Data architects are well placed to address this problem, but research has mostly framed their role in technical terms and offers limited guidance on how to coordinate architecture, governance, and user enablement. This thesis addresses that gap by designing a framework that supports data architects in developing SSBI environments that also enable predictive analytics.

The study follows a qualitative, inductive Design Science Research (DSR) methodology grounded in an interpretivist philosophy. Problem awareness was established through participant observation of five breakdown episodes within a live enterprise environment, complemented by expert input. These insights, together with four theoretical principles drawn from the literature (socio-technical alignment, modularity, layered architecture, and integrated enablement), informed an artefact structured into five domains: Architecture and Integration, Governance and Quality Management, Enablement and Analytical Capability Building, Security and Compliance, and Maturity and Continuous Improvement. The domains are operationalised as 22 prioritised components organised along explicit SSBI and predictive-analytics paths, supported by a Data Readiness Assessment and a Maturity Map.

The framework was assessed through formative, scenario-based evaluation with two practitioners, who confirmed its usefulness as both a strategic communication-and-sequencing instrument and an operational checklist, and whose feedback prompted three refinements to the final artefact. The thesis contributes an operational framework for practitioners and a set of preliminary design principles that may transfer to other settings.