Data isn’t a technical detail, it’s the foundation. A CRM relying on inaccurate, incomplete, or outdated data becomes a system employees actively avoid.
And this isn’t just an internal CRM concern. As highlighted in McKinsey’s 2025 technology trends report, organizations are entering an era where AI, automation, and real-time decision-making will depend on reliable, structured, and scalable data.
Their analysis shows how rising compute needs, agentic AI, and cross-system integrations make data quality the determining factor in whether digital transformation succeeds or stalls. If your CRM is going to support tomorrow’s workflows, your data needs to meet tomorrow’s standards, not yesterday’s legacy habits.
The essential question isn’t just “can we trust our data?” but “should we?”
Legacy data often reflects years of inconsistent entry, unstructured notes, missing fields, and conflicting formats. If this isn’t addressed before migration, user adoption collapses quickly.
A CRM should also generate new information you don’t have today: interaction histories, follow-up tracking, conversion outcomes, issue-resolution paths. But if no one owns data quality, if verification rules aren’t established, and if training is inconsistent, the system becomes unreliable.
Once trust is broken, workarounds appear instantly: personal spreadsheets, sticky notes, shadow systems… all early warning signals of CRM abandonment. Here is a formula that never fails:
- Clean, structured data → high trust → high adoption
- Dirty, inconsistent data → staff distrust → workarounds → CRM abandonment
