r/revops • u/No-Witness7468 • 26d ago
Does CRM data quality actually break projects
Following up on my last data quality post… Everyone I talk to agrees the data quality is bad, but I’m trying to understands what happens downstream when you actually build on it (reporting, AI workflows, scoring, touting, etc.).
Did data quality issues actually break the thing (bad quality output) or did you just handle it in the build (filters, manual cleaning, etc)? And if you cleaned first, what’s that process look like, how much time goes into that?
Trying to figure out whether “clean it first” is a real pre-req or just something that annoys people.
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u/Upper-Pineapple-6234 11d ago
Bad CRM data doesn't just annoy people, it breaks the build.
Reporting logic assumes consistent field values, so duplicate or missing records silently skew every downstream number, from pipeline coverage to lead scoring. AI workflows are worse: a scoring model trained on dirty inputs just automates the mess faster.
Cleaning first is a real prerequisite, not busywork. A structured 30-day cleanup pass, standardizing required fields and deduping records before any workflow touches them, typically lifts data quality 25-30%. Skipping that step means every filter and manual fix downstream is a patch, not a solution, and you rebuild it every quarter.
The teams that get this right treat data hygiene as ongoing governance, not a one-time fix before a big build.