In modern industrial production, the transition to data-driven operations has become a strategic priority. Companies increasingly leverage data to optimize production, reduce costs, and strengthen their competitive advantage.

The importance of data in industry is also reflected in international analyses. The Organisation for Economic Co-operation and Development (OECD) emphasizes that the effective use of data is a key factor in enhancing business competitiveness and resilience in the digital economy.

However, the value of analytics does not depend solely on collecting and processing data—it also depends on data quality. When data are incomplete, inaccurate, or inconsistent, analytics may yield misleading insights and suboptimal operational decisions.

This challenge is more widespread than often assumed. International studies indicate that up to 75% of executives do not fully trust the data they use for strategic decision-making, highlighting the critical importance of reliable data in modern industry.

In an environment where performance indicators and strategic decisions rely on data, data quality becomes the foundation of trust.

In industrial environments, primary data refers to the records generated directly from the production process at the moment an event occurs. These data originate from equipment sensors, PLC systems, MES platforms, IoT infrastructures, or event logging forms (digital or manual), capturing measurements such as operating and downtime durations, production speed, reject rates, temperatures, energy consumption, and quality-related events, and more.

These data represent the initial snapshot of reality on the shop floor. They have not yet been processed or transformed into indicators or reports. Instead, they form the foundation upon which subsequent analyses, calculations, and performance metrics are built.

The importance of collecting accurate data directly from the source is internationally recognized as a critical element of modern manufacturing. The World Economic Forum highlights that the use of digital technologies and real-time data enables operational optimization and improved productivity across industrial environments.

Nevertheless, data quality remains a significant challenge. Research by Hexagon AB and Forrester Research indicates that 98% of industrial organizations face at least one major data-related issue, such as inaccuracies, incomplete data, or inconsistencies between different systems.

The value of primary data, therefore, lies not only in its availability but also in its accuracy, completeness, and consistency. When data collection at the source contains gaps or errors, these problems propagate through every subsequent level of analysis, ultimately affecting the reliability of analytical results.

In industrial production, many decisions are based on processed data such as dashboards, aggregated reports, and performance indicators. These tools facilitate production monitoring and performance evaluation. However, processed data is the result of transformation processes that include filtering, calculations, and aggregation. If the original data contains errors or omissions, these issues are incorporated into every subsequent level of analysis.

Simply put:

• Primary data shows what actually happened in production.

• Processed data shows how what happened is interpreted.

The further analysis moves away from the original source, the more difficult it becomes to identify the root cause of a data error.

According to the DAMA International Data Management Body of Knowledge (DAMA-DMBOK), data quality is evaluated through key dimensions such as:

• Accuracy
• Completeness
• Consistency
• Timeliness
• Traceability
  

 These parameters form the basis for any reliable analysis.

The importance of collecting data directly and accurately from the source is internationally recognized as a critical element of modern manufacturing. The World Economic Forum notes that leveraging digital technologies and real-time data enables operational optimization and increases productivity across industries.

Nevertheless, data quality remains a significant challenge. Research by Hexagon and Forrester indicates that 98% of industrial companies face at least one major data-related problem, such as inaccuracies, incomplete data, or inconsistencies across systems. These problems often stem from the collection of raw data.

However, the value of raw data lies not only in its availability but also in its accuracy, completeness, and consistency. When source-level recording contains gaps or errors, these problems carry over to subsequent levels of analysis, affecting the reliability of the results.

In an environment where performance metrics, productivity reports, and OEE calculations rely on a continuous flow of data, even minor errors in data recording can distort the final picture of production. The “garbage in, garbage out” principle captures exactly this: when input data is inaccurate, the results of the analysis cannot be reliable.

Even small errors can have significant consequences. A small measurement error, for example, in the range of 2%, can affect calculations such as OEE, energy metrics, or scrap rates. When this data accumulates into thousands of records, the initially small error can lead to significantly incorrect conclusions.

These consequences are not limited to data analysis alone but directly impact operational decisions. In industrial settings, decisions regarding production planning, equipment maintenance, investments in new equipment, or the allocation of human resources are based on metrics and data analysis.

