Data & Industrial ROI: 5 Fatal Mistakes to Avoid

The 5 Fatal Errors in Production Data Collection and Their Measurable Impact

Data is now emerging as the driving force behind industrial performance. Present at every stage, from quality to logistics, it enables the optimization of maintenance, planning, and decision-making.

Poorly collected or poorly exploited data generates hidden costs: slower ramp-up, bottlenecks, loss of resources on low value-added tasks. Often, failure results not from technology, but from a lack of methodology, strategic misalignment, or poor management of the data lifecycle.

To generate high industrial ROI, information flows must be structured rigorously, teams must be engaged, and every piece of data must be translated into concrete action. Industrial directors and site managers benefit from considering data as a strategic asset, a driver of innovation and resilience. Embarking on this path means turning every challenge into an opportunity for growth and profitability for the plant.

Error 1: Lack of Strategic Vision and Forgotten Financial Alignment

Defining precise objectives before collecting data is key to boosting industrial ROI. Accumulating indicators without questioning their relevance leaves the manufacturer in a fog of indecision. Too many industrial sites multiply KPIs for the sake of exhaustiveness, to the detriment of real performance: reducing non-quality costs, improving margins, speeding up ramp-up, optimized stock management, controlling the carbon footprint.

This overabundance of data, rarely exploited strategically, reduces result clarity and hinders continuous improvement. The result: productivity levers remain hidden, investments are poorly prioritized, and performance management falters.

The hidden cost of poor data quality is colossal: between 15% and 25% of operating income for an average company (Source: Gartner/Forbes). This waste translates into distorted decisions, costly rework, and a loss of productivity per square meter, potentially representing millions of euros in lost margin each year.

Investing in IIoT or MES solutions without clearly defined objectives exposes the company to major risk: OEE or TRS only matter if they are aligned with the strategy. The lack of financial coherence between management and operations generates tension and wastes time.

The “Top-Down” approach is essential: identify 3 to 5 strategic objectives (financial, operational, environmental), translate them into KPIs for the shop floor, then determine which data to collect. This method turns raw data into a performance lever and generates measurable and sustainable industrial ROI. It makes every managerial level accountable and provides site directors with a roadmap validated by management, making arbitration and daily management easier.

By structuring their data project around shared objectives and coherent indicators, manufacturers turn data into a driver of growth, competitiveness, and sustainability.

Error 2: The Myth of Manual Entry and the Impact of Latency

Relying on manual records (paper, Excel) or late entries exposes the plant to significant latency and permanent inconsistency between available information and action on the shop floor. Manual entry, inherited from outdated practices and unfortunately still too common, creates delays in information transmission, omissions, and errors that accumulate day after day and team after team. At each manual step, the risk of error increases: data entry errors can reach 1% to 8% of all collected data (Source: Various Data Quality reports, American Society for Quality – ASQ). This error rate involves an invisible but real cost: time spent checking, correcting, or searching for reliable information slows decision-making and directly affects collective performance.
The consequences do not stop there: bad entries often lead to wrong decisions that reduce the plant’s actual productivity. Maintenance teams sometimes have to intervene several times to solve a poorly identified problem, operators waste time searching for information or correcting incorrect data. The time operators spend entering data, instead of producing or improving processes, is a loss of added value that is hard to recover and penalizes the plant’s overall efficiency.

This is where intelligent automation makes perfect sense. Automated collection via IIoT (sensors, PLCs, APIs) and integrated management systems (MES/ERP) reduces by 10% to 15% the indirect work time spent on data management (Source: MES/IIoT supplier case studies). Automation ensures better data reliability, traceability, and immediate responsiveness to incidents or changes in pace.
The pragmatic approach is to automate collection at all critical points: production flows, maintenance, quality, logistics. Connected sensors enable real-time data reporting, anomaly detection, and instant team alerts. Where manual entry remains unavoidable (small runs, specific contexts), it is essential to make it intuitive, quick, and mobile, with interfaces adapted to the shop floor. Industrial tablets, mobile apps, and automation solutions make operators’ lives easier, reduce errors, and speed up data consolidation.
This strategy accelerates access to (reliable) data, strengthens team responsiveness, and maximizes industrial ROI across the entire value chain. Site managers have up-to-date, actionable information for immediate adjustments, while operations teams gain autonomy and efficiency. Automating collection is therefore a profitable investment that frees up time, reduces hidden costs, and improves the accuracy of industrial analyses.

