<\/p>\n\n\n\n
Artificial intelligence is growing at an accelerated pace in Italian companies, but the race for technological adoption has left behind a fundamental prerequisite: data governance. This article explores what industrial data governance truly means and why its absence is currently the primary cause of failure for AI initiatives in production.<\/p>\n\n\n\n
Starting from the Italian context, where the dualism between rapid adoption and delayed governance is clear, we then analyze the European regulatory framework (Data Governance Act, Data Act, and AI Act) that is transforming governance from a best practice to a progressively binding requirement. We examine the investment directions of the most advanced companies, the critical issues that remain open, and an operational four-step path, inspired by Lean Thinking, to initiate governance incrementally and measurably. In conclusion, we reflect on how data, when well-governed, is the strategic enabler of servitization and competitive advantage in the industry of the future.<\/p>\n\n\n\n
In 2025, the artificial intelligence market in Italy reached 1.8 billion euros, with growth of +50% in just one year. During the same period, 71% of large companies had already launched at least one active AI project. Yet only the 9% from those same companies<\/strong> can declare that they have structured AI governance processes \u2014 and only the 24% says it is satisfied with the quality of its data<\/strong>.<\/p>\n\n\n\n It's not a paradox; it's an exact photograph of what happens when you run faster than your ability to control. And in a manufacturing context \u2014 where data originates from machines, sensors, ERP, MES, and SCADA systems with different standards and heterogeneous sources \u2014 this gap between adoption and governance becomes a concrete operational risk, even before it's a compliance issue.<\/p>\n\n\n\n In the book \u201c25 years of Lean Thinking the Italian way<\/em>\u201c<\/a>, Michele Bonfiglioli writes a sentence that serves as a manifesto: \u201cAll these tools are worthless without a reliable database and centralized governance. Without consistent data, any system becomes a sham.\u201d<\/em> This article develops that intuition: what is industrial data governance, where are the most advanced companies investing, what are the organizational bottlenecks, and what European regulatory framework is reshaping the rules of the game for the manufacturing industry<\/strong>.<\/p>\n\n\n\n Industrial data governance is a Organizational discipline<\/strong>, ..., not a technology. It does not coincide with the purchase of a data catalog, the installation of a data lake, or the deployment of a master data management platform. It is a set of policies, roles, processes, and standards <\/strong>that define who owns the data, who guarantees its quality, how it is classified, who can access it, and, above all, who is responsible when something is wrong.<\/p>\n\n\n\n In production, this translates into concrete questions: Do the efficiency data coming out of the MES and those entering the ERP tell the same story? If a plant operator and a headquarters analyst look at the same line indicator, do they see the same number? When a supplier sends quality data for a batch, is it integrated consistently with the assembly process data, or does it end up in a parallel Excel sheet?<\/p>\n\n\n\n If the answer is \u201cit depends,\u201d \u201cnot always,\u201d or \u201cI don't know,\u201d industrial data governance is an open problem. And as confirmed by\u2019Bonfiglioli Consulting's experience in digital transformation projects<\/a>, this problem is among the leading causes of failure for AI and analytics initiatives: not because the algorithms are wrong, but because the data they work on is ungoverned.<\/p>\n\n\n\n One of the most common digital wastes in manufacturing companies is Data redundancy<\/strong>the same information replicated across multiple systems with different definitions, asynchronous updates, and no single authoritative source. Michele Bonfiglioli explicitly identifies it among the change<\/em> digital waste \u2014 the new waste in the era of the connected industry: automating a useless task means digitizing waste; replicating inconsistent data across multiple platforms means amplifying the error.<\/p>\n\n\n\n The breaking point is organizational before it is technical.