Data Integration challenges faced by Manufacturers

Today, manufacturers have to navigate a competitive business environment whilst rapidly meeting the evolving customer needs. 

As manufacturing businesses scale their production, continue innovation, rejig vendors, recruit, and reskill employees, vast volumes of data production happens. In addition to this, there is a constant influx of customer and product-related data. 

Such data streams 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. This information can enable smart decision-making if it were readily available.

As a manufacturer, having a sustainable data integration strategy can help you integrate data across multiple databases, software applications and enterprise systems to increase the operational efficiency of your business.

What is data integration, and how does it work?

Data integration is the process of collating data from different sources to provide users with an integrated view or perspective. 

In a digital business, data flows seamlessly and securely across a landscape centred around data and algorithms. 

Data integration aims to give you a clear overview of the flow of all information across your business processes; making it accessible and easier to assimilate and process by systems and users alike. 

It allows you to derive maximum value from the information resources across the business ecosystem at any point in time.

Data Integration encompasses process and product. Implementing the best tools with a flawed data management strategy can lead to skewed data and faulty assumptions. Similarly, an otherwise 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

Free flow of information in Industry 4.0:

The launch of new technologies, especially the Internet of Things (IoT), has ushered in the fourth industrial revolution, or Industry 4.0. 

Traditional manufacturing facilities have evolved into smart factories with highly digitised manufacturing facilities that integrate technologies such as AI, big data, IoT and robotics. Additionally, with businesses increasingly developing complex products that require collaboration among different factories and owners, lots of data is being generated. 

So, from customer engagements, production or organisational changes, manufacturing businesses must efficiently gather, share, and process vast amounts of data. 

Data integration facilitates the free flow of information through the different manufacturing stages by linking digital and physical systems across manufacturing operations.

Improved efficiencies:

A sustainable data integration system can help manufacturing and other businesses reduce IT spending, optimise resource use, enhance data quality, and encourage innovation without drastic changes to existing data structures or applications. 

Competitive advantages:

Organisations with established data integration capabilities enjoy competitive advantages. This includes increased operational efficiency due to the reduced need for manual transformation and combining data sets. Likewise, automated data transformations efficiently apply business rules to data, enhancing data quality. Finally, having a holistic view of the data enables better insights development.

Supports the IT departments:  

Situations where different IT teams work for other departments or functions within an organisation lead to difficulties in having 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.

Drives business growth:

Data integration simplifies complex data enabling accurate analysis, which helps businesses make better decisions based on predictive insights. As employees share and access data quickly from the central database, it leads to manufacturing more relevant products that enhance customer satisfaction and increase sales and profits.

Challenges and Ways to Overcome it

A data integration challenge is an issue that obstructs you from achieving optimal control over your data integration processes and output. It’s the hurdle in your way, preventing you from getting a single, unified and accurate view of your data.

Here are common data integration challenges that manufacturers face and reccos to fix them. 

#1 Poor Data Quality:

One of the main issues affecting data integration is 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 quality is primarily attributed to discrepancies in data collection protocols or unnecessary involvement of human effort in the data management processes.

For example, two engineers assigned to assess the quality of a machine may evaluate it differently depending upon their experience, competence level and probably even with some element of bias involved. Similarly, errors such as duplication or loss of records and typographical errors are not uncommon.

How to overcome it:

One of the most basic techniques manufacturers can implement to minimise human error 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). 

Often, operators are instructed to describe their findings from the manufacturing production line by picking and choosing from close-ended options in a checklist rather than open-ended responses that may ramble without focus.

Another practical approach is to constantly upskill employees through regular training programmes about the manufacturing processes and machinery. 

With advances in IoT, many manufacturers have started installing sensors directly on machines to automatically collect data on a real-time basis and direct them to the server before they integrate into an extensive database. 

This method of instantly collecting, processing and integrating data using a straightforward and timely approach facilitates agile performance leading to overall business efficacies.

#2 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. Additionally, big data suggest greater depth, variation, and volume of data, making integration more time-consuming and difficult. 

How to overcome it:

Redefining the data quality metrics to track the data points against the threshold automatically will smoothen the data integration process. Likewise, as data volume increases, incorporating speedier and more robust processors will help to ensure convenient and timely integration.

For instance, a fast-paced assembly line environment where the quality of a particular product on the production line may need screening through deep learning-based computer vision algorithms. If the processor is not quick enough to process data points in a tight schedule, it may compromise production efficiencies.

Similarly, Big Data will also require attention to a wide range of data parameters that may seem mutually exclusive but may still have an indirect correlation from the perspective of lean efficiencies. This factor can influence the overall manufacturing process.

#3 Data Ranking:

Not every bit of data is important. Therefore, sourcing, processing, and finally integrating it will weigh in on your budgets and may land up misrepresenting your data management results.

How to overcome it:

Before you begin your integration exercise, rank your data in the order of importance, allocating your data points based on the significance of their impact on your manufacturing operations. 

