Relational Effects of Data Networks: How Strategically Aligned Data Networks Impact Digital Transformation

Abstract

Digital Transformation (DX) has become a pervasive global phenomenon that is having a profound effect at all levels of an enterprise. The umbrella term DX is supported by nascent technologies such as blockchain, Internet of Things (IoT), and cloud-based computing and networking. The speed by which these new DX technologies have emerged has challenged current technical infrastructures, budgets, and skillsets as organizations attempt to incorporate and implement these technologies as part of pervasive digital operational initiatives. DX also includes the transformative effects (deployment and adoption) of these technologies, and these are referred to as “outcomes” in this dissertation. Not surprisingly, recent studies indicate that at least seventy percent of DX projects either fail or underperform. As firms assess existing technology-to-business alignments in search of attributable causation, they discover that these alignments are often opaque regarding the capabilities required to obtain optimal digital transformation outcomes from the application of specific technologies. This is especially true as underlying technology infrastructures and architectures, which have had a traditionally functional role such as the data network, are increasingly relied upon to support the strategic outcome requirements of DX. This dissertation uses an inductive, multiple case study approach to explore these relationships and outcomes. It directly observes a set of large organizations across multiple verticals. These organizations have all completed pervasive digital transformation initiatives, more specifically, this study measured the resultant levels of digital transformation project outcomes achieved as a fraction of the initial digital transformation project deliverables. Furthermore, the study makes inferences regarding the relationship of the data network deployment posture to the observed digital project outcomes. To study this empirically, data network alignment is classified as either being functionally or strategically aligned. Analysis of the resultant data revealed four distinct themes: (a) organizations that procured data network equipment with pricing as the critical determinant experienced sub-par digital transformation outcomes; (b) organizations which considered key business drivers when procuring and architecting the data network achieved more successful digital transformation outcomes; (c) organizations which did not perform a network upgrade or perform a significant data network architectural change during the previous five years did not meet their own goals of digital transformation, and (d) some DX technology deployments achieved a high percentage of project deliverables without undertaking a data network upgrade. With respect to the fourth theme, however, the resulting low internal adoption of deployed DX technology among targeted operational units resulted in a ‘split-brain’ operation and thus an overall underperformance of the DX project. The final dissertation chapter includes recommendations for future qualitative research on both digital transformation outcomes and the effect of managed network services on digital transformation outcomes as well as the need for quantitative research to establish deeper causation beyond the loose causation which is posited in this paper.