Four key data-integration challenges facing manufacturers -

Four key data-integration challenges facing manufacturers

As the complexity and quantity of data grow, the subsequent integration of data is becoming an emerging problem for data scientists.

With the rise of digital sensing technology and the internet of things (IoT), manufacturers are now increasingly handling a large amount of data daily as part of their routine operations. Such data streams originate from disparate sources throughout an organization such as machine sensors, supply chain, regulatory requirements, financial performance, raw material inventory, and human performance.

Each of the data alone is often not useful for any manufacturer unless it is integrated enough to give a sufficient picture at the enterprise level. As the complexity and quantity of data grow, the subsequent integration of data is also becoming an emerging problem for data scientists. The article presents the top challenges associated with data integration for manufacturers.

Challenge #1 Poor Quality Data

One of the biggest challenges for data integration is its poor quality. If the individual data point is incorrect, it can be misled on a greater scale when integrated with the rest of the data points to form the database. The poor quality often originates from inconsistencies in data collection protocols or from excessive human involvement in data management processes.

Human tends to make an error; two machine operators tasked to inspect the machine’s health may judge the machine differently depending upon their level of competence, experience, and to some extent their biases as well. Also, other errors could include duplication of records, typographical errors, and loss of records.

One of the simplest techniques that manufacturers adopt to mitigate human error is to bring consistency in data collection. This could be as simple as recording more than once to get the precise results to as complex as making strict standard operating procedures (SOP). More commonly, operators have to pick and choose from the checklist to collect data as opposed to writing long stories about their findings from the manufacturing production line.

The other useful approach is to increase competency through training employees about the manufacturing processes and machines. With the advancement of IoT, the manufacturers are now placing sensors directly on the machine that automatically collects the data on a real-time basis and sends them over to the server before being integrated into a big database. The benefit from this instant collection, processing, and integration of data is its convenience and timeliness of use as triggers to increase lean performance and overall business efficacies.

Challenge #2 Handling Big Data

As the data gets bigger over time, the challenges associated with its integration also get complex. This means a process to simply perform a manual check on every data point will not function. Instead, the data quality metrics would have to be defined to automatically track the data points against the threshold.

Also, big data would imply a greater variety and volume of data that could bring a range of integration complexities. The greater 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 require to be screened through deep learning-based computer vision algorithms. Now, if the processor is not fast enough to process data points in a tight timeline, the overall production efficiency can be compromised.

Similarly, Big Data would also involve considering a variety of data parameters. Such parameters may look mutually exclusive but from the perspective of lean efficiencies, there may still have an indirect correlation among them and thus can exert an impact on the overall manufacturing process.

Challenge #3 Prioritization of data

The prioritization of data is another impact that is worth considering before the integration exercise. Not every piece of data is relevant – therefore, collecting, processing, and integrating them will not only be a waste of money but may well in turn end up misleading the data management results.

The best way is to perform prioritization of data points based on the severity of their impact on manufacturing operations. Manufacturers may use techniques such as Failure Mode Effect and Criticality Analysis (FMECA) to come up with data points that should be collected and integrated to cater to emerging failure modes of manufactured products.

Challenge #4 Data Security

Data Security is among the emerging challenges for data integration. In a conventional manufacturing environment, not every piece of data would be on the cloud as it could simply be on a piece of paper or some offline workstation. This provides inherent cyber security to silo data sets. 

With the emergence of cloud-based data integration, practically every set of data is now exposed to the cloud and in turn vulnerable to increasing cyber-attacks, malware, and ransomware threats thus increasing their risk of getting data corrupted or compromised. 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 to integrate new data with the legacy data


In a nutshell, the presence of high-quality and relevant data is the cornerstone for effective decision-making. Manufacturers of the modern era are currently handling data of vast scales as part of their routine operations. This has exposed them to unprecedented challenges when it comes to handling and managing data on their product and processes. To enable safe operation, it is important that the data is safely and efficiently integrated to form a big 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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