The paradox of industrial data: abundant, yet dormant

The term industrial data represents a strategic challenge for all manufacturing companies, especially SMEs and mid-caps. While sensors, MES/MOM systems and Industry 4.0 initiatives are multiplying, most of the data generated in factories remains either unusable or unused. According to Splunk, 55% of enterprise data is “dark data”—unused, even though it is a lever for competitiveness, productivity, and sustainability. This article offers a factual analysis to alert industrial decision-makers to this untapped potential, identify its causes, and present concrete value creation levers, through use cases and strategic recommendations. Dillygence, an expert in industrial performance, shares here the keys to turning raw data into business value.

1) Key figures: underexploited industrial data

The reality of “dark data”

Modern factories generate millions of data points daily: IoT sensors, quality histories, process logs, maintenance reports, logistics data, etc. Yet, 55% of this data is never exploited (Splunk), representing a considerable missed opportunity for performance and innovation. In industrial SMEs, this rate even rises above 60%, highlighting an increased difficulty in turning raw data into operational leverage. This accumulation of “dark data” hinders quick decision-making, responsiveness to disruptions, and the ability to anticipate deviations. Additionally, information dispersion between departments and tools worsens the problem, making access to useful data more complex. Another concerning finding: according to McKinsey, between 30 and 40% of reports produced daily in factories are considered barely usable or redundant, resulting in an information overload with no real added value for operations.

Loss of competitiveness: impact on industrial SMEs and mid-caps

According to McKinsey, SMEs and mid-caps leverage only 10 to 20% of their data potential, which results in productivity losses, low responsiveness and anticipation, difficulty optimizing flows, and higher operational costs. Day-to-day management overwhelms decision-makers, who neglect in-depth data analysis. Every unexpected stop, untraced scrap, or non-optimized cycle erodes profitability. This underuse of industrial data not only prevents rapid anomaly detection but also blocks the identification of improvement opportunities along the value chain. Leaders then miss opportunities to adjust their strategies, cut costs, and increase production capacity. Finally, the lack of data valorization limits access to advanced decision-support tools and slows technological integration, yet a key factor for the competitiveness of industrial sites.

2) Breaking down the structural barriers to industrial data exploitation

System fragmentation: IT, OT, and ET silos

Fragmentation between IT systems (information systems), OT (“Operational Technology”: machines, etc.), and ET (“Engineering Technology,” engineering, R&D) prevents joint exploitation of data. Formats are heterogeneous, interfaces often incompatible, and there are few inter-system connectors, which slows integration and data flow valorization. This technical and organizational dissociation causes inefficiencies, duplicate information, and limits the ability to cross-reference shop floor data with strategic analyses. According to Verdantix, 68% of French industrial companies struggle to harmonize IT/OT/ET data flows, hindering quick decision-making and overall process optimization.

Lack of governance and no clear strategy

Few industries have a formal data governance plan: no repositories, taxonomies, or structured “data lineage.” Initiatives remain ad hoc and unsustainable, data quality is rarely assured, and fewer than 30% of industrial companies have real governance (Verdantix). Without a structured approach, industrial data remains scattered, unreliable, and underused. This complicates traceability, hinders the integration of new technologies, and limits the ability to generate reliable predictive analytics. Establishing robust governance, based on common repositories and quality control processes, becomes a strategic lever for turning data into a competitive advantage and accelerating value creation across the entire industrial chain.

Limited resources and production-driven priorities

Human and financial resources are often limited in industry, leading to constant trade-offs. Day-to-day operations focus most efforts on maintaining production, handling emergencies, and solving operational problems, relegating digital transformation and the exploitation of industrial data to the background. Only 12% of industrial SMEs put digitalization at the core of their strategy according to McKinsey, a sign of priority given to business continuity over process innovation. There is little time for deep data mining, funding is often tied up in equipment, and data management skills are rare. As a result, a data-driven culture is yet to be established, hindering advanced management and intelligent automation initiatives.

