A company drowning in data debt had no idea how bad it was until we ran a structured audit across 8 quality dimensions. We found 340K issues in 1.2M records, built automated cleanup scripts, and deployed prevention rules that cut new error introduction by 94%.
The company had been accumulating data debt for years. Customer records had duplicate entries, addresses with missing postal codes, phone numbers in 14 different formats, email addresses that were clearly fake, and transaction records that didn't match between systems. Revenue reports didn't tie out. Marketing was emailing people who'd been dead for years. Sales reps were calling numbers that belonged to other companies.
But nobody knew the scale of the problem. Was it 5% of records? 50%? Which types of errors were most common? Which tables were the worst offenders? Without a structured audit, remediation was impossible — the team was playing whack-a-mole with individual bad records instead of fixing root causes.
We conducted a structured data quality audit across 8 dimensions — Completeness, Accuracy, Consistency, Timeliness, Uniqueness, Validity, Integrity, and Conformity — profiling every record in the 4 core tables (Customers, Transactions, Products, Interactions). Then we built automated cleanup scripts and deployed real-time prevention rules that catch errors at the point of entry.
Data quality isn't one thing — it's eight. We scored every table across each dimension on a 0–100 scale, creating a quality fingerprint that shows exactly where the problems are concentrated:
The worst dimension was Completeness. In the Customers table alone, 42% of records were missing at least one critical field — postal code (34% null), phone number (28% null), email (19% null), or company name (15% null). Some records had nothing but a name and an ID — imported from a legacy system migration in 2019 that nobody ever cleaned up.
The downstream impact: marketing campaigns were being sent to incomplete segments, shipping estimates were wrong (no postal code), and the sales team couldn't call or email a quarter of their pipeline.
Key insight: The 2019 legacy migration was the root cause of 61% of all completeness issues. A single bulk import — done without validation — created more data debt than 4 years of organic data entry combined.
Abstract quality scores are useful for executives, but the engineering team needs to see the actual mess. Here's a side-by-side comparison of real records before and after cleanup — every highlighted field was an issue our scripts detected and fixed:
This single record had 9 issues across 5 quality dimensions: extra whitespace and casing (Conformity), typo in email domain (Accuracy), inconsistent phone format (Consistency), null postal code (Completeness), invalid date format (Validity), misspelled enum value (Validity), currency symbol in numeric field (Conformity), and unstandardized source label (Consistency).
The Uniqueness dimension revealed 14,200 duplicate customer records — 8.4% of the table. The worst case: one customer existed 7 times with slight name variations ("John Doe", "john doe", "J. Doe", "JOHN DOE", "John D.", "Jon Doe", "Doe, John"). Each duplicate had its own transaction history, making the customer's true lifetime value invisible.
We built a fuzzy matching engine that groups probable duplicates by normalized name + email + phone similarity, then merges transaction histories into a single golden record.
We classified every issue into a structured taxonomy. The top 3 categories alone — null values (31%), format inconsistencies (22%), and duplicates (14%) — account for 67% of all issues. These are also the three most automatable fix types, which is why the remediation scripts achieved such high coverage.
Key insight: 67% of all data quality issues fall into just 3 categories that can be fixed with automated scripts. The remaining 33% require human review — but focusing automation on the top 3 first gives you the highest ROI per engineering hour.
Fixing bad data is expensive. Preventing it is cheap. We deployed 24 automated validation rules that run on every data write — catching errors before they enter the system:
Select a table and time window to explore quality scores, issue trends, and dimension breakdowns. This monitoring view runs daily in production.
Completeness, Accuracy, Consistency, Timeliness, Uniqueness, Validity, Integrity, Conformity — each scored 0–100 per table for a precise quality fingerprint.
Every issue categorized into a 12-type error taxonomy with root cause tagging, automation potential scoring, and remediation priority ranking.
Python + SQL scripts targeting the top error categories: deduplication, format standardization, null backfill, typo correction, and enum mapping.
Validation rules running on every data write — from email format checks to anomaly detection — cutting new error introduction by 94%.
Daily-refresh dashboard tracking quality scores across all 8 dimensions with threshold alerts, trend lines, and table-level drill-down.
Delivered a 30-page runbook documenting every dimension definition, scoring methodology, cleanup script, prevention rule, and escalation procedure.
Quality score improved 36 points
New errors entering the system
From audit to fully automated prevention
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