<\/strong>. It requires the definition of three roles that are still missing in most companies: the data owner<\/strong> (domain or process data owner), the data steward<\/strong> (Daily Quality Operations Manager) and the Data Product Owner<\/strong> (responsible for using that data as a business asset). According to research from MIT Sloan Management Review, organizations that assign formal data ownership achieve significantly better results in analytics and AI projects compared to those that treat governance as a collateral IT activity, and identify clarity of roles as the main enabler, even before technological tools.<\/p>\n\n\n\n This alignment between organizational responsibility and data quality is exactly what the model Lean World Class\u00ae<\/strong> by Bonfiglioli Consulting<\/a> This also applies to the digital perimeter: first, operational responsibilities are structured, then digitalization takes place, never the other way around.<\/p>\n\n\n\n Italy presents a significant dualism. On the front of technological adoption, ISTAT data 2025<\/a> The data show that 38.1% of companies with at least 10 employees have reached a high or very high level of digitization, with the manufacturing sector among the most active in investing in IoT, automation, and robotics. On the governance front, the picture is less robust.<\/p>\n\n\n\n L\u2018Artificial Intelligence Observatory of the Polytechnic University of Milan<\/strong><\/a> notes that, despite an AI market that is projected to grow by 50% by 2025, only 9% of large Italian companies have structured AI governance processes. 54% are in the process of establishing such processes, though no specific deadlines have been set yet. 19% of workers report using only AI tools approved by their company \u2014 which means that the vast majority use uncontrolled tools.,<\/strong> with company data circulating outside any governance perimeter. In manufacturing, where data relates to processes, design, and the supply chain, this dispersion is a real risk.<\/p>\n\n\n\n This is compounded by the fact that\u2018Politecnico di Milano Data Management Observatory<\/strong>:<\/a> Only 24% of Italian companies report being satisfied with the quality of their data, with many falling below average in terms of data governance and their ability to calculate the business value of their information assets. Investing in sensors, MES, and IoT platforms without establishing data quality governance means\u2014to use the metaphor from the book\u2014 \u201c25 years of Lean Thinking the Italian way<\/em>\u201c<\/a>, build a sophisticated navigation system on a map full of errors.<\/p>\n\n\n\n Europe has built a regulatory framework in just a few years that transforms data governance The best practices for progressively binding requirements<\/strong>. For manufacturing companies, three measures define the scope of action.<\/p>\n\n\n\n The Data Governance Act<\/strong> DGA<\/a>, effective from September 2023, establishes the conditions for secure and trustworthy data sharing between businesses, sectors, and EU countries. It introduces certified data intermediaries and creates the framework within which sectoral Data Spaces are built\u2014controlled sharing spaces that allow industrial supply chains to collaborate on data without losing information sovereignty.<\/p>\n\n\n\n The Data Act<\/strong>, effective September 2025, introduces a fundamental principle for connected factories: the entity that generates data through IoT devices and connected machinery has the right to access<\/strong> to that data and to share it with third parties on fair terms. This redefines the relationship between machine manufacturers and users: a manufacturing company that uses connected equipment can claim access to operating data, even when the machine manufacturer would prefer to keep it for commercial reasons. This is a turning point that, if properly exploited, opens up new possibilities for optimization and servitization.<\/p>\n\n\n\n L'AI Act<\/strong>, the world's first law on artificial intelligence, provides to Article 10 <\/a>Specific data governance requirements for high-risk AI systems: datasets must be documented, verified, representative, and free of critical bias, with management practices ensuring integrity and relevance. Full applicability for high-risk systems took effect in August 2026. For companies using AI in critical manufacturing contexts\u2014predictive maintenance, automated quality control, production planning\u2014compliance is no longer deferrable.<\/p>\n\n\n\n Industrial data governance investment directions in the 2025-2026 biennium focus on three main axes.<\/p>\n\n\n\n Governance embedded in operational workflows.<\/strong> The traditional model\u2014governance as a separate layer, often managed by IT as a post-script audit activity\u2014is giving way to an approach where data quality, classification, and access rules are embedded directly into workflows. The principle is that Governance that is not continuous is not governance<\/strong>: Quality controls must take place at the moment the data is generated or transformed, not downstream. The global data governance market, valued at $4.60 billion in 2026, is growing at a CAGR of 16% through 2031, with Europe recording the highest rate\u2014estimated at 20% annually\u2014and Germany as the dominant market on the continent. These figures do not merely reflect the growth of the software sector: they signal an awareness that industrial data is a strategic asset<\/strong> which requires the same level of oversight as that used to manage physical facilities.<\/p>\n\n\n\n Data Space for Manufacturing.<\/strong> The program The European Commission's "Digital Europe" initiative<\/a> has funded the construction of industrial data spaces dedicated to manufacturing, with funding of up to 3 million euros per project, designed to enable companies to share data along the supply chain\u2014among OEMs, suppliers, service providers, and customers\u2014while maintaining control over their information sovereignty. The framework Gaia-X<\/strong> f<\/a>It provides the technical architecture to build these spaces in a federated and interoperable manner, based on the principles of self-description, sovereignty, and verifiable trust. For Italian manufacturing SMEs, participation in these data-sharing ecosystems is not just a technological opportunity: it is a competitive advantage within the European supply chain.<\/p>\n\n\n\n AI Data Governance.<\/strong> As the number of AI projects in production increases, the specific governance of the data used to train and validate models becomes a priority in its own right. This includes managing the provenance of training data, documenting biases, ensuring the traceability of dataset versions, and certifying the quality of inputs. In a manufacturing company that uses predictive models for quality or maintenance, ungoverned training data can lead to incorrect automated decisions\u2014and to liability that is difficult to assign after the fact.<\/p>\n\n\n\n System silos.<\/strong> ERP, MES, PLM, CRM, and SCADA speak different languages, with misaligned definitions of the same entities: order, product, machine downtime, defect, and batch. The result is digital silos\u2014that is, systems that manage data related to the same process but lack a shared model\u2014which necessitate manual reconciliations, leading to wasted time and an increase in errors.<\/p>\n\n\n\n Lack of clear ownership.<\/strong> When no one is formally responsible for a piece of data, that data gradually deteriorates. Empty fields, outdated values, and non-standardized codes accumulate without anyone having the authority or incentive to correct them. Data governance requires a formal assignment of responsibility, with clear roles and measurable KPIs.<\/p>\n\n\n\n Reactive governance rather than preventive governance.<\/strong> Most companies discover data quality issues when they launch an AI or advanced analytics project\u2014that is, when it is too late to clean up the data in a timely manner without compromising the project. Integrating quality controls into daily operational processes is still rare but crucial to the sustainability of any advanced digital initiative.<\/p>\n\n\n\n Shadow AI as a new governance risk.<\/strong> An increasingly significant concern is \u201cShadow AI\u201d: the use of artificial intelligence tools not approved by the company\u2014often via personal accounts or external services\u2014which introduces untraceable data flows and renders traditional governance ineffective. This is not just a cybersecurity issue: when technical documents, process data, commercial information, or industrial know-how are entered into tools outside the company\u2019s perimeter, the company loses control over where that data ends up, who processes it, and what confidentiality safeguards are in place. In this sense, Shadow AI is a form of silent erosion of the single source of truth and compels industrial data governance to monitor not only systems and platforms but also everyday usage behaviors.<\/p>\n\n\n\n Latest Developments in Italian Legislation.<\/strong> The recent implementing decrees on AI confirm that the governance of artificial intelligence is now an issue that is not only technological, but also organizational and related to responsibility. The message coming from the Italian framework is clear: AI must be managed using an integrated system <\/strong>compliance, cybersecurity, data protection, vendor control, training, documentation, and traceability. For manufacturing companies, this means that data governance is no longer an accessory support for digital projects, but a condition for legitimacy and operational continuity.