Data governance maturity assessment tool: A design science approach
Marchildon, Philippe
Data governance maturity assessment tool: A design science approach - 2019.
37
Today, the use of data has become a strategic asset as it allows organizations to discover new trends and develop deeper insights into their customers and partners, as well as the markets and environments in which they operate. To properly manage their data, organizations rely on a data governance framework (DGF) that defines the processes, policies, practices, and structures necessary to orchestrate and optimize the collection, storage, use, and dissemination of data as organizational assets. However, most organizations fail to implement a DGF tailored to their needs since they do not know the precise maturity level of their data management practice and therefore do not know where to start when implementing a DGF. To help organizations evaluate their operations against data governance best practices and help them develop, deploy, and/or improve their DGF accordingly based on their key gaps, the present paper develops a data governance maturity assessment tool using a design science research approach. The proposed artifact, which consists of 11 dimensions and 72 questions, allows organizations to assess where they stand in terms of data governance and, in turn, to better define and prioritize the goals, content, and activities of their data governance initiatives.
Data governance maturity assessment tool: A design science approach - 2019.
37
Today, the use of data has become a strategic asset as it allows organizations to discover new trends and develop deeper insights into their customers and partners, as well as the markets and environments in which they operate. To properly manage their data, organizations rely on a data governance framework (DGF) that defines the processes, policies, practices, and structures necessary to orchestrate and optimize the collection, storage, use, and dissemination of data as organizational assets. However, most organizations fail to implement a DGF tailored to their needs since they do not know the precise maturity level of their data management practice and therefore do not know where to start when implementing a DGF. To help organizations evaluate their operations against data governance best practices and help them develop, deploy, and/or improve their DGF accordingly based on their key gaps, the present paper develops a data governance maturity assessment tool using a design science research approach. The proposed artifact, which consists of 11 dimensions and 72 questions, allows organizations to assess where they stand in terms of data governance and, in turn, to better define and prioritize the goals, content, and activities of their data governance initiatives.
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