1 - Introduction

Abstract

This chapter introduces the concept of data quality as something that can be quantified, measured, and improved, all with a strict focus on return and investment. The data quality problem is pervasive in organizations across all industries. The chapter summarizes the processes of data quality in steps to build a data quality practice. It also defines some of the basic data quality concepts like data domains and their mappings, data quality rules and business rules, measurement and current state assessment, data quality requirement analysis, metadata policy, discovery of metadata and data quality and business rules, and root causes analysis and supplier management organization's data collection. These are valuable business resources, which until now, has been largely underutilized. However, the technological inventions that unlock and distribute these databases, have evolved a procedural methodology to help integrate the technical, organizational, and behavioral issues associated with enterprise knowledge. This methodology is referred to as knowledge management and plays pivotal role in data quality.