Four key data integration challenges for manufacturers


As the complexity and volume of data increases, data integration after the fact becomes an emerging problem for data scientists.

With the advent of digital sensor technology and the Internet of Things (IoT), manufacturers are increasingly processing large amounts of data as part of their day-to-day operations. Such data streams come from different sources within an organization, e.g. B. Machine sensors, supply chain, regulatory requirements, financial performance, raw material inventory and human performance.

Individual data alone is often of no use to any manufacturer unless it is sufficiently integrated to provide a sufficient enterprise-level picture. With the increasing complexity and volume of data, the subsequent integration of data becomes an emerging problem for data scientists. The article presents the top challenges related to data integration for manufacturers.

Challenge #1 Poor data quality

One of the biggest challenges for data integration is its poor quality. If the single data point is incorrect, it can be mislead to a larger extent when integrated with the rest of the data points to form the database. The poor quality often stems from inconsistencies in data collection protocols or from excessive human involvement in data management processes.

Man tends to make a mistake; Two machine operators tasked with checking the condition of the machine may judge the machine differently depending on their skill level, experience, and to some extent their biases. Other errors can also include duplicating records, typos, and losing records.

One of the simplest techniques manufacturers employ to mitigate human error is to achieve consistent data collection. This can be as simple as recording more than once to get the exact results, and as complex as being strict Standard flow (SOP). More often operators have to select from the checklist to collect data rather than writing long stories about their insights from the assembly line.

The other approach that makes sense is to increase competency by training employees about the manufacturing processes and machines. With the advancement of the IoT, manufacturers are now placing sensors directly on the machine, which automatically collects the data in real time and sends it to the server before being integrated into a large database. The advantage of this instant collection, processing and integration of data is its convenient and timely use as a trigger to increase lean performance and overall business efficiency.

Challenge #2 Dealing with Big Data

As the data grows over time, the integration challenges also become complex. This means that a process to simply check each data point manually won’t work. Instead, the data quality metrics would need to be defined to automatically track the data points against the threshold.

Also, big data would imply greater diversity and volume of data, which could introduce a number of integration complexities. The larger volume would require faster and more robust processors to enable timely integration.

For example, in a fast-paced manufacturing environment, the quality of the product on the production line may need to be verified by deep learning-based computer vision algorithms. Now, if the processor isn’t fast enough to process data points in a tight timeframe, overall production efficiency can be impacted.

Similarly, big data would also involve considering a variety of data parameters. Such parameters may seem mutually exclusive, but from a lean efficiency perspective, they can still have an indirect correlation between them and thus have an impact on the overall manufacturing process.

Challenge #3 Prioritizing data

Prioritizing data is another aspect to consider before the integration exercise. Not all data is relevant – therefore, collecting, processing and integrating it is not only a waste of money but can also lead to misleading data management results.

The best way is to prioritize data points based on the severity of their impact on manufacturing operations. Manufacturers can use techniques such as Failure Mode Effect and Criticality Analysis (FMECA) to identify data points that should be collected and integrated to account for emerging failure modes of manufactured products.

Challenge #4 Data security

Data security is among the emerging challenges for data integration. In a traditional manufacturing environment, not all data would be in the cloud as it could simply reside on a piece of paper or an offline workstation. This provides inherent cybersecurity for siled datasets.

With the advent of cloud-based data integration, virtually every dataset is exposed to the cloud, making it vulnerable to increasing cyberattacks, malware, and ransomware threats, increasing the risk of data corruption or compromise. Some of the important aspects that should be considered to enable secure data integration are protecting data lineage, protecting sensitive data and defining clear protocols Integrate new data with legacy data.


In short, having quality and relevant data is the cornerstone of effective decision-making. Modern era manufacturers currently process data on a large scale as part of their routine operations. This has presented them with unprecedented challenges when it comes to handling and managing data related to their products and processes. In order to enable secure operation, it is important that the data is securely and efficiently integrated into a large database.

Bryan Christiansen is the founder and CEO of Limble CMMS. Limble is a modern, easy-to-use mobile CMMS software that takes the stress and chaos out of maintenance by helping managers organize, automate, and streamline their maintenance operations.

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