Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers

Abstract

Many companies see Data Governance as a promising approach to ensuring data quality and maintaining its value as a company asset. While the practitioners' community has been vigorously discussing the topic for quite some time, Data Governance as a field of scientific study is still in its infancy. This article reports on the findings of a case study on the organization of Data Governance in two large telecommunications companies, namely BT and Deutsche Telekom. The article proposes that large, service-providing companies in general have a number of options when designing Data Governance and that the individual organizational design is context-contingent. Despite their many similarities, BT and Deutsche Telekom differ with regard to their Data Governance organization. BT has followed a more project-driven, bottom-up philosophy; Deutsche Telekom, on the other hand, favors a rather constitutive, top-down approach. The article also proposes a research agenda for further studies in the field of Data Governance organization.

Data Governance as a Collective Action Problem

“…The authors propose gradually defining more detailed governance rules, while ensuring the diversity of framing and ideologies of the collective action are taken into account in a manner where all individuals with "substantive interest" are adequately represented (Constantinides and Barrett 2015, p. 15). Their findings emphasize the importance of ideology and meaning within and between subgroups and how these, if not taken into account, will undermine centralized governance efforts of a large-scale information infrastructure; a perspective which contrasts how data governance literature tend to focus on defining an organization-wide approach (Khatri and Brown 2010;Otto 2011a).…”

“…The lack of a widely accepted definition of data governance (Pierce et al 2008) continues to permeate the field (Al-Ruithe et al 2018;Benfeldt Nielsen 2017;Brous et al 2016). One stream of research argues that data governance should function as the means to resolve poor data quality, emerging from the field of data quality management (Alhassan et al 2016(Alhassan et al , 2018Al-Ruithe et al 2018;Cheong and Chang 2007;Otto 2011a;Soares 2010;Weber et al 2009). While little attention is paid to what is actually meant by resolving poor quality, Otto mentions ensuring availability of specific data sets to remain compliant with regulatory or legal provisions, effective reporting, and integrated customer management (2011b).…”

“…While previous research has contributed valuable insights on how to design and conceive of data governance as an organization-wide program (Alhassan et al 2016;Brous et al 2016;Otto 2011cOtto , 2011aWeber et al 2009), studies that theorize how data governance is introduced into the complex reality of organizations by practitioners are hard to come by (Benfeldt Nielsen 2017). Findings from peripheral domains suggest that data governance is in fact challenging.…”

Data work in context: Value, risks, and governance

“…These domains are data principles “clarifying the role of data as an asset”; data quality “establishing the requirements of intended use of data”; metadata “establishing the semantics or content of data so that it is interpretable by the users”; data access “specifying access requirements of data”; and the data lifecycle “determining the definition, production, retention and retirement of data” (Khatri & Brown, , p. 149). In practice, Otto () presents evidence from a comparative case study in the telecommunications sector. The key finding is that data governance is a matter not only for data and informational professionals but also other professionals, while its implementation is also contingent on organizational context.…”

Quality and Value of the Data Resource in Large Enterprises

“…Data governance is a strategic framework, which defines decision-making rights with regard to the use and management of data in an enterprise. The goal of data governance is to maximize the value of the data resource (Khatri & Brown, 2010;Otto, 2011aOtto, , 2011b.…”