Data Integration Challenges Faced by the Manufacturing Industry

In today’s rapidly changing and competitive business environment, the manufacturing industry is facing new challenges or struggling hard to meet the evolving customer needs. Over the last couple of years, the manufacturing sector has witnessed significant growth in technological adoptions across production facilities in order to enhance productivity, remain competitive and deliver value-based services to customers.

Huge volumes of data production tend to occur in this area as the manufacturing businesses conduct several processes such as – gauge their production, rejig vendors, recruit and reskill employees and continue innovation. Such huge volumes of data generated from machine sensors, raw material inventories, supply chains, regulatory procedures, financial information, human resources, and other diverse sources create a web of critical information – which can be utilized to enable smart decision-making. Having a sustainable data integration strategy in place can help manufacturers incorporate data across multiple databases, software applications and enterprise systems to increase the operational efficiency of their business. Associating with a professional provider of data processing services can help manufacturing companies or businesses build updated data sets which is crucial for good data management.

How Does the Concept of Data Integration Work?

In simple terms, data integration is the process of gathering data from multiple sources to provide users with an integrated view or perspective. When it comes to a digital business environment, data flows seamlessly and securely across a landscape centered around data and algorithms. Data integration provides a clear overview of the flow of information across the business processes, making it accessible and easier to integrate and process by systems and users alike. In addition, it also allows businesses to derive maximum value from the information resources across the business ecosystem at any point in time. Data integration encompasses both process and product. Implementing the best tools with a flawed data management strategy can lead to skewed data and faulty assumptions. In the same way, an efficient data management strategy can hit all the wrong notes if the tools are user-friendly or speedy or require a significant workforce for maintenance.

Importance of Data in Manufacturing

• Improved Efficiency – With a sustainable data integration system in place, manufacturing businesses can help reduce IT spending, optimize usage of resources, improve data quality, and encourage innovation without any significant changes to the existing data structures or applications.

• Enhance Business Growth – Data integration simplifies complex data handling challenges and enables accurate analysis, which in turn helps businesses make better decisions based on predictive insights. As employees’ share and access data quickly from the central database, it helps in manufacturing more relevant products that enhance customer satisfaction and increase sales and profits in the long run.

• Free Flow of Information – The launch of new technologies such as the Internet of Things (IoT), has ushered in the fourth industrial revolution, or Industry 4.0. Traditional manufacturing facilities have evolved into smart factories that utilize highly digitized manufacturing facilities that integrate technologies such as AI, Big data, IoT and robotics. Moreover, with businesses increasingly developing complex products that require collaboration among different factories and owners, lots of data tend to get generated. Therefore, from customer engagements, production or organizational changes, manufacturing businesses must gather, share and process vast amounts of data in an efficient manner. Data integration facilitates the free flow of information through the different manufacturing stages by linking both digital and physical systems across manufacturing operations.

• Competitive Advantage – Organizations with established data integration capabilities enjoy competitive advantages. These include increased operational efficiency due to the reduced need for manual transformation and combining data sets. In the same way, automated data transformations efficiently apply business rules to data, thereby improving data quality.

• Supports the IT Departments – In a manufacturing company, situations tend to arise, where different IT teams work for other departments or functions within an organization. This often results in difficulties in establishing one standard database or analytics. Data integration at the infrastructure levels helps the IT team access and share the data needed by the different constituents.

Key Data Integration Challenges

Rapid digital transformation has brought in a number of challenges in handling or managing an increasing volume of data. Data integration challenge can be considered as a key issue that obstructs a manufacturing business or company from achieving optimal control over the whole process and output of data integration. In fact, the challenges involved in this process prevent a business or organization from achieving a single, unified and accurate view of data. Reports suggest that 61 percent of organizations believe that data management challenges will slow the automation of their business processes.

In fact, the main challenges arise from the increasingly distributed nature of industrial data sets. When data is produced by multiple sources, it usually ends up being in unstructured manner. This leads to challenges in industrial data access, integration, and sharing. As a result, many organizations are able to capture data, but are not able to extract and analyze it. The Big data generated by industrial IoT is usually characterized by heterogeneity, variety, unstructured features, noise, and high redundancy. These features can prevent manufacturers trying to properly use their data.

