Data Profiling and Quality Issue Management
How to find where your data quality actually breaks before it reaches production, and what to do once it has — the operational complement to your Data Quality Rules.
What's included
- 3-pass profiling methodology: structure (types, nulls, duplicates), content (ranges, patterns, outliers), and relationships (referential integrity across tables)
- Quality issue log template with severity levels and suggested resolution SLAs
- Triage workflow: who decides, who fixes, who verifies
- Legal Notice
Why this document exists
Having quality rules defined doesn't help much if nobody systematically knows where to look for failures, or what to do once they're found. Profiling is the process of examining real data to find where those rules are being broken — before a flawed report reaches leadership or an AI model gets trained on defective data. And without a clear triage process, detected quality issues pile up in a spreadsheet nobody reviews. This document closes that loop: detect, log, prioritize, fix, verify.
Frequently asked questions
How is this different from Data Quality Rules?
Data Quality Rules define WHAT a data point must satisfy. This document covers HOW to detect where that threshold is being missed (profiling) and WHAT TO DO when it is (triage and resolution). They're complementary.
Do I need automated profiling tools to use this?
No, the methodology is designed to be applied manually with basic SQL queries or Excel, though it also works as a reference framework if you already use an automated profiling tool.
Does this replace legal advice?
No. It's a working methodology, but it doesn't constitute legal, technical, or professional advice of any kind — see the included Legal Notice.
What format is it delivered in?
PDF with the profiling methodology, Excel with the issue logging and triage template, and the Legal Notice.