Data Quality Risks are a Leading Cause of Project Failure

Many organizations underestimate the complexity and severity of data-related risks. Poor documentation, semantic mismatches, duplication, and unrealistic assumptions about existing data quality often stay hidden until late in projects, when they are costlier to fix. Beyond technical failures, these risks lead to delays, budget overruns, compliance violations, and reputational damage. Proactively addressing data quality is essential for delivering value and avoiding failure.

📊 Data Quality Metrics and Scores Serve Distinct Purposes

Metrics evaluate aspects such as accuracy, completeness, or consistency, providing detailed insight into where issues exist. Scores, by contrast, aggregate multiple metrics into a single weighted number that reflects dataset health. Metrics guide technical fixes, while scores improve communication with non-technical stakeholders. The right balance of metrics and scores ensures both actionability and explainability, embedding data quality into everyday decision-making.

🗂 Managing Quality Across Medallion Architecture Requires Layered Controls

The Bronze layer emphasizes detecting ingestion errors and anomalies. The Silver layer enforces schema integrity, consistency, and business logic conformance. The Gold layer validates KPIs, aggregations, and analytical outputs. Automated checks, regression testing, and continuous monitoring across all layers enable early detection and build trust in the data products delivered to users.

💡 High-Quality Data is a Business Enabler, Not Just a Technical Necessity

When data is complete, accurate, and timely, organizations cut manual work, accelerate decision-making, and support automation. Reliable data underpins governance, analytics, and strategy—transforming data quality from a control mechanism into a driver of efficiency and trust.