Assessment of process capabilities in transition to a data‐driven organisation: A multidisciplinary approach

Abstract

The ability to leverage data science can generate valuable insights and actions in organisations by enhancing data-driven decision-making to find optimal solutions based on complex business parameters and data. However, only a small percentage of the organisations can successfully obtain a business value from their investments due to a lack of organisational management, alignment, and culture. Becoming a data-driven organisation requires an organisational change that should be managed and fostered from a holistic multidisciplinary perspective. Accordingly, this study seeks to address these problems by developing the Data Drivenness Process Capability Determination Model (DDPCDM) based on the ISO/IEC 330xx family of standards. The proposed model enables organisations to determine their current management capabilities, derivation of a gap analysis, and the creation of a comprehensive roadmap for improvement in a structured and standardised way. DDPCDM comprises two main dimensions: process and capability. The process dimension consists of five organisational management processes: change management, skill and talent management, strategic alignment, organisational learning, and sponsorship and portfolio management. The capability dimension embraces six levels, from incomplete to innovating. The applicability and usability of DDPCDM are also evaluated by conducting a multiple-case study in two organisations. The results reveal that the proposed model is able to evaluate the strengths and weaknesses of an organisation in adopting, managing, and fostering the transition to a data-driven organisation and providing a roadmap for continuously improving the data-drivenness of organisations.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Evaluation of Data-Driven Decision-Making Implementation in the Mining Industry

“…Gökalp et al [27] consider building DDDM and analytical capabilities further by introducing the concept of transitioning into a data-driven organisation -that is, one that bases all business decisions on insights derived from data rather than from managers' intuition. [27] state that organisations do not generate value from data science investments because of a lack of organisational management alignment and culture, rather than because of ineffective data or technologies. [27] propose that, to become data-driven, an organisation needs to improve its capabilities in five management processes:…”

“…[27] state that organisations do not generate value from data science investments because of a lack of organisational management alignment and culture, rather than because of ineffective data or technologies. [27] propose that, to become data-driven, an organisation needs to improve its capabilities in five management processes:…”

“…Strategic alignment 5. Sponsorship and portfolio management [27] provide a model to assess an organisation's capability level for each management process mentioned above. The model consists of six levels numbered zero to five, representing increasing levels of capability [27].…”

“…Sponsorship and portfolio management [27] provide a model to assess an organisation's capability level for each management process mentioned above. The model consists of six levels numbered zero to five, representing increasing levels of capability [27]. An organisation that achieves a level four rating for all five management processes has fully transitioned from an organisation that is intuition-driven to one that is data-driven [27].…”

“…The model consists of six levels numbered zero to five, representing increasing levels of capability [27]. An organisation that achieves a level four rating for all five management processes has fully transitioned from an organisation that is intuition-driven to one that is data-driven [27].…”