14 August 2025
Enterprise Data Maturity Assessment (DMA) for a Multinational in the Manufacturing Sector
Enterprise Data Maturity Assessment (DMA) for a Multinational in the Manufacturing Sector
Challenge
A decentralised multinational manufacturing organisation faced the challenge of each entity managing its own data initiatives independently, without a consistent strategic approach. The central data team, lacking direct authority, functioned primarily as a provider of data services whilst navigating a landscape of fragmented and unaligned efforts. To unlock the organisation’s full data potential and enable informed decision-making at scale, a structured approach to assess and harmonise maturity levels across entities became essential. The organisation needed a central framework that could not only evaluate existing practices but also act as a catalyst for cultural and operational alignment across business entities.
Solution
An Enterprise Data Maturity Framework (DMF) was developed in close collaboration with the central data office and key business stakeholders to strengthen existing data disciplines and introduce new ones, including AI Risks, AI Operating Model, Data Product Management, and IoT. The framework was designed to reflect both global strategic priorities and local operational realities, ensuring relevance across all entities.
A carefully designed DMA process ensured participants could provide accurate, well-considered responses that reflected the true maturity of their organisations. We embedded best-in-class facilitation methods and interactive workshops to drive engagement, align interpretations, and foster shared understanding of data capabilities across technical and business teams
To promote clarity and consistency, a comprehensive glossary of over 200 terms was created. This common language significantly reduced ambiguity, improved comparability of results, and laid the foundation for a unified data culture, while improving data literacy.
The framework was tested and refined through four pilot assessment sessions, enabling continuous improvement before full-scale deployment. Feedback loops from these pilots allowed us to fine-tune both the questions and the logic model behind the DMA, ensuring robustness, scalability, and executive buy-in. To ensure data integrity and reliability of insights, we also conducted a dedicated validation phase to review results and identify potential outliers. Individual follow-up sessions were held with participants whose responses diverged significantly from the group average to confirm whether these represented genuine differences in maturity or input inconsistencies. This process strengthened confidence in the results and enhanced the credibility of the DMA outcomes.
Benefits
The assessment process uncovered valuable insights for decentralised entities, both during the engagement and upon receiving tailored recommendations, forming the foundation for future data roadmaps. Leaders gained unprecedented visibility into organisational strengths and weaknesses, enabling data-driven prioritisation of initiatives and investments.
A unified framework was established, aligning decentralised data practices with central capabilities and ensuring consistency across the group. This alignment transformed the central data team from a service provider into a strategic enabler. Ultimately, the DMA became a cornerstone of the company’s global data strategy, now used to monitor maturity evolution, guide transformation programmes, and foster a shared vision of value creation through data.
Practical Applications
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Use maturity scores by role (e.g., Technical, Business) to target capability-building programmes for specific audiences.
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Compare maturity scores by function to identify departments requiring priority support.
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Track progress towards maturity targets across key activity areas such as Data Governance, Data Technologies, and Data-as-a-Service.
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Monitor maturity by stream to ensure balanced development across data strategy, governance, technology, and scalability.
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Leverage radar chart insights to address gaps in specific competencies, such as Data Literacy or Data Product Management.