Error 3: Neglecting Data Quality and Silos

Data is incomplete, duplicated, inconsistent between systems (OT/IT), and this fragmentation severely hampers industrial efficiency. When data is not standardized, each department—production, maintenance, quality, supply chain—uses its own formats and nomenclatures, making it almost impossible to aggregate and analyze data at the plant level. This lack of standardization drastically reduces the ability to manage all operations coherently and proactively. Information stuck in silos (ERP, MES, CMMS, quality tools) limits cross-functional visibility and prevents the quick detection of performance levers, with a direct impact on industrial ROI.
The “dirtier” the data, the higher the cost of cleaning and integrating it into analytical tools (BI, AI, digital twin). This “Data Prep” time consumes up to 60% to 80% of data scientists’ work (Source: Forbes / Tech-Target). IT and data teams then spend more time consolidating, correcting, and harmonizing information than producing valuable analyses for business units. The result: delayed decisions, hindered innovation, and missed opportunities. Poor quality data exposes the company to major risks: batch rejections, product recalls, resource waste, or overstocking of raw materials—all factors that erode industrial profitability and competitiveness.
The only credible answer: establish robust Data Governance, with clear rules on quality, traceability, and information standardization. This means defining responsibilities, audit processes, and control tools to ensure each collected data point is reliable, accessible, and usable. Implementing a unified data platform—such as an industrial Data Lake or Mesh—then becomes the essential foundation for breaking silos, merging business data, and generating high-value analyses. This architecture facilitates teamwork, accelerates information flow, and fosters innovation.
This structured approach speeds up decision cycles, secures regulatory compliance, and strengthens operational resilience. Companies that invest in the quality and governance of their data ensure they stay agile, manage industrial transformation with precision, and generate sustainable financial gains. Data thus becomes a strategic asset, serving plant performance and growth.

Error 4: Resistance to Change and Forgetting the Operator

The operator is not involved; data collection is seen as administrative overhead or a policing tool. This negative perception intensifies when interface ergonomics are neglected and no targeted training is provided to explain the purpose of the initiative. Shop floor teams, often faced with rapid changes and imposed tools, struggle to understand the concrete benefits of data collection. Result: data collection becomes a constraint, causing demotivation and errors, instead of being a driver of continuous improvement.
This lack of buy-in on the floor largely explains why the failure rate for digital transformation projects exceeds 70%, mainly due to low user adoption (Source: McKinsey / Harvard Business Review). Investing in MES or IIoT systems quickly becomes a sunk cost if people are not at the heart of the project. Operators, the main actors in industrial performance, must be involved from the design phase, receive suitable training, and have intuitive interfaces.
Conversely, structured support, combining operational training, involvement in tool selection, and intuitive interfaces, can increase productivity by 5% to 10% (Source: studies on employee training and engagement). Data must first and foremost serve the operator: providing immediate feedback via dashboards, for example, gives meaning to collection and enables real-time adjustments. Digital tools that value shop floor expertise, facilitate communication, and make performance visible to all, create a positive dynamic and strengthen trust within teams.
The operator thus becomes a driver of progress, with direct involvement in collective performance and the creation of sustainable industrial ROI. Shop floor ownership of data tools enables rapid anomaly detection, process optimization, and faster problem resolution. Companies investing in the human side of their digital transformation generate rapid gains, limit resistance, and maximize industrial ROI at every project stage.

Error 5: Over-Control (Collecting Too Much) and Unstructured Analysis

Collecting 50 KPIs without ever using one to guide industrial decisions is like moving forward blindly: data becomes background noise that paralyzes action instead of supporting it. This indicator inflation, inherited from reporting culture, creates cognitive overload for managers and teams, who struggle to distinguish critical signals from secondary data. Too many manufacturers fall into the “more is better” trap, accumulating indicators for the sake of completeness but without a clear purpose. Result: only superficial findings, never getting to the root cause of performance, quality, or productivity issues.

The more irrelevant information there is, the longer decision-making takes, generating frustration and managerial inertia. Teams waste time analyzing useless data, producing reports of no value, and justifying discrepancies that could have been anticipated with a more targeted approach. The hidden cost of a late or ill-advised decision can exceed 5 to 10 times the initial collection expense, directly impacting industrial ROI (Source: Internal estimate, management consulting firms). Every unnecessary data point stored weighs down infrastructure: server costs, bandwidth, cloud processing, not to mention growing complexity for IT and business teams.
The winning approach is to focus on a “golden set” of 5 to 7 truly decisive KPIs, tailored to the site’s strategic and operational challenges. This rigorous selection should be based on proven methods (DMAIC, 5 Whys, PDCA), transforming data into a driver of continuous improvement, innovation, and profitability, rather than a simple backward-looking reporting tool. Structured analysis of performance indicators, combined with financial and operational interpretation, enables quick identification of gaps, rapid deployment of corrective actions, and optimal resource use.
Thus, each selected indicator becomes a tangible lever for speeding up decision cycles, optimizing resources, and maximizing industrial ROI over the long term. Companies that streamline their management, favoring quality over quantity, ensure they remain agile, responsive, and competitive. Over-control does not mean mastery: only structured analysis, based on relevant data, guarantees lasting value creation and the success of industrial projects.

Conclusion: Secure Data to Secure the Future

Beyond technological tools, all ROI relies on rigorous data management and solid methodology. Structured data governance turns every industrial investment—IIoT, MES—into a measurable competitive advantage: increased productivity, reduced hidden costs, optimized quality, and faster decision-making. Neglecting this strategy deprives the plant of its ability to generate long-term value.

Embedding data at the heart of industrial strategy means integrating it into financial, operational, and human considerations. Executives, industrial directors, and site managers have a key role: aligning technology investments, organizational choices, and management methods to strengthen resilience and profitability.

Integrating data strategy from the design stage of industrial projects and investing in team training establishes a dynamic of excellence and innovation. Companies that structure their data governance secure their competitiveness, steer their growth, and anticipate market changes.

Mastering industrial data thus forms the foundation of an agile, responsible, and sustainable industry, capable of generating industrial ROI and turning every challenge into an opportunity for progress.