Album

Mastering Data Quality Evaluation: ISO 19157:2023: Practical Implementation in Geospatial Data Management

Mastering Data Quality Evaluation: ISO 19157:2023Practical Implementation in Geospatial Data ManagementTrim Size:8.5 × 11 inchesIntroductionIn today’s data-driven world, the accuracy and reliability of geospatial information define the success of digital transformation, planning, and decision-making.Mastering Data Quality Evaluation: ISO 19157:2023translates the complex requirements of the international standard into clear, practical guidance for professionals responsible for geospatial data management.This book provides a complete framework for understanding and applying ISO 19157:2023 principles, offering practical methods for evaluating data quality, documenting results, and ensuring long-term compliance. Readers will gain the tools needed to build datasets that are accurate, consistent, and trusted across global systems.Key BenefitsImproved Data Integrity:Learn how to assess completeness, accuracy, consistency, and usability in accordance with ISO 19157:2023.Actionable Implementation:Follow practical, step-by-step guidance to embed quality evaluation into everyday geospatial workflows.Transparent Reporting:Develop traceable reports that meet audit and governance standards.International Alignment:Ensure data interoperability and compliance with global quality frameworks.Enhanced Decision-Making:Use reliable data to support policy, research, and innovation.Audit Readiness:Understand the standard’s conformance and compliance requirements for ISO certification.Future-Oriented Approach:Integrate data quality evaluation with AI-based and automated systems.Why Choose This BookUnlike overly technical manuals, this guide is written in clear, professional language that emphasizes usability and practical outcomes. It bridges theory and real-world application, helping geospatial professionals, quality managers, auditors, and decision-makers implement ISO 19157 effectively. Each chapter links key concepts to field practice, empowering organizations to adopt standardized data evaluation processes that lead to measurable im

Lagu: 97 · 4.211 diputar