The five red flags of bad data ACT identifies: duplicate records (same client under variant spellings), incomplete or misused fields (placeholders like 00000 ZIP), outdated or incorrect info (stale phone numbers, dead carrier appointments), inconsistent formatting, workflow or system issues (misfiled notes). The four real costs: operational inefficiencies (7 hours/week wasted per employee per MIT Sloan), lost revenue (missed renewals from outdated contacts), compliance & E&O risk, client churn (customers leave after 1–2 poor experiences).
The five quick wins.
Assign Data Champions — accountability per department.
Designate a specific lead per department (CL, PL, L&H) responsible for ongoing data quality monitoring and cleanup. Give them authority to enforce standards and a weekly time allocation for cleanup work. The Champion role moves data quality from "everyone's problem" to "someone's responsibility."
Merge duplicates + audit key fields.
Run a deduplication scan using AMS tools. Identify and consolidate duplicate client records, ensuring the surviving record contains the most complete and current information. Audit the 8 critical data categories: client info, policy details, claims history, CSR & producer info, carrier appointment data, marketing preferences, document management, agency naming conventions.
Standardize formats — the basics that compound.
Enforce consistent formatting rules for ZIP codes (5-digit or ZIP+4), phone numbers (consistent dash/parenthesis format), name capitalization (Title Case), and address abbreviations (St. vs Street). Inconsistencies prevent accurate deduplication and make automated data integration unreliable.
Attach documents to correct files — eliminate orphans.
Audit unattached documents and notes. Ensure all correspondence, certificates, endorsements, and claims documents are labeled correctly and attached to the appropriate client and policy files. Misfiled documents are E&O exposure waiting to happen.
Review workflows for consistency.
Audit current data entry processes to identify steps where bad data enters the system. Implement required-field validation where the AMS supports it. The system should make it harder to enter bad data than good data — not the reverse.
The cadence that compounds. CSR daily: clean 5 client records per day. Producer weekly: spot-check the top 10 accounts. Principal quarterly: audit key fields. Small, consistent effort yields large cumulative results — typically 8–10 weeks to clean a small book, 6+ months for a mid-size one. The work compounds invisibly until the buyer's QoE team begins their diligence — at which point the difference between clean and dirty data shows up as either deal momentum or retrade leverage.
Terminology on this shelf
- Data Champion
- Department-level role responsible for ongoing data quality monitoring and cleanup. ACT term.
- AMS Data Hygiene
- State of cleanliness and accuracy of Agency Management System records.
- Placeholder Field
- A field filled with dummy data (e.g., 00000 ZIP, "TBD" name).
- Critical Data Element Review
- Systematic audit of the 8 data categories ACT flags as essential.
- NPN
- National Producer Number. Producer license currency depends on this being current.