Introduction

There are few businesses today that are not reliant on high quality information to support performance and productivity. In today’s organizations, the importance of high-quality data within any environment is dictated by the needs of the operational and the analytical applications that will process the data. Data governance is instituted as a means for data quality assurance, which has two contexts:

1. The ability to protect against negative business impacts by identifying data quality issues before any material impact takes place (such as failure to comply with regulations or allowing fraudulent transactions to occur).
2. Establishing pervasive trust in the data and providing confidence that the organization can take advantage of business opportunities as they arise.

Operational data governance is the manifestation of the processes and protocols necessary to ensure that an acceptable level of confidence in the data effectively satisfies the organization’s business needs. A data governance program defines the roles, responsibilities, and accountabilities associated with managing data quality. Rewarding those individuals who are successful at their roles and responsibilities can ensure the success of the data governance program. To measure this, a “data quality scorecard” provides an effective management tool for monitoring organizational performance with respect to data quality control.

Establishing Business Objectives

In this paper we look at taking the concepts of data governance into general practice as a byproduct of the processes of inspecting and managing data quality control. By considering how the business is impacted by poor data quality – and establishing measurable metrics that correlate data quality to business goals – organizational data quality can be quantified and reported within the context of a scorecard that describes the level of trustworthiness of enterprise data.