Data Quality

CHAPTER 10Data Governance

INTRODUCTION

In the previous chapters pertaining to the first three phases of the DARS framework – Define, Analyze, and Realize – we discussed the importance of data to improved business performance. We also analyzed the poor state of data quality and suggested measures including key best practices to improve or realize the data quality. But once these data quality practices are implemented or realized, these measures need to be controlled or managed to ensure that data quality levels are sustained. The fourth phase of the DARS model is the sustain phase that looks at measures so that the data quality efforts are supported and maintained to ensure optimal performance. While there are many practices to sustain data quality, one solution to improve and sustain the data quality in business is with effective data governance. In Chapter 1, we discussed that data management and data governance work together to improve data quality in the business. While the previous chapters were primarily on data management, this chapter will go deeper into the role of data governance in data quality.

What exactly is data governance? According to Gartner, “Data governance is the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics” (Gartner 2022a). But, why is data governance needed? What is the value of data governance? The main purpose of data governance ...