Here discussed are some of the key data challenges involved in the manufacturing sector –

• Poor Data Quality – One of the key challenges affecting data integration is related to its quality. If the individual data point is incorrect, it will magnify even further on integration with the remaining data points to form the database. The poor data quality is primarily attributed to several discrepancies involved in data collection protocols or unnecessary involvement of human effort in the data management process. For instance, two manufacturing engineers when assigned to assess the quality of a machine may evaluate it in a completely different manner based on their level of experience, competence level and probably even with some element of bias involved. Also, errors such as duplication or loss of records and typographical errors are not uncommon. One of the basic techniques manufacturers can implement to minimize these errors is to enhance consistency in the data collection procedure by having the recording done more than once to get more accurate results and creating strict standard operating procedures (SOP).

• Managing Big Data – As the volume of data increases with time, processes that involve simple manual checks on every data point will not suffice and integrating it becomes more complex. Furthermore, big data suggest greater depth, variation, and volume of data, making data integration a more time-consuming and complex task. Redefining the data quality metrics to track the data points against the threshold automatically is one technique that will help smoothen the data integration process. Similarly, as data volume increases, incorporating speedier and more robust processors will help in timely integration.

• Data Ranking – When it comes to data in the manufacturing industry, not every bit of data is important. Therefore, sourcing, processing, and finally integrating it will be cost-effective and may land up misrepresenting data management results. Before beginning the data integration process, it is important to rank data in the order of importance, allocating data points based on the significance of their impact on the manufacturing operations.

• Extracting Data from Multiple Sources – One of the most critical industrial data challenges concerns the ability of manufacturers to bring together data from multiple sources like the factory floor, offices, and any other location-based sources of industrial data. The main issue in integrating multiple industrial data sources is that traditional ” transaction oriented” ERP systems were designed as independent applications that were not meant to exchange information. Software developers did create several ways to export data from one application to another, but this did not work in a streamlined way. On the other hand, traditional data management techniques were also developed to deal with one specific data source. These techniques also failed to provide the real-time, instant answers that many industrial data analytics applications require. Manufacturers require a more unified data model that facilitates industrial data sharing, rather than simply data exchange. Integrated PLM tools facilitate this process by making any change visible and accessible to everyone in the company.

• Data Security – Data security is one of the key challenges for data integration. When it comes to traditional manufacturing ecosystems, certain critical data would not be on the cloud but in an offline format, thus offering certain security against potential cyber-attacks. However, with cloud-based data integration, nearly all data sets that get exposed to the cloud are susceptible to malware, cyber-attacks, and ransomware threats. Incorporating systems and regular checks for protecting data lineage and sensitive data is one of the best ways to overcome this challenge. In addition, establishing clear protocols for integrating new data with legacy data can also help ensure secure data integration. This will require manufacturers invest more than they initially might have budgeted.

• Data Storage – Over the last few years, there has been a rapid explosion in the volumes of industrial data manufacturers generate and collect. Storage management systems have failed to keep pace so far. In fact, industrial data storage has thus become one of the most significant issues of Industry 4.0. Storing only relevant, insightful data is one important way to overcome this.

• Data Visualization and User Interaction – The big data industry is striving hard to provide better visualization and customer interaction tools. Presenting information in an intuitive and user-friendly style is important, as it helps users produce better insights and devise new solutions. Data visualization and data exploration provide a new platform to understand the meaning of data. Without it, data is just a series of numbers. Data exploration tools can analyze the correct dataset to help find patterns and meaning in the information. These tools could find a correlation that the human eye could have missed. The more the volume and complexity of data, the more the visualization issue becomes a priority.

• Keeping Pace with Change – Manufacturing companies are continuously on the lookout for new options for data management, such as cloud computing, virtualization and other methods, and changes or shifts can affect the quality of data integration. Another critical challenge is that many large companies allow their department heads to purchase and employ applications and databases ad-hoc. The real problem arises when individual departmental data needs merging or explicit study. Issues such as data sharing and availability affect critical business decisions and pose a considerable challenge. Establishing a sustainable and robust data integration strategy and adopting new storage methods make it easier to share data, maintain existing data, and change or upgrade simultaneously.

Manufacturing businesses have to routinely navigate enormous amounts of handling and managing data on their products and processes. Having access to timely, superior-quality, and relevant data is key to business growth and success. As the manufacturing industry has unique data needs, data collection, management and seeking maximum value from the applications, functions, and processes can be extremely challenging during the data integration process, if not done properly.

A careful and thorough analysis of business goals, identifying the critical challenges and adopting the right culture, approach, and tools can help even the most complex manufacturing businesses successfully deal with intricate data integration challenges. Availing the services of a professional business process outsourcing company can prove to be indispensable, particularly when there is a huge amount of data to process and manage. Such professional providers will help ensure good data quality, accurate sharing and effective management of industrial data.