Data Integration

Data Integration is the bringing together of data from different systems, applications and sources into a usable overall context. The aim is to make information consistently available so that processes, analyses and decisions are based on a reliable data basis. This is not only about technical interfaces, but also about data quality, data models, timeliness and business meaning.

In organizations, relevant data is often created in many separate systems: for example in service management tools, financial systems, asset databases, cloud platforms or security solutions. Data integration ensures that this information does not remain isolated but can be connected. This is particularly important when workflows are to be automated, reports consolidated or AI applications supplied with reliable context data. The Data Integration capabilities of a platform help to control data flows centrally and make them traceable.

Good data integration reduces manual reconciliation, avoids media breaks and improves transparency about services, costs, assets or risks. At the same time, it needs clear responsibilities and rules so that data is not only connected technically but interpreted correctly from a business perspective. In IT organizations, integration with a CMDB is often an important building block, because technical relationships and service contexts converge there.

See also: IT Asset Management and Security Analytics.