Combining organizational change management and organizational ambidexterity using data transformation

Purpose The purpose of this paper is to propose a practicable data-driven theory for the implementation and management of organizational change by combining the organization ambidexterity research and the organization change management research. Design/methodology/approach This study is based on the qualitative approach and uses a single case (in-depth investigation approach) study to come up with a data-driven theory, which is usable in the context of organizational change management and organizational ambidexterity (OA). Besides, in-depth interviews of change management practitioners, this study uses various sources of secondary information. Findings The study finds that owing to the reactive, ad hoc, and discontinuous nature of change often triggered by external factors or internal crisis within the organization, an organization need to continually engage with the existing data. The outcome must be driven toward preparing for the change through data engagement, implementation and reinforcement. The authors found that in order to be successful it is essential to have a strategy, set-up the right operating model, be clear on the scope of the change management work-stream and continuously monitor the progress through defined milestones and acceptance criteria. For companies targeting to achieve competitive differentiation through ambidexterity, a well-grounded change management program is the key for the success. Originality/value The study suggests that there is little work combining organizational change management and OA from a practitioner's point of view. Accordingly, the authors propose a new data-driven organizational change management theory, which the authors term as the tripod theory for organizational change management. A practitioner's perspective on the topic using a case study of an insurance company's data transformation and a framework for structuring the change management program makes a meaningful contribution to the existing literature.