What is Data Integration? Application Integration vs. Data Integration, and 5 Data Integration Methods and Strategies

What is Data Integration?

Data integration is the process of combining data from multiple sources into a single, unified view. This can be done for a variety of reasons, such as improving decision-making, reducing costs, or complying with regulations.

There are two main types of data integration: application integration and data integration. Application integration refers to the combination of data from different applications. Data integration, on the other hand, refers to the combination of data from different data sources.

There are five main methods of data integration: extract, transform, and load (ETL), enterprise information management (EIM), Master Data Management (MDM), virtual data federation, and cloud-based data integration.

Each method has its own advantages and disadvantages. ETL, for example, is a popular method because it is relatively easy to implement. However, it can be time-consuming and requires specialised skills. EIM, on the other hand, is more complex but provides a more comprehensive view of the data.

The most appropriate method of data integration will depend on the specific needs of the organisation.

Application Integration vs Data Integration

Application integration refers to the combination of data from different applications. This can be done manually or using an application integration platform. Application integration platforms provide a way to connect different applications so that they can share data.
Data integration, on the other hand, refers to the combination of data from different data sources. Data sources can include databases, spreadsheets, and text files. Data integration can be done manually or using a data integration platform. Data integration platforms provide a way to connect different data sources so that they can share data.

There are several differences between application integration and data integration:

  • Application integration is often used to connect two or more applications so that they can share data. Data integration, on the other hand, is used to combine data from multiple data sources.
  • Application integration usually requires the use of an application integration platform. Data integration can be done without a platform, but using one can make the process easier.
  • Application integration is typically done for a specific purpose, such as sharing customer information between a CRM and ERP system. Data integration is done for a variety of reasons, such as reducing costs or improving decision-making.
  • Application integration is often done between applications that are owned by the same organisation. Data integration can be done between data sources that are owned by different organisations.

5 Data Integration Methods and Strategies

There are five main methods of data integration: extract, transform, and load (ETL), enterprise information management (EIM), Master Data Management (MDM), virtual data federation, and cloud-based data integration.

1. Extract, Transform, and Load (ETL)

ETL is the most popular method of data integration. It involves extracting data from multiple sources, transforming it into a consistent format, and loading it into a destination database.
ETL is a popular method because it is relatively easy to implement. However, it can be time-consuming and requires specialised skills.

2. Enterprise Information Management (EIM)

EIM is a more comprehensive approach to data integration. It involves managing all of the data in an organisation, from multiple sources, in a single repository. EIM provides a single view of the data that can be used by all applications.
EIM is more complex than ETL but provides a more comprehensive view of the data. It can be used to support decision-making, compliance, and other business processes.

3. Master Data Management (MDM)

MDM is a method of data integration that focuses on managing a single copy of master data. Master data is the core data that is used by an organisation, such as customer information or product data.
MDM is used to ensure that the master data is accurate and consistent across all applications. It can also be used to improve decision-making and business processes.

4. Virtual Data Federation

Virtual data federation is a method of data integration that uses virtualisation technology to combine multiple data sources. This allows organisations to access all of their data from a single platform.
Virtual data federation provides a way to connect different data sources without replicating the data. It can be used to reduce costs and improve decision-making.

5. Cloud-based Data Integration

Cloud-based data integration is a method of data integration that uses cloud computing to connect different data sources. This allows organisations to access all of their data from a single platform.
Cloud-based data integration provides a way to connect different data sources without replicating the data. It can be used to reduce costs and improve decision-making.

To summarise, data integration is a critical part of any business. It allows organisations to connect their data so that it can be used to support decision-making, compliance, and other business processes. There are five main methods of data integration: extract, transform, and load (ETL), enterprise information management (EIM), Master Data Management (MDM), virtual data federation, and cloud-based data integration. Choose the right method for your organisation based on your needs and goals.

If you would like to discuss any other these topics further or are planning a data integration project then please feel free to contact Chester Apps.