When agencies tell me their Epic data is messy, they usually mean one of a few specific things: reports that don't tie out, duplicate clients, policies coded inconsistently across the team, or premium data that doesn't match what was actually written. Each of these points to a different underlying problem — and a different fix.
The Four Dimensions of Epic Data Quality
I think about Epic data quality across four dimensions:
- Consistency — Are the same types of data always coded the same way? Are policy types, activity types, and document types used consistently across the team?
- Completeness — Are required fields populated? Are policies linked to the right client records? Are there gaps in the history that could create problems in an audit?
- Accuracy — Does the data match reality? Are policy limits, effective dates, and carrier information correct?
- Timeliness — Is data being entered when it should be? Are activities being closed out, or are there open items sitting for weeks?
Where to Start
Most agencies should start with consistency, because it's the lever that has the highest impact on reporting. If your code tables are a mess — dozens of policy types that mean the same thing, activity types that nobody uses consistently — your reports will never be reliable no matter how accurate the underlying data is.
Start there. Audit your code tables. Standardize. Train your team. Then tackle completeness and accuracy as a second phase. Timeliness usually improves as a byproduct of the other fixes.