Data Quality Framework for a Multi-Source Analytics Platform
Designed a data quality framework with automated validation, anomaly detection, and lineage tracking. Raised data quality scores from 62% to 96%.
Challenge
Analytics platform ingesting data from 6 sources with no validation — dashboards showing conflicting numbers, eroding executive trust.
Solution
Data quality framework with automated validation rules, anomaly detection, lineage tracking, and quality scorecards per source.
Result
Data quality score improved from 62% to 96%, executive trust in dashboards restored.
The Problem
At a global enterprise, the analytics platform had grown organically over three years. It ingested data from six sources — a CRM, two transactional databases, an ERP system, a third-party marketing feed, and a manual spreadsheet upload. Each source had its own schema, refresh cadence, and quirks. There was no validation layer between ingestion and the dashboards.
The result was predictable: the same metric showed different numbers depending on which dashboard you opened. Revenue figures in the finance view didn't match the sales dashboard. Customer counts diverged by 15% between marketing and product reports. Executives stopped trusting the data entirely and reverted to asking analysts to pull numbers manually — defeating the purpose of the platform.
When I inherited this problem, the first thing I did was quantify it. I ran a quality audit across all six sources and scored each on completeness, accuracy, consistency, and timeliness. The composite data quality score was 62%. Nearly four in ten data points had some issue.
What I Did
I designed a data quality framework with four pillars: validation rules, anomaly detection, lineage tracking, and quality scorecards.
For validation, I worked with data engineers to define rules per source — null checks, range validations, referential integrity constraints, and freshness thresholds. These ran automatically on every ingestion cycle and quarantined records that failed.
For anomaly detection, we implemented statistical checks that flagged unusual volume spikes, value distribution shifts, or sudden drops. This caught issues like a marketing feed silently deduplicating records or an ERP batch job failing without alerting anyone.
Lineage tracking was critical for debugging. We mapped every metric back to its source fields, transformations, and aggregation logic. When a number looked wrong, anyone could trace exactly where it came from.
Finally, I introduced quality scorecards — a weekly report showing each source's quality score, trend, and top issues. Source owners were accountable for their scores, and we reviewed them in a fortnightly data governance standup.
The Outcome
Within four months, the composite data quality score climbed from 62% to 96%. Dashboard discrepancies dropped to near zero. The quarantine system caught an average of 200 bad records per week that previously flowed straight into reports.
The biggest win was restoring executive trust. The CFO, who had stopped using the platform entirely, started running board prep directly from the dashboards. We eliminated roughly 20 hours per week of manual data pulls that analysts had been doing as workarounds. The framework also became the standard for onboarding new data sources — every new integration had to pass quality gates before going live.