EBOOK
About
Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization.
In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses:
Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality
Butterfly effect of data quality
A detailed description of data quality dimensions and their measurement
Data quality strategy approach
Six Sigma - DMAIC approach to data quality
Data quality management techniques
Data quality in relation to data initiatives like data migration, MDM, data governance, etc.
Data quality myths, challenges, and critical success factors
Students, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout.
In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses:
Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality
Butterfly effect of data quality
A detailed description of data quality dimensions and their measurement
Data quality strategy approach
Six Sigma - DMAIC approach to data quality
Data quality management techniques
Data quality in relation to data initiatives like data migration, MDM, data governance, etc.
Data quality myths, challenges, and critical success factors
Students, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout.