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What is data integration and what are the benefits?

May 22, 2024 4:29:47 PM / by Alleantia

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By combining data from multiple sources or systems into a unified format, companies can gain a complete view of their data and derive meaningful information.


 

Data Integration is the process of combining, consolidating and coordinating data from different sources and systems to obtain a set of 'contextual' information.

The goal is to create a single reliable and consistent data repository. This repository, called a data lake, will be useful for supporting many valuable activities. Among these activities are data analysis, reporting, predictive maintenance and informed decision-making.

Data Integration goes hand in hand with the concept of Interoperability, encompassing all processes related to the transfer and consolidation of data within and between data stores, applications and organisations.

Integration consolidates data into coherent physical or virtual forms, while data interoperability is the ability to communicate between multiple systems.

 

Data Integration and Interoperability makes it possible to combine data from different sources and provide a unified view of them.

Integrating data from different sources, looking for relationships and analysing them to obtain detailed information is crucial for optimising and understanding processes.

But the ability to quickly access data and perform analysis to support growth is often hampered by several barriers, such as:

 

  • accessing data from heterogeneous devices and systems
  • the management of non-integrated OT and IT systems
  • the inability to scale IIoT solutions

 

Typically, all kinds of data are collected within companies. From these, information is obtained to only partially reconstruct the course of a process.

Even if a company receives all the data it needs, it often resides in several separate sources. For example, the information needed may include data from industrial machinery, from CRMs, from the interaction between operators and systems or from third-party applications.

Let us also not forget that data costs money, having a lot of data means investing operators' time in numerous and endless activities. Finally, it is increasingly common to collect useless data that will never be used.

 

What is the purpose of data integration

The amount of data generated and transferred during processes must be utilised and easily available. The motivation is to deepen knowledge in order to derive added value and economic benefit for companies.

Data integration eliminates the need to manually acquire data from business processes and reduces the risk of errors and subsequent analysis through real-time data.

This simplifies analysis and avoids having to start over for each report or application, allowing immediate value to be created.

 

The Benefits of Data Integration

  • Improves synergy and interaction of systems
  • Saves time
  • Reduces errors
  • Produces more useful data
  • Enables simplified Edge Intelligence

    In short, integrating data in manufacturing brings many benefits such as better management and control of production, increased efficiency and accuracy.

    However, to take full advantage of these benefits, an effective and sustainable integration process must be planned and implemented.

 

Data Integrations best practices

  1. Find data sources in the company. These may include machinery, ERP systems, SRPA, quality control, supply chain management and other data recording systems.
  2. Define the data to be integrated. Once the data sources have been identified, it is necessary to define which data is to be integrated. This may include data on production, inventories, customer orders, shipments, suppliers and other aspects of manufacturing.

  3. To integrate data, you have to choose a platform. The platform must connect to different data sources. It must bring information together in one place (data lake). This platform should be able to handle large volumes of data, provide accurate data mapping and offer cleaning and normalising capabilities.

  4. Clean and normalise data. Before integrating data, it is necessary to clean and normalise it to ensure that it is accurate, consistent and reliable. This may include correcting typing errors, standardising formats and normalising values.

     

  5. Integrating the data. Once the data has been cleaned and normalised, it can be integrated into a single repository.

     

  6. Monitor and maintain the data. After integration, it is important to monitor and maintain them to ensure that they are accurate, consistent and reliable over time. This includes a unified view for their periodic verification, correction of errors and cleaning of unused data.

 

data_integration_pipeline

 

Besides the key steps, it is important to consider some additional elements that can contribute to the success of data pipelines in the manufacturing sector:

  1. Data security. Ensuring data security is critical to protecting sensitive and critical business information. This includes implementing access controls, data encryption, monitoring of suspicious activity and compliance with data privacy regulations.
  2. Scalability and performance. Pipelines must be designed to handle large volumes of data and adapt to the growing needs of the business over time. This means they must be scalable and able to maintain high performance even with increased workload.
  3. Integration with existing systems. Data pipelines must be integrated with the company's existing systems, such as ERP systems, MES, CRM and others, to ensure a complete and integrated view of activities.
  4. Process automation. Process automation can help improve operational efficiency and reduce repetitive manual work. This can include the automation of data collection and processing, as well as the automation of data monitoring and maintenance activities.
  5. Real-time analysis. In the manufacturing sector, it is often important to have access to real-time information to make immediate decisions and react quickly to changes in the operating environment. Data pipelines must be able to support real-time analysis to provide up-to-date and timely information to users.

    Adding these elements during the design and implementation of data flows in manufacturing can help companies. This allows them to make the most of their data and gain important competitive advantages.

 

Examples of data pipelines

Having contextual data pipelines is useful to retrieve consistent and reliable insights.

For example, data pipeline tools can work to understand potential future trends through predictive analysis based on contextual data.
A production department can use predictive analysis to know when raw material is likely to run out or to predict which supplier might cause delays. Using efficient data pipeline tools provides in-depth information that can help the production department streamline its operations.

 

Data pipeline for production monitoring:

 

  • Collection of production data from machine sensors, Manufacturing Execution Systems (MES), operator terminals and other monitoring devices.
  • Normalisation of data, detection of anomalies, aggregation of data by time period and calculation of production metrics (e.g. OEE - Overall Equipment Effectiveness).
  • Data analysis to identify production trends, identify machine downtime, predict production peaks and optimise production planning.
  • Creation of real-time dashboards to monitor production status, generate reports on production efficiency and identify critical points requiring action.

 

Data pipeline for supply chain management:

  • Collection of data on customer orders, inventory stock, supplier delivery times and other supply chain data.
  • Data normalisation, comparison of inventory data with customer orders, identification of discrepancies and calculation of estimated delivery times.
  • Data analysis to identify inefficiencies in the supply chain, optimise stock management, identify obsolete inventory risks and improve the accuracy of demand forecasts.
  • Creation of dashboards to monitor the status of the supply chain, analysing the flow of goods and identifying areas for improvement in supply chain management.

Data pipeline for predictive maintenance:

  • Collection of condition sensor data, historical maintenance data, machine operating data and other relevant equipment health information.
  • Data normalisation, identification of patterns and anomalies in the data, calculation of equipment health metrics.
  • Data analysis to identify early signs of machine failure, predict the optimal time to perform preventive maintenance and maximise equipment availability.
  • Creation of dashboards to monitor equipment status, visualise failure forecasts and plan maintenance activities.

 

Alleantia's approach to Data Integration

 

Why is data integration and data management with Alleantia so easy?

+5000 MACHINE DRIVERS - Support for a wide range of CNC PLC sensors

+20 APIs available - Bi-directional connectivity with SaaS/Paas applications on premise and on cloud.

 

 

Alleantia links your data, from any system, in any format →

 

alleantia data integration layer

 

 

Tags: Industry 4.0

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Written by Alleantia