Data Quality First: Foundation of Reliable Results

No model outperforms its data. Data Quality First is a data science book devoted to building reliable results by treating data quality as a strategic foundation, not a technical afterthought.
The writing traces how poor data silently undermines analytics, machine learning, and decision-making. Inconsistent definitions, missing values, bias, and drift create false confidence long before failures are visible. This book positions quality as a continuous discipline, not a cleanup task.
Rather than focusing only on validation rules, the book emphasizes systems. Readers learn how governance, ownership, documentation, and monitoring prevent quality decay at scale. Each chapter connects data quality decisions to trust, reproducibility, and long-term reliability.
The tone is rigorous and practical, designed for analysts, engineers, and leaders responsible for outcomes. Language remains precise and actionable, translating quality principles into operational practice.
Data Quality First moves through data sourcing, validation, lineage, monitoring, and accountability—showing how reliability is built intentionally.
Key themes explored include:
• Data quality as strategy
• Trustworthy analytics
• Bias, consistency, and accuracy
• Ownership and governance
• Reliability over speed
Data Quality First is for teams who value trust—offering a blueprint to ensure results stand on solid ground.