually for contact data and 10-15% for company data. New prospects enter your market, companies merge, people change roles, and your own reps introduce errors or duplicates.

This isn't a bug; it's a feature of dynamic business environments.

Why It's an Ongoing Operational Discipline

  1. Data Decay is Inevitable: As mentioned, contact data can degrade by 20-30% per year, and company data by 10-15% [Source: HubSpot]. If you don't have continuous processes, your CRM will be obsolete within 3-5 years, crippling your GTM efforts.
  2. New Data Ingestion: Every lead form, every sales activity, every marketing campaign, every integration with a new tool (e.g., ZoomInfo, Apollo, G2, your ERP) introduces new data, which needs to conform to your standards.
  3. Evolving Business Needs: Your ideal customer profile (ICP) shifts, your product lines expand, your sales territories change. Your data schema and definitions must adapt.
  4. Cost of Bad Data: The average company loses 12% of its revenue
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