Data quality management (DQM) is the ongoing discipline of measuring, monitoring, and improving the accuracy, completeness, consistency, and timeliness of data across your business systems. It combines technology, processes, and people to ensure that the data driving your decisions is actually reliable. DQM isn't a one-time project. It's a continuous practice.
Why It Matters
Bad data costs U.S. businesses an estimated $3.1 trillion per year according to IBM. For a typical mid-market B2B company, that translates to misrouted leads, wasted ad spend, inaccurate forecasts, and failed integrations. Teams that treat data quality as someone else's problem end up with a CRM nobody trusts, reports nobody believes, and automation that breaks in production.
The Five Dimensions of Data Quality
- Accuracy: Does the data reflect reality? Is the email address current? Is the job title correct? Accuracy is the most important dimension because everything downstream depends on it
- Completeness: Are required fields filled in? A contact with a name but no email and no phone is incomplete and unusable for outreach
- Consistency: Is data formatted the same way everywhere? If marketing uses 'Software' and sales uses 'SaaS,' your segments won't work
- Timeliness: Is the data current? A record updated 2 years ago has probably decayed. Freshness matters for B2B data
- Uniqueness: Does each entity appear only once? Duplicates inflate counts and create conflicting information
Example
A VP of Sales notices that 30% of leads assigned to the West Coast team are actually East Coast companies. Investigation reveals the 'State' field has no validation rules. Reps type whatever they want. DQM fix: add picklist validation, clean existing data, and monitor for drift. Lead routing accuracy jumps from 70% to 98%.
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