Data integration software is a process that combines data from multiple sources and provides users with a unified view of them. In this process, data is retrieved from multiple sources and is combined in such a way that it can provide consistent, comprehensive, and current and correct information for business reporting and analysis.
A data integration solution takes different data types (data sets, documents, and tables) to be merged by users, organizations, and applications, for the use of personal and business processes or functions. Data integration covers several different sub-areas such as:
- Data warehousing
- Data migration
- Enterprise application/information integration
- Master data management
Importance Of Data Integration
Data integration is important for commercial (such as when two similar companies need to merge their databases) as well as scientific (combining research results from different resources) domains.
Data integration encourages collaboration between internal as well as external users. It combines data from several sources that are stored using various technologies.
Benefits of Data Integration
The use of data integration has the following benefits.
- It improves collaboration and unification of systems.
- It saves time and boosts efficiency.
- It delivers more valuable data and reduces errors (and rework).
- It simplifies business intelligence processes of analysis.
The ingestion process is the first step in data integration. It includes steps such as cleansing, ETL mapping, and transformation. Data integration solutions involve a few common elements such as a master server, a network of data sources, and clients accessing data from the master server.
In the process, the client sends a request to the master server for data. The master server then takes the required data from internal and external sources. The data is extracted from these sources and consolidated into one coherent data set. This is served back to the client for use.
In the data integration process, collecting important data might not be much of a challenge in the modern world, but properly integrating that data into a meaningful and structured format remains challenging.
Challenges faced during Data Integration
It is a challenging task to gather data from several sources and transform them into meaningful information. Currently, enterprises are generating different types of data (unstructured or real-time) from all kind of sources such as videos, IoT devices, sensors, and the cloud. It becomes critical for businesses to adapt to the data integration infrastructure as the data obtained from varied sources differs in terms of volume and format.
