Why this is in my collection

From the publisher:

Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techni

Highlights

  • Ten Steps: a quality methodology you can actually execute — McGilvray turns "improve our data" from aspiration into a numbered, repeatable process with entry and exit criteria; methodology as the antidote to heroic one-off cleanups, the DQ counterpart to model-driven repeatability.
  • The information life cycle (plan, obtain, store, maintain, apply, dispose) locates the defect — tracing where in the life cycle quality breaks tells you where the fix belongs; the same upstream-first instinct as fixing the model instead of patching every pipeline downstream.
  • Business impact techniques justify the rules budget — her toolkit for quantifying what poor data costs (from anecdotes to full cost analyses) is how a rules/quality layer wins funding against feature work; rules are the forgotten pillar partly because nobody priced their absence.
  • Data quality dimensions are chosen per assessment, not applied wholesale — the method selects only the dimensions that matter for the business issue at hand; scoped, purpose-driven checking rather than exhaustive checkbox governance.
  • Root cause, then prevention, then correction — the step ordering insists that cleansing without addressing cause is rework on a schedule; structure addresses the source, magic scrubs the symptom quarterly.
  • Sustainability is a step, not an afterthought — the method ends with embedding controls and monitoring so improvements persist; quality as an operating rhythm, exactly what automated checks-in-DuckDB style tooling operationalizes.

Highlights on this page are generated with the help of AI.