The importance of data integration for data-driven business management

Data-driven business management requires data integration, as it ensures that data from different sources and systems are brought together and harmonised. Only when complete, correct and consistent internal and external information is easily and quickly accessible and can be linked together can it fully develop its benefits as a basis for data-driven decisions:

  • Data Integration enables companies to bring together data from different internal and external sources. This creates a holistic picture of the company's data that reaches across different departments, systems and processes. Such a comprehensive understanding is critical to identifying connections, patterns and relationships in the data and making informed decisions.

  • The integration of real-time data offers companies the chance to bring more transparency into their processes, to increase planning reliability or to detect possible sources of error in time. By integrating real-time data such as physical values (temperature, pressure, etc.), production companies can gather information about the effectiveness of their systems. Actual values can be compared with target data using algorithms that trigger reaction scenarios in real time if certain threshold values are exceeded or not reached.

  • Data integration enables companies to solve data quality problems by automatically cleansing, validating and harmonising data. Inconsistencies, redundancies and errors in the data are detected and corrected to ensure high data quality and consistency.

  • Data integration supports efficient decision-making processes by facilitating access to relevant and timely data. Managers can access integrated data without having to spend time and resources manually searching and compiling data. This enables faster and more informed decisions by providing up-to-date data for analysing and evaluating options.

  • Data Integration helps to manage data effectively. Data is easier to organise, store and process. This facilitates access to relevant data and enables more efficient use of data for operational processes, reporting, compliance requirements and other business needs.

Informatec Data Integration

How companies should define a data integration strategy

A clear data management strategy is important to carry out data integration effectively. Without a solid strategy, difficulties can arise in data identification, prioritisation, mapping and updating.

  • A requirements analysis should include identification of data sources, data targets, data formats, integration patterns (batch-oriented or real-time), required data quality, data protection requirements, volume of data to be integrated, etc.

  • Subsequently, existing data must be assessed in terms of their quality, consistency, completeness and relevance. It makes sense to identify data sources that should be prioritised for integration, as well as data that may need to be cleansed, harmonised or transformed.

  • Based on the company's requirements and goals, suitable data integration technologies can then be identified and selected.

    • Extract, Transform, Load (ETL) is a commonly used technology for data integration. It includes the extraction of data from different sources, the transformation of the data according to the requirements and the subsequent loading into the target system or storage.
    • Data virtualisation allows data from different sources to be virtually integrated and presented as a single logical data source. In doing so, data is merged on demand in real time without creating a physical copy of the data. This enables flexible and agile data integration.
    • Data replication technologies copy and synchronise data from a data source to one or more target environments. This allows organisations to replicate data in real time or on a set schedule to ensure data consistency and availability.
    • Data integration platforms provide a comprehensive suite of tools and functions to support various aspects of data integration. They often include ETL capabilities, data mapping, data quality management, transformation, workflow orchestration and monitoring. These platforms enable organisations to manage the entire data integration process from ingestion to delivery.
  • To ensure effective data integration, the next step should be to develop a suitable data architecture and data modelling. It must be determined how the data is to be integrated, transformed and made available. Real-time data is playing an increasingly important role. They offer companies a particularly large potential for value creation, but require special attention and a well-coordinated and well thought-out architecture for data integration.

  • An important aspect of the data integration strategy is to implement measures for data validation and quality control. This includes testing to ensure that the integrated data is correct as well as consistent and meets the defined quality standards.

  • In addition, governance policies and compliance requirements related to data integration should be considered. This includes data integrity protection, data security, data protection regulations, data access controls and compliance with legal requirements such as the GDPR.

  • After defining the data integration strategy, the implementation of the corresponding technologies and processes takes place. In order to ensure efficient data integration, the integration process should be continuously monitored so that errors can be corrected at an early stage and adjustments can be made if necessary.

What is Data Integration?

Why concepts for the deletion and archiving of data are important

The concepts of deletion and archiving should be incorporated into the data integration process.

  • To ensure that data that is no longer needed or has lost its validity is removed from the systems, companies should have deletion concepts in place. This helps to improve data quality and to keep data stocks lean and up-to-date. It must be determined when and how data is to be deleted in order to ensure that any legal and internal company guidelines are adhered to.

  • For compliance reasons, historical analysis, or other purposes, the archiving of certain data may be necessary. Accordingly, companies should create an archiving concept that specifies which data should be archived, defines the archiving processes and technologies, and ensures data integrity and accessibility over time.

Our data integration services

As a data intelligence expert for holistic end-to-end data intelligence with many years of project experience, we support companies in all process steps from requirements analysis to tool selection and solution implementation..

Our product portfolio in the area of data integration includes data integration software from three leading providers:

We maintain close partnerships with all three manufacturers and ensure that we stay up-to-date with the latest technologies through regular training. As a Qlik Elite Solution Provider, Microsoft Partner, and Talend Gold Partner, we are happy to advise you on the selection and implementation of a data integration solution that best suits your requirements - so that you can derive the maximum benefit from your data.