Industrial data governance: the basis for more efficient and sustainable production

Today's industry is facing one of the biggest challenges in its recent history. Market demands, pressure to meet climate targets, consumer demand for transparency and technological acceleration are converging in a profound transformation of the production model. This transformation is not only aimed at greater competitiveness, but also at more resilient, flexible, efficient and sustainable production.

In this context, industrial digitisation - driven by technologies such as the Internet of Things (IoT), artificial intelligence, edge computing, or cyber-physical systems - is generating massive amounts of operational, environmental and logistical data. However, the mere existence of this data does not in itself guarantee value. The key is to govern it properly, i.e. to establish principles, processes, roles and technologies that ensure that this data is reliable, accessible, useful and secure. In other words, that the data is fit to be harnessed to improve industrial processes.

This is why industrial data governance is positioned as a strategic factor. It is not just a matter of ‘having data’, but of turning it into a critical infrastructure for decision-making, resource optimisation, intelligent automation and ecological transition. Without data governance, there is no Industry 4.0. And without Industry 4.0, the challenges of sustainability, energy efficiency or full traceability are intractable.

In this article we explore why data governance is essential in industry, what concrete benefits it brings to production processes and how initiatives such as the National Industry Data Space can accelerate this transformation.
 We then analyse its impact at different levels, from the most relevant use cases to the collaborative frameworks that are emerging in Spain.

Why is data governance key in industry?

Industrial data comes from a multitude of distributed sources: IoT sensors, SCADA systems, automated assembly lines, maintenance platforms, ERP or Manufacturing Execution Systems (MES), among others. This heterogeneity, if not properly managed, can become a barrier to the integration and useful analysis of information.

Data governance overcomes these barriers by establishing the rules of the game for data capture, storage, quality, interoperability, use, protection and disposal. This enables not only operational efficiency but also long-term sustainability. How?

• Reducing operational inefficiencies: by having accurate, up-to-date and well-integrated data between systems, tasks can be automated, rework avoided, and unplanned downtime reduced. For example, a plant can adjust the speed of its production lines in real time based on the analysis of performance and demand data.
• Improving sustainability: robust data management can identify patterns of energy consumption, materials or emissions. With this information, processes can be redesigned to be more sustainable, eco-design can be applied and the environmental footprint can be reduced. Data, in this case, acts as a compass towards decarbonisation.
• Ensuring regulatory compliance and traceability: from ISO 9001 to the new circular economy regulations or the Digital Product Passport, industries must demonstrate compliance. This is only possible with reliable, traceable and auditable data.
• Facilitating interoperability between systems: data governance acts as the ‘glue’ that binds together the different technological silos of an organisation: quality, production, logistics, maintenance, purchasing, etc. The standardisation and semantic alignment of data allows for more agile flows and better informed decisions.
• Boosting the circular economy: thanks to the full traceability of a product's life cycle, from design to recycling, it is possible to identify opportunities for reuse, material recovery and sustainable design. This is supported by data that follows the product throughout its life.

What should data governance look like in the industrial environment?

A data governance model adapted to this context should include:

▸Specific roles: it is necessary to have a defined team, where everyone's responsibility and tasks are clear. Some of the roles that cannot be missing are:

• Data owners: responsible for the use of the data in their area (production, quality, maintenance...).
• Data stewards: ensure the consistency, completeness and accuracy of the information.
• Data governance team: coordinates the strategy, defines common policies and evaluates compliance.

▸Structured processes: Like the roles, it is necessary to define the various phases and operations to be carried out. These include the following:

• Classification and cataloguing of data assets (by type, criticality, use).
• Data quality control: definition of validation rules, cleaning of duplicates, exception management.
• Data life cycle: from its creation on the machine to its archiving or destruction.
• Access and security: privilege management, usage audits, traceability.

▸Organisational policies: to ensure interoperability and data quality it is necessary to have standards, norms and guidelines to guide users. Some examples are:

• Standards for nomenclature, formats, encoding and synchronisation.
• Standards for interoperability between systems (e.g. use of standards such as OPC UA or ISA-95).
• Guidelines for ethical and legally compliant use (such as Data Regulation, GDPR or environmental legislation).

This approach makes industrial data an asset managed with the same rigour as any physical infrastructure.

Industrial use cases enabled by data governance

The benefits of data governance in industry are realised in multiple practical applications. Some of the most representative use cases are:

1.Predictive maintenance

One of the great classics of Industry 4.0. By combining historical maintenance data with real-time sensors, organisations can anticipate machine failures and avoid unexpected downtime. But this is only possible if the data is governed: if its capture frequency, format, responsible parties, quality and availability have been defined.