When this data is inaccurate or incomplete, organizations may be led to make incorrect decisions, such as: 

• Optimizing the wrong bottleneck in production
• Addressing problems that do not actually exist
• Inability to identify the true cause of losses or downtime
• Investing in equipment or solutions that do not address the real problem

Organizations with reliable, high-quality data can achieve significantly better operational performance than those relying on inaccurate or fragmented data. For this reason, data quality is not merely a matter of technical infrastructure but a fundamental prerequisite for reliable analysis and sound business decisions.

When analysis is based on incomplete or inaccurate data, the consequences go beyond incorrect numbers in a report. They influence how industrial organizations prioritize actions and allocate resources.

In manufacturing environments, data is used to evaluate equipment performance and identify improvement opportunities. According to analyses by McKinsey & Company on Industry 4.0, advanced analytics in manufacturing can lead to a 30–50% reduction in unplanned machine downtime and a 10–30% increase in production output.

However, when data is inaccurate, performance metrics may paint a misleading picture of production. As a result, non-critical activities may be prioritized, or investments may be made that do not meet the actual needs of production.

This need becomes even more apparent when we consider that manual data entry can lead to up to 100 times more errors compared to automated systems. Practices such as manual data entry or transferring information between different systems significantly increase the likelihood of errors and directly impact data reliability.

At the same time, it is estimated that unplanned downtime costs the industry over $50 billion annually, highlighting the importance of reliable data for timely problem detection and sound maintenance decision-making.

Data quality is also essential for AI and advanced analytics initiatives. Studies indicate that up to 73% of AI/ML projects are negatively impacted by data quality issues, significantly limiting their effectiveness.

Another common challenge involves system integration. Research suggests that around 42% of organizations struggle to integrate data across systems such as ERP, MES, and PLC platforms. The lack of a unified view leads to fragmented information, multiple versions of the truth, and unreliable dashboards.

Overall, poor data quality may significantly impact operational efficiency, with some studies estimating that organizations can lose 20–30% of their operational performance due to inaccurate or fragmented data.

Reliable primary data does not occur by chance. It is the result of structured processes for data collection, validation, and management.

Key practices include:

▶ Clear data quality and validation rules: Data should pass through defined validation checks before being considered reliable. These rules help identify errors, inconsistencies, or out-of-range values early, ensuring that only valid data is used in analytics.

▶ Unified data architecture: Common standards and uniform data structures ensure that information is not isolated within individual applications or databases, and reduce errors caused by different formats or sources. As a result, all systems work together seamlessly and produce consistent results.

▶ Proper data collection from equipment and sensors -> Reliability begins on the shop floor:

• • Correct selection, installation, and positioning of sensors and measurement equipment: The leading cause of errors in industrial primary data is incorrect installation, improper placement, or unsuitable sensor selection.
  • Calibration and recalibration: Even the most accurate sensors drift over time and require periodic calibration.
  • Data transmission integrity and security. Even when data is collected correctly, transmission issues may arise, such as signal noise, packet loss, latency, duplicate measurements, and incorrect timestamps
  • Continuous monitoring of sensor health 
  • Recording all relevant metadata, including: (precise timestamp, sensor or machine ID, measurement unit, sensor status at the time of measurement (OK / Fault)

▶ Continuous monitoring of data quality: Data quality cannot be ensured solely through rules; it requires continuous monitoring. Ongoing oversight enables early detection of anomalies, data loss, or deviations, ensuring that data remains reliable throughout operations. Industrial data collection and analytics platforms, such as SEEMS’ aRTi-D^TM platform, help ensure primary data quality by starting with the proper installation of sensors and measurement equipment. Through structured deployment processes, built-in validation mechanisms, and continuous monitoring of sensor status and data flows, such platforms help industrial teams detect deviations or data collection issues early. Combined with unified data architectures and validation mechanisms, these platforms enable organizations to build more reliable datasets for monitoring and analyzing production, allowing production teams to rely on data that more accurately reflects what is truly happening on the shop floor. 

The question “Does your analysis rely on accurate primary data?” is not only about the tools or systems an organization uses. It is ultimately about the reliability of the decisions made daily in industrial production.

When data is accurate and consistent, performance indicators and analytics tools can more accurately reflect production operations and support meaningful improvements.

Organizations that invest in strong data governance and reliable primary data collection can identify real bottlenecks, uncover hidden production potential, and significantly improve decision-making.

Solutions that enable reliable primary data collection directly from the production line and transform it into actionable insights—such as those developed by SEEMS—can play a critical role in supporting the transition toward more efficient, truly data-driven industrial operations.