Abstract:
While (big) data promises immense opportunity, initiatives focused on using data to pursue insight have mixed outcomes. The Management Support Systems (MSS) model summarises what we currently understand within Information Systems (IS) about the implementation and use of systems to improve organisations’ use of data. Adopting an ethnographic approach to observe how practitioners in two contrasting organisations actually generate insight from data, this research challenges the implicit information processing and implementation logics of the MMS model. The pragmatic messiness of pursuing insight is described in two monographs, which reveal the socially constructed nature of data in relation to phenomena, and the importance of data engagement to produce insight. Given that this PhD study also seeks to generate insight from data, it is compared and contrasted reflexively to the two cases observed. While the inquiry logic pursued in this study was made explicit, and was regularly reviewed and challenged, the two cases left this largely implicit. The use of tools is shown to facilitate and constrain inquiry, with related data acting as boundary objects between the different practitioner groups involved. An explanatory framework is presented and used to suggest various enhancements to the MSS model. First, the Problem Space is reframed to reflect the distinct, though interdependent logics involved in inquiry versus realising envisaged benefits from insights. Second, the MSS artefact itself is contextualised and Data Engagement rather than MSS or Tool Use is positioned as central. Third, Data are disentangled from the wider MSS artefact, as a critical, distinct construct. Fourth, an Alignment construct is introduced to address the boundary spanning nature of data initiatives. The thesis also highlights the value of using Wenger’s (1998) Communities of Practice (CoP) situated learning framework to study data initiatives, and the related value of mapping groups as a technique for further development. Some questions are provided for practitioners to gain a better understanding of data initiatives. Wider implications are also noted for the socio-material theorising of Data, and distinguishing between Data, Information and Knowledge concepts within the IS discipline.
