Design and evaluation of a reference model for data strategy development

The value of data to organisations, whether public or private, is undisputable yet public sector entities, particularly those of local government, have yet to leverage this value. Instead data is often perceived as a by-product of transactional processing which little inherent value. This is compounded by the ever-increasing volume of data, and the complexity of managing this data within the diverse and siloed delivery models required by local authorities. Statutory requirements and legislative imperatives, such as GDPR, place a significant responsibility on the public sector in the management of data with most giving little attention to the strategic management of this valuable asset as they scramble to deliver services with limited resources and changing landscapes.

The overall objective of the research is to design and evaluate ways in which local government data management can be improved within a Design Science Research (DSR) context, stated as follows: (1) Identify the components of a data strategy development framework using a taxonomy-building approach; (2) conduct a case study to evaluate the ensuing artifact; (3) evolve and complete construction of the artifact as a reference model for data strategy development; and (4) complete an ex-post evaluation of the artifact that demonstrates its utility.

DSR entails the development of an artifact, which can be a model, method, or instantiation, that is theoretically underpinned and rigorously evaluated. DSR falls within the pragmatist philosophical paradigm where a real-world emphasis and practical manifestation are to the fore. For the current study, the emphasis is on creating a useful artifact within an established domain, that of public sector data, but for which there is a dearth of solutions to improve how this data is managed.

Though the data management domain has much available by way of technology and tools and there is a body of research available in the IT and data governance fields, yet there is little research to guide the strategic management of data. Enterprise Architecture (EA) speaks to the alignment between business and technology in an organisation and existing EA frameworks and tools are adopted in the development of the solution for the current study. This EA-based approach guides the design of the solution artifact, a Reference Model for Data Strategy Development (RM-DSD) and follows the rules of the ArchiMate Modelling specification.

There are two key contributions of the study: that of the theoretically underpinned Reference Model for Data Strategy Development (RM-DSD) guided by Tallon et al (2013) An Emerging Theory of Information Governance, and, the development of a qualitative research method for reference model build (QRM-RMB) underpinned by Nickerson et al. (2013) A Method for Taxonomy Development and its Application in Information Systems. The noted primary research contribution is the RM-DSD, evolved from a rigorous taxonomy development method, represented as a conceptual framework which in turn serves as the viewpoint for the development of the model. The model, as represented through the ArchiMate (The Open Group 2019) modelling specification, is presented over three layers: the motivation, strategy, and business layer as a prescriptive tool to guide data strategy development. A rigorous evaluation follows the Pries-Heje et al. (2008) strategies for ex-ante and ex-post evaluation. The research methods include case study, focus groups and interviews.

Findings from the study confirm the need for a strategic approach to data management and the utility of the derived artifact as a reference model for data strategy development. The value of the qualitative research method has also been evident in its use throughout the current study.

A clear next step in the research will be to instantiate the model in the development of an actual data strategy in a local government scenario and subsequently within the broader public sector. This will provide an opportunity for further refinement of the model and the potential to develop the model as a formalised toolset.