Data Integration

The complexity of modern business processes requires organizations to use tons of systems, applications, and data volumes from different business partners. These data are siloed and separate in many cases, posing a challenge to accurately understanding the information it holds. So, to optimize the value of the data assets, you’ll need to unify the different data sets, which is where data integration plays a role.

So, what is data integration? How does it work? This article seeks to answer all your questions and provide an in-depth summary of data integration, how it works, and the different types.

Data Integration Defined

Data integration is the process of combining data from disparate sources, typically for analysis, reporting, or business intelligence. Its premise is to ensure that data can easily be accessed when needed and easy to consume. So, when data integration is done correctly, it can benefit the organization immensely, from saving costs to improving data quality and fostering innovation. In addition, having a unified view of data also helps improve a business’s efficiency and customer service.

For instance, sales teams deal with different data sources that provide valuable customer information. However, going through these different sources or applications individually to learn about a customer can be exhausting and inefficient. They can end up with corrupt data, leading to incorrect analysis. With a customer data integration system, the sales team can easily access 360-degree views of their customers, enabling them to personalize their services, close more sales, and turn the vendor-customer relationship into a strong partnership.

How does data integration work?

On the surface, the concept of data integrations looks simple, but in reality, it can be complex and challenging to achieve. Mainly because many organizations no longer rely on a single database for data maintenance. Instead, they use different data servers, from structured and unstructured data to master and transactional data.

Data integration kicks off with different data servers reaching out to a server cluster. In turn, the server cluster makes an API request to an external system to use synchronous software or asynchronous parallel-processed paradigm in ingesting data into a staging area or data lake. At this staging area, data cleansing, mapping, and transformation occur. Afterward, the data is unified or duplicated from the different sources into an enterprise data warehouse or a new data source system.

Different types of data integration.

  1. Data Virtualization: Data virtualization involves retrieving and manipulating data without moving it physically. Here, the user gains a consolidated view of data, almost in real-time, through a single interface despite the data remaining in different data source systems.
  2. Data warehousing: This type of data integration involves the cleansing, formatting, and storage of data using a data warehouse. Analysts commonly use data warehousing in comparing consolidated data from various heterogeneous sources to gain insight into an organization.
  3. Data Consolidation: Data consolidation uses the ETL (Extract, Transform, and Load) data integration tool to combine data from multiple source systems into a single data source. The ETL software helps make the data more accessible by transforming it into a usable format before loading it into a data warehouse for users to report and query the unified data view.
  4. Middleware data integration: This data integration approach uses the middleware application as a mediator for normalizing data and moving it into a master data pool. The middleware application helps format and validate the data before moving it to the cloud data warehouse or a database. Middleware is helpful for situations where the data integration cannot access data from an application on its own.
  5. Application-based integration: Application-based integration uses software applications to locate, extract, and integrate data. During this integration process, the software uses the different data systems to create data that is compatible with all the different systems and the destination system.