2. Complete product traceability

From raw material to end customer, every event in the value chain is recorded and accessible. This is vital for sectors such as food, automotive or pharmaceuticals, where traceability is both an added value and a regulatory obligation. Data governance ensures that this traceability is not lost, is verifiable and meets the required interoperability standards.

3. Digital twins and process simulation

For a digital twin - a virtual replica of a physical process or system - to work, it needs to be fed with accurate, up-to-date and consistent data. Data governance ensures synchronisation between the physical and virtual worlds, and allows the generation of reliable simulation scenarios, from the design of a new production line to the optimisation of the factory layout, i.e. of the different elements within the plant.

4. Energy monitoring and emission control

Real-time monitoring of energy, water or gas consumption can reveal hidden inefficiencies and opportunities for savings. Through intelligent dashboards and KPIs defined on governed data, industrial plants can reduce their costs and advance their environmental sustainability goals.

5. Automation and intelligent quality control

Machine vision systems and machine learning algorithms trained with production data allow to detect defects in real time, adjust parameters automatically and improve final quality. Without good data quality (accuracy, completeness, consistency), these algorithms can fail or generate unreliable results.

The National Industry Data Space: key to collaboration and competitiveness

For industrial data governance to transcend the scope of each company and become a real lever for sectoral transformation, it is necessary to have infrastructures that facilitate the secure, reliable and efficient sharing of data between organisations. The National Data Space for Industry, framed within the Plan for the Promotion of Sectoral Data Spaces promoted by the Ministry for Digital Transformation and the Civil Service, is in this line.

This space aims to create an environment of trust where companies, associations, technology centres and administrations can share and reuse industrial data in an interoperable manner, in accordance with ethical, legal and technical principles. Through this framework, the aim is to enable new forms of collaboration, accelerate innovation and reinforce the strategic autonomy of the national productive fabric.

The industrial sector in Spain is enormously diverse, with an ecosystem made up of large corporations, SMEs, suppliers, subcontractors, clusters and R&D centres. This diversity can become a strength if it is articulated through a common data infrastructure that facilitates the integration and exchange of information in an orderly and secure manner. Moreover, these industrial data can be complemented with open data published by public bodies, such as those available in the National Catalogue of Open Data, thus extending the value and possibilities of reuse for the sector as a whole.

The strengths of this common infrastructure allow:

• Detect synergies along the value chain, such as industrial recycling opportunities between different sectors (e.g. plastic waste from one chemical industry as raw material in another).
• Reducing entry barriers to digitisation, especially for SMEs that do not have the resources to deploy advanced data analytics solutions, but could access shared services or data within the space.
• Encourage open innovation models where companies share data in a controlled way for the joint development of solutions based on artificial intelligence or predictive maintenance.
• Promote sectoral aggregate indicators, such as shared carbon footprints, energy efficiency levels or industrial circularity indices, which allow the country as a whole to make more coordinated progress towards sustainability and competitiveness objectives.

The creation of the National Industrial Data Space can be a true lever for modernization for the Spanish industrial fabric:

• Increased international competitiveness, by facilitating compliance with European market requirements, such as the Data Regulation, the Digital Product Passport, and sustainability standards.
• Regulatory agility and improved traceability, allowing industries to respond quickly to audits, certifications, or regulatory changes.
• Proactive capacity, thanks to the joint analysis of production, consumption, or market data that allows for the prediction of disruptions in supply chains or the demand for critical resources.
• Creation of new business models, based on the provision of products as a service, the reuse of materials, or the shared leasing of industrial capacities.

The deployment of this national data space not only seeks to improve the efficiency of industrial processes. It also aims to strengthen the country's technological and data sovereignty, enabling a model where the value generated by data remains within the companies, regions, and sectors themselves. In this sense, the National Industrial Data Space aligns with European initiatives such as GAIA-X and Manufacturing-X, but with an approach adapted to the context and needs of the Spanish industrial ecosystem.

Conclusions

Data governance is a fundamental pillar for the industry to move toward more efficient, sustainable, and resilient models. Having large volumes of information is not enough: it must be managed properly to generate real value.

The benefits are clear: operational optimization, improved traceability, a boost to the circular economy, and support for technologies such as artificial intelligence and digital twins. But the real leap forward comes when data is no longer managed in isolation and becomes part of a shared ecosystem.

The National Industrial Data Space offers this framework for collaboration and trust, facilitating innovation, competitiveness, and technological sovereignty. Investing in its development means investing in a more connected, intelligent industry that is prepared for the challenges of the future.

Content prepared by Dr. Fernando Gualo, Professor at UCLM and Data Governance and Quality Consultant. The content and point of view reflected in this publication are the sole responsibility of its author.