Handbook of Data Quality: Research and Practice - Practical Guide

In this review of Handbook of Data Quality: Research and Practice the reviewer finds a focused, academic-to-practice reference for anyone responsible for data governance, analytics, or systems that rely on accurate records. The book addresses how the explosion of diverse, large-scale and public data sources has raised the stakes for data accuracy, currency and completeness, and the review highlights the single biggest reason to buy: it treats data quality as an integral part of the whole data cycle rather than an afterthought, making it useful for practitioners who must bridge theory and operational control.

Key Features

• Comprehensive perspective: Presents data quality as a lifecycle concern, helping readers consider quality at collection, storage and use stages.
• Real-world relevance: Emphasizes risks introduced by large-scale and public web data, which is directly applicable to modern analytics projects.
• Decision-focused framing: Connects data accuracy, currency and completeness to business intelligence and organizational outcomes, clarifying why quality matters to stakeholders.
• Practical orientation: Offers guidance for incorporating quality control into existing data management practices rather than relying on standalone remedies.
• Cross-domain value: Useful to individuals, corporations and government agencies facing data-driven decisions and regulatory exposure.

Who It's For

The handbook is best suited for data stewards, data architects, analytics managers and IT professionals who need to understand how poor data quality can propagate through business intelligence systems and lead to incorrect decisions. Its focus on lifecycle and operational integration makes it a practical reference for teams designing governance and control processes.

Researchers and graduate students interested in the evolution of data quality thinking will also find the material useful, but readers seeking a quick how-to checklist or lightweight tool-specific instructions should look elsewhere, as the work emphasizes principles and integration rather than step-by-step software tutorials.

Pros & Cons

Pros

• Frames data quality across the whole data cycle, which helps build sustainable controls rather than temporary fixes.
• Directly addresses challenges posed by diverse and public web data, making it relevant to contemporary datasets.
• Highlights the connection between data quality and strategic decision-making, useful for communicating with non-technical stakeholders.

Cons

• Not a plug-and-play manual for specific tools, so readers seeking detailed software instructions will need supplemental resources.

Specifications

Title	Handbook of Data Quality: Research and Practice
Author / Brand	Shazia Sadiq
Primary focus	Data quality across the data lifecycle
Audience	Practitioners, researchers, agencies and corporations
Coverage highlights	Accuracy, currency, completeness and governance implications
Relevance	Large-scale, diverse and web-exposed datasets

Our Verdict

The Handbook of Data Quality is a valuable, practical reference for professionals who must integrate quality thinking into data management and business intelligence processes. It delivers strong conceptual guidance about risks from diverse and public data and is good value for teams aiming to reduce erroneous decisions by treating data quality as an organizational responsibility.

Frequently Asked Questions

Is this book practical for IT teams?
Yes; it emphasizes integrating quality into data operations, which helps IT teams design sustainable controls rather than temporary fixes.

Will it teach specific software techniques?
No; the focus is on principles and lifecycle integration, so tool-specific tutorials are not the book's purpose.

Who benefits most from this handbook?
Data stewards, analytics managers and policy-makers who need to understand how quality affects decisions and governance will gain the most.