Digital Transformation Designer: Towards a Comprehensive, Collaborative and Easy-to-Use Modeling Support for Enterprise-level Change Endeavors

Authors

  • Tobias Kautz University of St.Gallen
  • Robert Winter University of St.Gallen

DOI:

https://doi.org/10.18417/emisa.19.4

Keywords:

Digital Transformation, Program Management, Enterprise Modeling, Business Engineering, Enterprise Architecture

Abstract

Established companies intending to leverage digital technologies are required to innovate their ‘legacy’ business models through organizational transformations. Existing modeling support often leaves a ‘white space’ between informal canvas-style models used in the early phases and (semi-)formal aspect models used in the later phases of transformation endeavors. Adopting a Design Science Research approach, this paper presents a semi-formal model that is intended to fill this gap, i. e., to provide easy-to-use support for the heterogeneous stakeholders that participate in early phases of digital transformation endeavors.Being based on the traditional Business Engineering set of models and methods, this comprehensive and collaborative approach was validated together with the digital transformation program manager of a large, international corporation. For supporting analysis, reflection and design tasks that involve a broad range from canvas-style models to enterprise architecture models, four requirements were identified to be central: (remote) collaboration support, a holistic and integrative perspective, an enterprise-level view, and a focus on change. The actual model is comprised of over 20 partial models including popular canvases depicting the transformation program’s content on the strategy-to-IT layers, the enterprise and local level, and in the as-is and to-be state. Demonstration and evaluation were done with practitioners and students of an Executive Master program focusing on digital transformation. Both confirmed the utility of the underlying method and recognized its distinctive features, while capability and IT landscape models were found especially relevant. The method is expected to be applicable for digital transformations beyond the case and also to be projectable to smaller-scaled digital business innovations.

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Published

2024-02-08

Issue

Section

Special Issue on Models-at-Work