Mastering Data Quality Evaluation: ISO 19157:2023

Practical Implementation in Geospatial Data Management

AUTHOR

Dr. Mohamed-Ali Ibrahim

INTRODUCTION

In today’s data-driven world, the reliability of geospatial information can directly affect planning, policy, infrastructure, environmental monitoring, emergency response, and strategic decision-making.

Mastering Data Quality Evaluation: ISO 19157:2023 provides a practical guide to understanding and applying the international framework for evaluating the quality of geographic information.

The book explains how organizations can assess, document, report, and improve geospatial data quality using the principles and methods presented in ISO 19157:2023. Readers are guided through the key data quality elements of completeness, logical consistency, positional accuracy, temporal accuracy, thematic accuracy, and usability, along with sampling techniques, reporting methods, metadata, uncertainty, conformance, and compliance.

The book also explores modern applications including AI, Machine Learning, automated data validation, real-time monitoring, Data Quality Management Systems, dashboards, data governance, and audit readiness, helping organizations integrate ISO 19157:2023 into contemporary geospatial data environments.

MAIN BENEFITS
Understand ISO 19157:2023: Gain a practical introduction to the principles and structure of the standard.
Improve Geospatial Data Quality: Evaluate whether geographic information is accurate, consistent, complete, usable, and fit for purpose.
Master the Six Data Quality Elements: Apply completeness, logical consistency, positional accuracy, temporal accuracy, thematic accuracy, and usability.
Improve Data Quality Reporting: Learn how to document results, metadata, uncertainty, limitations, and recommendations clearly.
Apply Effective Sampling Methods: Understand random, stratified, systematic, and cluster sampling for large geospatial datasets.
Strengthen Data Governance: Align data quality objectives, roles, responsibilities, KPIs, and monitoring with organizational goals.
Improve Traceability and Reproducibility: Use metadata, version control, audit trails, standardized naming, scripts, and documentation.
Prepare for Compliance and Audits: Build clear evaluation procedures, supporting evidence, reporting systems, and audit-ready records.
Use AI and Automation: Explore automated data cleaning, validation, anomaly detection, quality monitoring, and reporting.
Support Better Decision-Making: Increase stakeholder confidence by providing trustworthy and clearly evaluated geographic data.
Improve Data Interoperability: Standardize quality evaluation and reporting to support data sharing across systems and organizations.
Prepare for Future Data Environments: Adapt data governance and quality frameworks as AI, IoT, satellite data, drones, and new technologies evolve.
WHY CHOOSE THIS BOOK?

Mastering Data Quality Evaluation: ISO 19157:2023 focuses on practical implementation rather than simply describing a technical standard.

The book provides guidance on:

ISO 19157:2023 principles and structure
Geospatial data quality evaluation
Completeness and logical consistency
Positional, temporal, and thematic accuracy
Data usability
Sampling and evaluation techniques
Metadata and data provenance
Reproducible quality evaluations
Data Quality Management Systems
Data quality KPIs and dashboards
Conformance and compliance
Internal audits and corrective actions
AI and automated data-quality monitoring
Real-time data validation
Integration into organizational data systems
Long-term compliance and audit readiness
Adapting to future technologies and ISO revisions

The book also contains practical tables, checklists, sample reports, evaluation frameworks, case studies, workflows, and visual explanations. For example, the manuscript provides a sample data-quality report structure and a conformance/compliance checklist, making it especially useful for real