Dissertation/Thesis Abstract

Data Governance: The Missing Approach to Improving Data Quality
by Barker, James M., DM/IST, University of Phoenix, 2016, 293; 10248424
Abstract (Summary)

In an environment where individuals use applications to drive activities from what book to purchase, what film to view, to what temperature to heat a home, data is the critical element. To make things work data must be correct, complete, and accurate. Many firms view data governance as a panacea to the ills of systems and organizational challenge while other firms struggle to generate the value of these programs. This paper documents a study that was executed to understand what is being done by firms in the data governance space and why? The conceptual framework that was established from the literature on the subject was a set of six areas that should be addressed for a data governance program including: data governance councils; data quality; master data management; data security; policies and procedures; and data architecture. There is a wide range of experiences and ways to address data quality and the focus needs to be on execution. This explanatory case study examined the experiences of 100 professionals at 41 firms to understand what is being done and why professionals are undertaking such an endeavor. The outcome is that firms need to address data quality, data security, and operational standards in a manner that is organized around business value including strong business leader sponsorship and a documented dynamic business case. The outcome of this study provides a foundation for data governance program success and a guide to getting started.

Indexing (document details)
Advisor: Fonseca-Lind, Sandra
Commitee: Xu, Jiefeng, Ziegler, James
School: University of Phoenix
Department: Management in Organizational Leadership
School Location: United States -- Arizona
Source: DAI-B 78/06(E), Dissertation Abstracts International
Subjects: Business administration, Information Technology, Information science
Keywords: Data architecture, Data governance, Data leadership, Data quality, Data standards, Master data management
Publication Number: 10248424
ISBN: 978-1-369-42756-1
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