3) The 3 families of data with the highest value creation

This trio corresponds to the components of OEE (availability, performance, quality), recognized as the most decisive KPI for measuring “smart factory” success by 86% of manufacturers, according to IoT Analytics. OEE provides a global view of productive efficiency, integrating downtime reduction, yield optimization, and quality improvement, each axis relying on relevant use of industrial data. This structured approach promotes team alignment around measurable goals, technological investment prioritization, and ongoing improvement of industrial competitiveness.

Availability: downtime, MTBF, and stoppage reduction

The “availability” pillar covers downtimes, MTBF (mean time between failures), and causes of stoppages. Leveraging this industrial data, from sensors and tracking systems, makes it possible to analyze logs in detail, study breakdown histories, cross-reference incidents with production cycles, and use maintenance indicators to precisely target machine park weak points. A structured approach streamlines identification of critical equipment, prioritizes interventions, and automates preventive maintenance alerts. This approach leads to a significant reduction in unplanned downtime: systematic exploitation of product/process data can cut downtime by 30 to 50% (McKinsey). By leveraging event logs, MTBF/MTTR tracking, root cause analysis (failures, micro-stops, changeovers), and standardized stoppage logs with reliable timestamping and cause taxonomy, it becomes possible to link this information to process conditions and target corrective actions. Analytics tools, as highlighted by Oracle, play a key role in reducing unplanned downtime and consolidating operational reliability.

Performance: cycle time, throughput, and WIP management

Industrial data related to cycle time, throughput, and WIP (work-in-progress) are precise levers for identifying bottlenecks, balancing production flows, and maximizing the overall output of a plant. In-depth analysis of these indicators, combined with real-time monitoring solutions, provides immediate visualization of saturation points, dynamic adjustment of rates, and optimized planning. By using real-time OEE indicators, refining scheduling, and reducing waiting and idle times, manufacturers achieve measurable productivity gains of 10 to 25%. Mastery of this data accelerates operational decision-making while promoting adaptability to demand fluctuations.

Quality Data, defects & scrap

Scrap and repair rates, defects by workstation or production batch, inspection results (machine vision, automated metrology), defect paretos, and precise tracking of non-conformities are the foundation of industrial quality data. Detailed analysis of these indicators quickly identifies risk areas, targets corrective actions, and optimizes control processes across the entire production chain. Correlating process parameters (temperatures, speeds, torques, recipes, pressures, environment) with quality indicators feeds root-cause analytics models and AI-assisted visual inspection. This approach enables earlier defect detection, automated sorting, significant scrap reduction, and improved product compliance. Industry studies frequently report defect reductions thanks to intelligent use of industrial data (DataGalaxy).

Common data model: foundation for continuous improvement and traceability

Structuring a common data model, based on business taxonomies, harmonized repositories, and reliable multi-site traceability, forms the backbone for orchestrating continuous improvement and deploying advanced projects such as predictive maintenance, digital twins, or consolidated performance analysis. This model streamlines team alignment, indicator standardization, and consolidation of industrial data at group level. It ensures tool interoperability, fosters long-term data valorization, and accelerates the ability to manage and benchmark multiple industrial sites in real time. Without this common architecture, the reliability of analyses and the speed of transformation remain limited, hampering digital maturity and the ability to generate lasting gains across the industrial value chain.

4) Concrete use cases

Predictive maintenance

Through automated analysis of breakdown histories, sensor feedback, and artificial intelligence, predictive maintenance (PdM) transforms industrial management by providing increased visibility into equipment status. Industrial data from IIoT systems in particular enables anticipation of incidents, targeted scheduling of interventions, and optimized resource allocation. Anticipating failures, more precise planning, and reducing corrective maintenance have led at Siemens to a 20% drop in unplanned downtime, increased line availability, and a significant decrease in operating costs. This data-driven approach supports the transition to a sustainable, competitive industry.

AI vision for quality: improved detection and lower scrap

The combination of AI-powered automated inspections and intelligent use of industrial quality data enables 98% defect detection and scrap reductions of 15 to 40%, according to Voxel51 and UnitX Labs. This approach relies on detailed analysis of non-conformities, real-time monitoring of scrap, batch traceability, and ongoing improvement of detection algorithms to secure production and minimize losses.

