Data Quality

The data economy is increasingly embraced worldwide in every industry as data-driven organizations have demonstrated improved business performance. Every company today is looking at leveraging data, analytics, and AI (Artificial Intelligence) for improved business performance. But most organizations are impaired with poor data quality that is adversely affecting their analytics and AI solutions. In fact, recent studies have found that just 3 percent of the data in an enterprise meets quality standards, 27 percent of data in the companies is flawed, and poor data quality impacts up to 12 percent of revenues.

To provide organizations a competitive advantage from data, Data Quality: Empowering Businesses with Analytics and AI provides readers with practical guidance and proven solutions to derive quality business data. The book examines the four phase D-A-R-S approach to data quality: Define-Assess-Realize-Sustain. Throughout the process, you'll learn to accelerate business results with high-quality data, discover the best data management and data governance practices, avoid common pitfalls, and meet the desired standards of data quality. Specifically, the author describes the 16 most common root causes of data quality degradation and offers 10 practical data management and data governance implementation patterns for remediating and sustaining data quality to drive exceptional business results. Ultimately, you will confirm that your firm's data is capable of powering high-level analytics and AI applications without leading you down blind alleys or dead ends.

An essential roadmap for data analysts, data governance professionals, data scientists, and other decision makers with a stake in deriving new and interesting intelligence from a firm's raw data, Data Quality will earn a place in the libraries of executives, founders, entrepreneurs, and other professionals who seek to ensure that the insights they're drawing from their data are valid and reliable.