Data quality
Data quality is the measured fitness of records for the decisions made from them, completeness, accuracy, consistency, timeliness and uniqueness, each measured against a stated requirement.
"The data is bad" is not a finding, because it names no dimension and no threshold. A finding is: 31% of open opportunities above $50k have no economic buyer recorded, against a requirement of 100% at stage 3. That version can be assigned, fixed and monitored.
Quality is only meaningful relative to use. A field nobody makes decisions from can be 60% populated without consequence; a field that gates routing must be complete and correct at the moment of routing. Auditing every field to the same standard wastes the effort that the load-bearing fields need.
Where it breaks
A quarterly data-quality initiative cleans every field equally, and the three fields that actually gate routing are stale again within a month.
Related terms
- Revenue data model
- The revenue data model is the set of objects, relationships and required fields that every revenue-facing process reads from and writes to.
- Metric definition
- A metric definition is the governed specification of a measure, its formula, source fields, filters, grain, owner and known limitations.
- Change control
- Change control is the governed process for proposing, reviewing, releasing and recording modifications to the revenue system, its fields, definitions, automations and stage criteria.
- Deduplication
- Deduplication is the ongoing enforcement that one real person or company is represented by exactly one record, across every object that references them.
Field notes on this
- You just inherited RevOps. Start with a charter.
Being told to "own revenue operations" is not a mandate. Until authority is written down, every decision you make is reversible by whoever objects loudest.
- Why four teams report four different revenue numbers
A reconciliation problem may be definitional, technical, or both. Test the metric contract before buying another data platform.