MES/MOM: measurable productivity gains and line optimization

The deployment of MES/MOM platforms structures the exploitation of industrial data, synchronizes production, and optimizes processes throughout the site. Thanks to multi-level centralization and traceability, these solutions enable precise tracking of work orders, real-time performance analysis, and rapid identification of bottlenecks. Automating repetitive tasks, integrating with ERP, and continuous improvement of industrial flows drive +22% productivity and +19% net margin according to industry studies (Oracle, McKinsey), while consolidating reliability and profitability of operations.

Proven ROI: quantified examples of successful implementations

With data-driven management, manufacturers achieve tangible, measurable results: notable EBITDA growth through flow and resource optimization, significant reduction in operating costs, 50% drop in unplanned downtime, and a 12% EBITDA increase in the rail sector (McKinsey). In addition, a 25% reduction in operational costs has been observed in the automotive sector, achieved by implementing integrated data governance and adopting modern MES (Oracle). These results illustrate the power of industrial data to drive lasting performance and profitability transformation.

5) What the deployed technologies reveal

MES/MOM: integrated management of industrial processes

MES/MOM solutions offer a global, integrated view of all manufacturing processes. They guarantee detailed traceability at every level, enable instant performance analysis, and interface with ERP and OT infrastructures. These systems gather industrial data, streamline flow coordination, optimize production order tracking, and allow bottleneck identification without delay. Concrete results are observed: unit manufacturing cost reduced by +22.5%, net margin up +19.4%, and on-time delivery rate improved by +22%. (netsuite.com)

PdM with IIoT and AI: condition-based and predictive maintenance

IIoT and AI enable the shift from reactive to predictive maintenance through sensors, machine learning, and analytics to anticipate and optimize intervention plans. The effects are immediate: lower costs, optimized equipment lifespan, –25% maintenance costs, and –50% unplanned downtimes. (McKinsey)

DataOps and IDM governance: quality and interoperability

DataOps approaches and industrial data management (IDM) ensure rigorous organization of flows, the development of suitable taxonomies, supervision of the data journey (“lineage”), and controlled access management. This structuring enhances the security and concrete valorization of data for company performance.

Interoperability and monetization: keys to digital transformation

Interoperability enables data sharing between sites, accelerates multi-factory benchmarking, and opens the way to value creation with partners, genuine accelerators for industrial digital transformation. Cross-site sharing and external valorization thus become major assets.

6) What should decision-makers remember?

Prioritize high-impact OEE use cases

Identify use cases related to availability, performance, and quality, based on a precise analysis of existing industrial data. Map data sources, assess their level of exploitation, and spot quick wins generating high ROI. This structured approach helps build a clear OEE roadmap, prioritizing high-value projects, securing investments, and efficiently mobilizing teams around objectives.

Structure governance: robust taxonomies and models

Data governance requires defining robust business taxonomies, implementing common repositories and effective monitoring tools, as well as formalizing clear processes for managing, ensuring quality, and securing industrial data. Involve IT, OT, and business teams in the process to guarantee coherence, reliability, and traceability. This structure ensures data availability for analysis, facilitates the integration of new technologies, and supports continuous improvement across the entire industrial site. Involving all stakeholders promotes strategic alignment and accelerates digital transformation, while ensuring regulatory compliance and lasting data valorization.

Adopt an agile approach: pilot phase, validation, and scaling up

Start with a pilot, validate the results, adjust if necessary, then scale up across the site or group. This method—testing before generalizing—secures adoption, maximizes impact, limits risks, and gradually involves teams around industrial data. It also encourages capitalizing on early feedback, identifying quick wins, and prioritizing investments. Working in successive iterations ensures better change management, ongoing skill development, and sustainable adoption of data-driven methods for industrial performance.

Measure ROI to demonstrate the business value of data projects

Each project should be evaluated with relevant indicators and transparent reporting, in order to objectively measure its impact on industrial performance. Track business KPIs using dynamic dashboards, comparing cost, output, and lead time before and after leveraging industrial data. Regular analysis of these results makes it easier to quickly identify areas for improvement and justify investments to stakeholders.