How data governance frameworks can leverage data-driven decision making

Abstract

With the technological advances, organizations have experienced an increasing volume and variety of data, as well as the need to explore it to stay competitive. Data governance (DG) importance emerges to support the data flow, to record and manage knowledge derived from data, as well as establishing roles, accountabilities, and strategies, which further results in better decision-making. Through the definition of strategies to manage data in a consistent manner, data governance establishes the path to an enterprise-wide standardization, providing unchallenging access, management, and analysis of data to derive useful insights. Research on data governance frameworks is limited and lacks a key perspective: how can firms ensure sustainability on their programs. Data governance programs can only be continuously valuable if supported by a holistic framework focused on sustainability. To understand this gap, five frameworks are presented, analyzed and evaluated according to an assessment matrix based on eleven critical success factors (CSF) for data governance. As a result of this study, where we offer a more comprehensive assessment tool, both researchers and practitioners can understand the maturity level of each CSF in the reviewed frameworks and identify which areas need further exploration and how to accomplish higher data governance maturity levels.