David Loshin

Business problems are directly related to unmet expectations regarding data quality. Flawed information production processes carry risks that can hinder the successful achievement of critical business objectives. However, these deficiencies can be mitigated through data quality management and control, which involves monitoring the quality of the information production process from start to finish to ensure that imperfections are identified, prioritized, and addressed early on, before significant impacts can occur. The Practitioner’s Guide to Improving Data Quality provides the fundamentals for understanding the effects of poor data quality and assists practitioners and managers in raising awareness, gaining sponsorship, planning, and establishing a data quality program. This book offers templates and processes for analyzing business impacts, defining data quality metrics, inspection and monitoring, remediation, and the deployment of data quality tools. It does not shy away from difficult topics, making it a groundbreaking book that offers practical advice on how to effectively accomplish these tasks.

Target audience:
Practitioners in data management, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, system analysts, and their managers.