European University Institute Library

Executing data quality projects, ten steps to quality data and trusted information (TM), Danette McGilvray

eng

Includes bibliographical references and index

illustrations

index present

non fiction

Executing data quality projects

bibliography

1233317906

Danette McGilvray

ten steps to quality data and trusted information (TM)

This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. --, Provided by publisher

1. Data Quality and the Data-Dependent World 2. Data Quality in Action 3. Key Concepts 4. The Ten Steps Process 5. Structuring Your Project 6. Other Techniques and Tools 7. A Few Final Words Appendix: Quick References