Data-Driven Transformation to Green Steel

Abstract

Steel manufacturers are under increasing pressure to reduce emissions and operate more sustainably. This transition requires strategic decisions based on reliable, consistent, and traceable data. This thesis investigates how large industrial companies like Tata Steel Nederland (TSN) can improve data governance to support that goal. The study focuses on improving reporting and decision-making processes that rely on shared data across departments.

The thesis begins with a literature review that highlights key gaps in current research. Eight core data governance criteria were derived from the challenges to structure the findings: data lifecycle management, interoperability, data classification, data security & compliance, data unit responsibility, version control, data storage, and performance monitoring. These criteria were drawn from academic literature and refined based on the steel industry context. While the steel industry has used data to optimize processes and monitor performance for decades, little attention has been paid to how this data is governed. Studies often focus on real-time production data but overlook the governance of supporting documents for sustainability disclosures, regulatory reports, and investment proposals. The role of data governance in aligning technical, regulatory, and operational reporting remains underexplored. Especially with new regulations like the Corporate Sustainability Reporting Directive, the need for structured, transparent data governance has become urgent.

To explore this, the study uses a qualitative case study at TSN’s Transformation Office. This team coordinates strategic reporting, including the Green Book, a report used to secure investment for the transition to green steel. Data was collected through interviews, document analysis, and observations during a five-month internship. The analysis showed that governance practices at TSN are often inconsistent, with unclear document versions, missing responsibilities, and scattered file storage. Most issues are not technical, but organizational.

To address these challenges, the thesis presents practical recommendations for each of the eight criteria. These include using consistent versioning, labeling files with classification levels, assigning responsibility for key data elements, and creating shared templates. The recommendations are designed to work within TSN’s existing systems and routines. They aim to enhance clarity, coordination, and accountability across teams without requiring the implementation of major new technologies. Importantly, the recommendations emphasize that lasting change depends on three key factors: top-down prioritization, practical bottom-up training, and alignment among the eight data governance criteria.

To test the recommendations, a focus group was held at the end of the study. One recommendation per criterion was discussed. Most were confirmed as useful, especially when aligned with team behavior and leadership support. Six out of eight recommendations were seen as transferable to other industries with similar data challenges. However, data unit responsibility and performance monitoring were not confirmed as broadly applicable.

This thesis contributes to the growing conversation about how data governance can support sustainability in heavy industry. It shows that better governance improves data quality, which in turn strengthens strategic reporting and decision-making. While the recommendations are tailored to TSN, many of the insights also apply to other data-intensive organizations. The findings highlight that successful change depends not only on tools and frameworks but on leadership, shared responsibilities, and practical skills.