Brief
Six-dimension data quality framework on Snowflake and Collibra (NDA).
Confidential client · Anonymized case study.
An enterprise data quality initiative focused on maintaining data integrity by generating analyzed reports and enforcing quality rules across large-scale operational datasets. The project served a major global corporation under strict confidentiality.
Problem
Large enterprise datasets suffer from issues with accuracy, completeness, consistency, timeliness, validity, and uniqueness. The team needed automated data-quality rules, analysis pipelines, and reporting to detect and prevent bad data before it propagated downstream.
Features
Outcome
SQL quality rules deployed
0
Data completeness improvement
0
Duplicate record reduction
0
Growth signal
Figures animate in as you scroll — pulled from this project's published results.
- Established a repeatable data quality framework for enterprise datasets
- Improved data accuracy and completeness through automated rules and cleaning
- Enabled governance stakeholders to monitor and enforce data standards via Collibra
Challenges & solutions
- Duplicate and near-duplicate records → FuzzyWuzzy-based fuzzy matching and deduplication pipelines
- Rule scalability across datasets → Modular SQL JOIN-based DQ rules deployed via Collibra
- Pattern discovery at scale → Snowflake profiling combined with Collibra governance workflows
- Multi-dimensional quality checks → Structured framework covering all six DQ dimensions
