HomeBUSINESSData Integration: Why It Forms The Basis For Industry 4.0

Data Integration: Why It Forms The Basis For Industry 4.0

Data Integration- Many manufacturing companies are in the process of digitizing manufacturing processes and entire value chains. They all need a workable concept. Some are still at the beginning, and others are already in the middle of it: Many manufacturing companies have successfully launched their Industry 4.0 initiative. Without data integration, however, Industry 4.0 is inconceivable. But what is essential in terms of data integration on the way to the intelligent factory and what is not?

Successful integration of all data streams is the foundation for essential adjustments to production, for example, to increase the overall system effectiveness or production effectiveness. Companies need to work in three areas: data, data integration, and data analysis to take advantage of the possibilities and numerous advantages of the digitized output. Because the core of Industry 4.0 initiatives consists of information, its integration and its evaluation.

Data Integration: Raw Material For The Value Chain

Classic Industry 3.0 has an excellent need for optimization in terms of using data to generate knowledge. Production systems of this generation show a high degree of fragmentation in data silos. They are hardly networked so that a holistic view of processes is not possible. It is estimated that less than one percent of a company’s unstructured data is currently being analyzed. This is why internal and external data are often not considered and linked – although the share of unstructured data is up to 80 per cent!

What Is Unstructured Data?

A typical example of semi- and unstructured data are logs, sensor and video data. On the other hand, structured data includes SAP data, among other things. At the moment, only a tiny part of unstructured data is evaluated because

  • They are available in very different formats, such as images, texts, machine and sensor data. This makes it challenging to find a suitable tool to access all of them and read and process the respective data format.
  • the data volume is exceptionally high
  • they must be extracted in an appropriate form
  • often specific (manual) solutions have to be developed so that the data can be connected at all
  • a large number of data updates and the lack of comparability of their sources complicate the processes
  • there is often a lack of specialist knowledge about data integration and the use of data sources

Great Potential: Data In Production

There is enormous potential in the data of manufacturing companies. However, its use is challenging for many organizations. The difficulties can be solved with suitable applications:

  • Volume – The Amount Of Data: Production systems are being equipped with more and more sensors that deliver data on a wide variety of production parameters permanently and in real-time. Big data technologies support the storage, processing and analysis of this data and provide valuable insights.
  • Velocity – The Transmission Speed: If, for example, an application is used for predictive maintenance, i.e. for predicting and avoiding system failures, the information must be promptly transmitted to the maintenance staff. Modern streaming analytics technologies enable such alerting and proactivity in the operational processes of a production facility.
  • Variety – The Variety Of Data: Probably the most significant challenge in analytical Industry 4.0 applications lies in the array of data. Before the data is brought together centrally, countless proprietary data sources must first be tapped. There is no standard in sensors, and information is delivered using a variety of formats and protocols. There is, therefore, a need for robust and flexible data integration tools to overcome these challenges.

Data Integration In Individual Expansion Stages

A significant challenge for the systematic use of data in production is to merge the scattered data sources so that, in the end, an optimal data flow is created that supplies all-important systems with the correct data at the right time. The following levels can be identified for this:

  1. Data Warehouse: Classic data warehouse implementations automatically merge data from the different systems, aggregate them and provide critical figures.
  2. Data Lake: The next stage of development is to integrate more data and make it available as information in the processes. Semi-structured and unstructured data must be linked and used. In addition to equipping old machines with appropriate sensors, the retrofitting, and the data connection of various sensors, new systems must also be created to store and process this information. Data lakes and big data technologies are the basis for all advanced analytics and data science applications.
  3. Near Real Time: The next level relies on the processing of data in almost real-time. The data streams are persisted in the data warehouse or data lake and are evaluated directly where they arise: close to the sensor. Analytics “on edge” enables problems to be averted in advance by triggering an alarm or automatic machine shutdown when certain thresholds are reached.
  4. Data Blending: This is the process of combining data from multiple sources to have an analytical data set for decisions. Data blending is necessary when a company’s data integration processes and infrastructure are insufficient to bring together specific data sets required by each business unit.

Data Evaluation: From Analysis To Recommendations For Action

The various levels of integration for data from digital products are also reflected in the characteristics of the evaluation processes for this information: the journey begins with the classic evaluation of historical data. It ends with advanced analytics with forecasts for future developments, including recommendations for action or orders to processes or other participants in the value chain. After overcoming the world of data silos, it is time to generate knowledge through analysis and distribute it to the right people in the company. So-called advanced analytics, therefore, form the heart of many Industry 4.0 applications.

  1. Business Intelligence: BI applications provide business users with information in the form of reports, dashboards or self-service evaluations. This provides, for example, insights into the performance of the systems or maintenance activities. In this way, comparisons of set-up times between machines or plants can be quickly discovered, such as deficits in individual teams or locations.
  2. Predictive Analyzes: It is less about looking backwards and more about future prognoses and discovering hidden connections in the data. Predictive analysis predicts what can happen next and opens up the possibility of proactively countering problems.
  3. Prescriptive Analyzes: They provide information on how companies should deal with future challenges and provide specific recommendations for action for upcoming tasks in production.

Realize Competitive Advantages With Data Integration

As part of a booming Industry 4.0 initiative, powerful modern technologies ensure that competitive advantages are realized, and new perspectives for future production scenarios are possible. McKinsey assumes that Industry 4.0 applications can reach a value of 3.7 trillion US dollars per year by 2025. Manufacturing companies benefit from the following advantages when implementing Industry 4.0 projects:

  • Increase in productivity and reduce costs
  • Greater accuracy of planning and forecasts
  • Shorten the go-to-market time
  • Increase in competitiveness

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