Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality

Foreword

Over the past few years, there has been a dramatic shift in focus in information technology from the technology to the information. Inexpensive, large-scale storage and high-performance computing systems, easy access to cloud computing; and the widespread use of software-as-a-service, are all contributing to the commoditization of technology. Organizations are now beginning to realize that their competitiveness will be based on their data, not on their technology, and that their data and information are among their most important assets.

In this new data-driven environment, companies are increasingly utilizing analytical techniques to draw meaningful conclusions from data. However, the garbage-in-garbage-out rule still applies. Analytics can only be effective when the data being analyzed is of high quality. Decisions made based on conclusions drawn from poor quality data can result in equally poor outcomes resulting in significant losses and strategic missteps for the company. At the same time, the seemingly countless numbers of data elements that manifest themselves in the daily processes of a modern enterprise make the task of ensuring high data quality both difficult and complex. A well-ground data quality program must understand the complete environment of systems, architectures, people, and processes. It must also be aligned with business goals and strategy and understand the intended purposes associated with specific data elements in order to prioritize them, ...