What Is Data Integration & How It Works?

One of the most major factors in a company is data and information. These are repeatedly called up, analyzed, and processed by employees in many different work steps. The problem with this is that this data is usually stored in different systems, which is why accessing it takes a lot of time. This is where data integration comes in. It aims to integrate data from different systems into one tool so that employees can access all information from one data source. You can read how this works and what it brings for companies in this article.

Definition:

Companies use many different systems and tools at their respective locations to manage their areas of responsibility. Customer relationship management in customer service, a campaign overview tool in marketing, customer applications, sales systems, etc. – they all have in common that they contain a lot of important data. This information is in good hands in the applications, but what if, for example, an employee in sales needs all the information available about a customer? Including the promotional emails received, the calls made so far, the content of the conversation and the last purchases made? The sales employee has to log in to many different systems, which leads to increased workload and is often not feasible due to the workload.

This is where data integration comes into play. It brings together all data and information from the various sources in a single view. A process is run through that includes the recording, cleaning, mapping, and transformation of the data to match the target system.

The advantages of data integration are manifold:

• The collaboration and standardization of different systems is improved
• The company’s employees save a lot of time through the uniform view of the data.
• The susceptibility of the data records to errors is minimized.
• More useful data can be accessed at the same time.

The goal of the company is to increase the clarity so that decisions can be made more easily, to ultimately remain competitive.

Differentiation From System Coupling

As the name suggests, data integration integrates data and information from many different sources into one view, with the effect that the number of interfaces to other programs is significantly minimized. That is the difference between the system coupling.

The system coupling brings together different systems and tools in one view without actually having integrated the data into this application. The system coupling tool is therefore only one link between many different systems, which increases the number of interfaces. It doesn’t have to be negative per se, it’s just more prone to failure. The great advantage of system coupling is that the merging of the systems works much faster than the complete integration of the data in one tool.

How Does Data Integration Work?

The integration of the data brings with it some challenges that companies have to cope with. It starts with the need to be clear about the types of data that will be integrated and what to do with them. Do they need to be available for special analyzes, are they for reference purposes, and how often do they need to be updated? In addition, a lot of data is stored in systems that are out of date. You are missing information such as time and date information. The situation is similar to data from external systems, which often lack details that are available in internal data. 

To be able to respond to all requirements, companies can choose from various data integration options:

• Manual data integration: With manual data integration, all data from the various sources is compiled by hand. Due to the high effort involved, this process is only useful for small companies with very little data.
• Middleware-based data integration: With this form of data integration, the middleware tool acts as a kind of middleman, collects the data, and transfers it to the master data pool. This is mainly used when the actual data integration tool has problems with certain data sets, for example, because they come from outdated systems.
• Application-based integration: Different software is used here to localize and integrate data. To do this, they convert the data into a form compatible with the target system.
• Uniform Access Integration: In Uniform Access Integration, the main component is a front end that displays all data from the various sources in a uniform manner, while the actual storage location remains the source. The data are therefore only displayed in the same way, while they remain essentially different.
• Common Storage Integration: Common Storage Integration is the continuation of Uniform Access Integration. Copies of all data records are made and integrated into the target system. There they are then displayed in a uniform view.

When it comes to data integration, you have to pay attention to a few things and decide on a lot. To make the process as uncomplicated as possible, a data integration tool is recommended.

Also Read : Big Data And Analytics: Definition, Infrastructure, Best Practices, And Use Cases In The Company