Manufacturers often use techniques including Failure Mode Effect and Criticality Analysis (FMECA) to come up with data points that can be collected. They look for ways to seamlessly integrated these data points to address failures of products.

#4 Data Security Concerns:

Data security concerns are a crucial challenge for data integration. In traditional manufacturing ecosystems, some critical data would not be on the cloud but in an offline format, thus offering some security against potential cyberattacks. 

However, with cloud-based data integration fast picking up, nearly all data sets now exposed to the cloud are susceptible to malware, cyber-attacks, and ransomware threats. The risk of getting data corrupted or compromised has vastly increased.

How to overcome it:

You should consider incorporating systems and regular checks for protecting data lineage and sensitive data and establishing clear protocols for integrating new data with your legacy data to ensure secure data integration.

#5 Keeping pace with change:

Manufacturing organisations continuously assess new options for data management, such as Cloud computing, virtualisation and other methods, and changes or shifts can affect the quality of data integration.

Another critical challenge is that many large enterprises allow their department heads to purchase and employ applications and databases ad-hoc. The 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.

How to overcome it:

Developing a sustainable and robust data integration strategy before trying and adopting new storage methods makes it much easier to share data, maintain existing data, and change or upgrade simultaneously.

Manufacturers must establish their long-term goals to decide on the proper integration strategy to achieve their goals; and, choose analytical tools that integrate with the data warehouse. This will result in uninterrupted cycles, especially in organisations where data is collected, processed, analysed, and reports are generated.

Other Ways to overcome the challenges of data integration in manufacturing

Organisations, including manufacturing businesses, can maximise their data integration goals by incorporating the following elements in their roadmaps:

Streamline Development:

Choose solutions that allow you to create a catalogue of formats and sub-processes that are reusable; especially for processes such as logging, retries, etc., which are non-functional. 

The capability to test any integration premise on demand will drastically lower the time required for implementation and maintenance.

Agile configuration:

Data integration processes are arranged to connect applications and systems. These configurations immediately reflect the change, ensure the appropriate systems are being used, and initiate changes across various environments in the manufacturing business, including product development, test, quality assurance and eventually production. 

Most organisations still change configuration parameters manually, which is costly and increases the chances of meddling with integration logic. Fully automated deployments for accessing and managing the variables are a more feasible option.

Testing:

Testing is at the heart of data integration development. So, the ex immediately as soon the logic is created or updated by the developer. 

However, many organisations have to implement processes before testing, leading to delays. An IDE (Integrated Drive Electronics) significantly reduced integration process development. 

Also, as specific critical data integration processes are to be tested and re-tested in production environments, using an API (Application Program Interface) to insert data and record test scenarios or an integration testing solution can help reduce project duration.

Leverage common data models:

Building a common data model make future integrations more convenient as all the integration processes will be compatible. The manufacturing business will benefit from it as the creation of services and products becomes easy.

Leverage earlier integrations:

In some organisations, legacy applications still comprise a critical part of business processes, containing relevant data that is required to be integrated with the other systems in the manufacturing environment. An ideal data integration system can be used multiple times. 

For example, you don’t want to manually enter an order into a system each time. It should be such that once you have entered it, the system passes it to another as needed and thus enabling value through data integration.

Data Integration Approaches and Techniques

Organisations no longer maintain data in one database. Instead of maintaining traditional master and transactional data, they maintain different types of structured and unstructured data across multiple sources. 

Physical data integration:

In this traditional approach, data is physically moved from the source system to a kind of staging area. Here, cleansing, mapping, and digital transformation processes are implemented before the data is physically shifted to a target system, such as a data warehouse or mart. 

Data virtualisation approach:

It involves creating virtualised views of the primary physical environment without having to move data physically.

Extract Transform and Load (ETL):

A common data integration technique where data extraction happens from multiple source systems; and converted to a different format and loaded into a centralised data store.

Enterprise Information Integration (EII):

A virtual layer of underlying data sources is created offering users real-time data integration. Thus, it allows developers and business users alike to interact with a  wide range of new data sources as if they were one database whilst presenting the incoming data in novel ways.

Enterprise Data Replication (EDR): 

Used as a data propagation technique; it is a real-time data consolidation method that involves moving a dataset from one database to another database having the same schema. Unlike EDR and ETL, EDR does not involve data transformation or manipulation.

In addition, Enterprise Application Integration (EAI), Change Data Capture (CDC), and other technologies are used by businesses with complex data management architectures.

Conclusion

The amount of data generated in companies is more than before and is key to business growth and success. Having access to timely, superior-quality, and relevant data underpins effective decision-making. Manufacturing businesses have to routinely navigate enormous amounts of handling and managing data on their product and processes. As manufacturers seek to get the maximum value from the applications, functions, and processes, they face tremendous challenges during data integration.

Reviewing 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.