<\/p>\n\n\n\n Difficulty in demonstrating value.<\/strong> Data governance is an investment with returns that are not immediately visible, making it vulnerable to budget cuts. Building a value narrative\u2014how much incorrect data costs in a production decision, how much reliable data is worth in a servitization contract\u2014is a managerial skill that is still underdeveloped.<\/p>\n\n\n\n Compliance and governance as separate silos.<\/strong> In many organizations, data management is handled by the legal\/compliance department for GDPR-related reasons, separate from the operational governance of industrial data. With the AI Act directly linking data quality to the legal liability of the AI system that uses it, these two aspects can no longer coexist in silos: they must converge into a single, integrated framework.<\/p>\n\n\n\n The Lean Thinking <\/a>\u2014 which Bonfiglioli Consulting has applied for over 25 years in manufacturing operations \u2014 also offers a fundamental operating principle for data governance: First, eliminate waste, then standardize, then continuously improve.<\/strong> Applied to data, it means not starting with an 18-month enterprise program, but with an incremental approach focused on measurable value from the very first cycle.<\/p>\n\n\n\n The path is divided into four sequential steps. The first is the critical domain mapping<\/strong>identify which data are fundamental for the most relevant decisions\u2014product quality, line efficiency, supplier reliability, planning accuracy. The second step is definition of minimum quality rules<\/strong> For those domains: completeness, accuracy, timeliness, consistency, calibrated to a sufficient level for the decisions those data must support, not to an ideal of statistical perfection.<\/p>\n\n\n\n The third step is the\u2019formal assignment of ownership<\/strong>For each critical domain, who is the data owner? Who is the data steward? The answer must be nominative, with measurable quality KPIs and visibility in reporting. The fourth step is the construction of a minimum data catalog<\/strong>An inventory of data assets with metadata, source, update frequency, owner, and certified quality level. Discipline is worth more than the tool: a well-governed shared registry produces more value than a sophisticated abandoned platform.<\/p>\n\n\n\n Operational metrics for monitoring maturity over time include: Data quality score per domain<\/em> (percentage of records compliant with defined rules), the Average anomaly resolution time from detection to correction at the source<\/em>, and the percentage of critical decisions supported by certified data<\/em>. These indicators transform governance from an abstract activity into a measurable process, with visibility in operational reporting. <\/p>\n\n\n\n Industrial data governance is not just a factor for internal efficiency. It is also the strategic enabler of the business model that the book \u201c25 years of Lean Thinking the Italian way<\/em>\u201c<\/a>, identifies as the competitive frontier of Industry 5.0: the servitization<\/strong>. Companies that sell not just products, but \u201cperformance as a service\u201d \u2014 guaranteed plant availability, certified output quality, energy efficiency as an SLA contract \u2014 need governed data not only within the factory, but along the entire value chain, including the end customer.<\/p>\n\n\n\n This expands the perimeter of governance: from internal data to data that flows between companies and customers through connected machinery, predictive platforms, and service level agreements. The value of data increases when it is securely and governedly shared \u2014 and this is the promise of European Industrial Data Spaces, where trust in the quality and origin of shared data is the condition for building ecosystems of competitive collaboration.<\/p>\n\n\n\nWhat is industrial data governance (and why it's not an IT project)<\/strong><\/h2>\n\n\n\n
The single source of truth problem<\/strong><\/h2>\n\n\n\n
Italian context: rapid adoption, lagging governance<\/strong><\/h2>\n\n\n\n
The European regulatory framework: governance becomes mandatory<\/strong><\/h2>\n\n\n\n
Where do the most advanced companies invest<\/strong><\/h2>\n\n\n\n
\n\n\n\nThe Critical Points<\/strong><\/h2>\n\n\n\n
The Lean Method Applied to Data Governance: Where to Start<\/strong><\/h2>\n\n\n\n
Data Governance and Servitization: Value Beyond the Factory<\/strong><\/h2>\n\n\n\n