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Communications of the Association for Information Systems

Author ORCID Identifier

Julia Kotlarsky: https://orcid.org/0000-0002-1478-549X

Ilan Oshri: https://orcid.org/0000-0001-6577-1795

Abstract

Nowadays organizations are often inspired to become data-driven enterprises that continuously and systematically use data (from internal and external sources) to inform strategic decision-making and actions to improve operational efficiencies, products, services, customer experience, and competitiveness. Such use of data implies that organizational actors across different organizational functions are making decisions based on information derived from analytical insights. Yet, many companies find the journey to becoming a data-driven enterprise a bumpy ride. A key issue faced by most firms is that their data management approach is not ready for data and analytics-based decision-making. In this teaching case, we follow the journey of an industrial enterprise, PETRONAS, attempting to transform into a data-driven enterprise. We report on four key data-driven ‘blockers’ encountered by PETRONAS Downstream and provide insights into steps taken to address these data challenges. This case study is suitable for courses that explore digital transformation and data management in an organizational context, from strategic and operational perspectives. For example, instructors teaching a Digital Transformation course might use this case for a session dedicated to learning about data-readiness for digital transformation. It is intended for postgraduate students (Master and MBA levels) as well as late-stage undergraduate students. Specific angles that could be explored in depth are:

  • identifying and capturing data-related requirements and issues from the perspectives of different organizational actors;
  • examining interdependencies between functional and technical aspects of data management and use (e.g., requirements, availability, sustainability of data supply) for fast and reliable decision-making;
  • sourcing of data: evaluating in-house and outsourced approaches for data sourcing and management;
  • implementation of data-related projects and change management.

DOI

10.17705/1CAIS.05508

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