Data Quality

CHAPTER 8Best Practices to Realize Data Quality

INTRODUCTION

The previous chapter looked at the architectural aspects of data quality. Based on the architectural elements discussed in the previous chapter, this chapter looks at the key principles and patterns, that is, the best practices required to improve data quality in the organization. As mentioned in Chapter 4, databases in business enterprises rarely begin their lives empty. Often, data origination and data capture activities start from data conversion or migration from some legacy database, spread sheets, and paper documents that often have their origins in mergers, acquisitions, and divestures. Hence the primary focus of this chapter is to look the key principles and patterns required to improve the quality of existing data. Principles provide high-level guidelines. They are abstract and not concrete. Patterns on the other hand are concrete and proven solutions to real-world problems. They are instantiation of the principles. Principles and patterns together form the best practices.

OVERVIEW OF BEST PRACTICES

 Data is a reflection of the process. A quality process both in business management and data management leads to quality data.

Organizations today are now looking for best practices or proven processes on improving data quality in a reliable and efficient manner. In simple terms, a best practice is a ...