Accelerating Innovation Culture: A Data-Driven Transformation Journey Across Complex Organizations

Abstract This paper aims to explore how a large, integrated multinational group of organizations can systematically assess and enhance its innovation culture. The objective is to identify key barriers and enablers of innovation using data-driven methods, and to propose a scalable framework that supports cultural transformation across complex, multi-disciplinary and diverse environments. A strong innovation culture is positioned as a critical enabler for successful transformation in the energy and petrochemical industries. A comprehensive survey was designed to capture both qualitative and quantitative insights into the current state of innovation culture. The survey included open-ended, ranked, and scaled questions, and was distributed across all sub-companies of the corporate group, encompassing a wide range of business units and seniority levels. Data calibration ensured demographic representativeness. Analytical methods included exploratory data analysis, Total Unduplicated Reach and Frequency (TURF) analysis, and Performance-Importance Matrices (PIMs). Open-ended responses were thematically coded to extract conceptual insights. The findings informed a strategic roadmap supported by inclusive initiatives such as innovation weeks, hackathons, and continuous learning platforms. The analysis grouped innovation barriers into three overarching categories - leadership (e.g., lack of resources, inconsistent support), organizational (e.g., unclear accountability, inefficient decision-making), and practical (e.g., fear of failure, weak collaboration) - drawing on established academic frameworks for understanding innovation challenges. The most significant barrier was fear of failure which if lowered in the organization would have the highest impact and greatest reach and therefore identified as a high priority improvement area. Key enablers included leadership support, acceptance of failure, resource allocation, and cross-functional collaboration. These insights led to the development of a multi-year implementation plan emphasizing inclusivity, targeted problem-solving, and agility. The approach demonstrates how data science can be leveraged to diagnose cultural dynamics and prioritize interventions that foster innovation. The framework is adaptable and can be applied across industries seeking to embed innovation as a core organizational capability. This paper introduces a replicable, data-driven methodology for diagnosing and enhancing innovation culture. It bridges employees and groups development with data science, offering actionable insights and scalable strategies that can benefit transformation efforts across the petrochemical and energy sectors.