Why data degrades, and why cleanup without correction at the point of entry regresses

Reference data degrades through three mechanisms that operate continuously. People and organizations change: contacts change roles and employers, companies merge and rename, addresses move. Records are created defectively: integrations and forms insert values that no rule checks, representatives enter what they know at the moment, enrichment tools write values of uneven provenance. Automation writes silently: workflow rules, Process Builder processes and flows stamp, copy and default values according to logic that may not have been reviewed for years, and legacy automation that has passed end of support continues to run, unsupported.

The arithmetic of a single remediation

The arithmetic follows from the mechanisms. If new records arrive with defects at a steady rate, a single remediation reduces the stock of defective records but does nothing to the flow. Within a few quarters the stock can return to where it was, and the effort must be spent again. This is the pattern when remediation lacks dedicated time and ownership: a modest attenuation of decay, followed by regression.

Correcting CRM data decay at the point of entry

Correction at the point of entry takes four forms, and our consultants identify in the assessment which apply to each in-scope object. Matching, duplicate and validation rules are configured so that most records are checked as they are saved, including those entered through the API or by import, and any integration exempted from them is governed separately. Integrations and forms are reviewed for the fields they populate and the transformations they apply, and are corrected or wrapped in validation. Automation that writes to in-scope fields is inventoried, refactored where its logic is wrong or unsupported, and regression-tested. Ownership of each standard is assigned to a named person in the function that defines it, so that the next change to a definition has an author.

The measure of success is not the condition of the data at handoff but its condition in the quarters that follow. Duplication rate, completeness of agent-critical fields and match rate are measured before the work and at handoff, and are best measured on a regular cadence thereafter; a remediation whose gains have decayed within a quarter or two was a cleanse, not a remediation. Trailhead's data quality module introduces the Salesforce features on which these measures rely.

What decay means for an agent

An agent reasons from what the records say, so a record that has decayed but still looks current can mislead it. That is why the measures above include the completeness of agent-critical fields.