Data quality : empowering businesses with analytics and AI / Prashanth H. Southekal.

Includes bibliographical references and index.

Business data -- Data quality in business -- Causes for poor data quality -- Data lifecycle and lineage --  Profiling for data quality -- Reference architecture for data quality -- Best practices to realize data quality -- Best practices to realize data quality -- Data governance -- Protecting data -- Data ethics.

"Quality data is the key for business enterprises to offer improved performance in operations, compliance, and decision making. According to McKinsey, data driven organizations provide EBITDA increases between 15 to 25% than peers. However, to be a data driven organization, data quality is very important. But most companies are plagued with poor data quality. A HBR study found that just 3% of the data in a business enterprise meets quality standards. According to Gartner, 27% of data in the world's top companies is flawed--so companies are looking for practical guidance to improve data quality. This book examines the four-phase DARS approach (Define-Assess-Realize-Sustain) for companies to manage high quality data in organizations. This approach provides a combination of strategy and tactical elements to deliver the greatest value from data to the business. It is a playbook that offers prescriptive recommendations based on proven best practices to realize and sustain data quality"-- Provided by publisher.