Data Integration Definition
Data integration is the process of combining data from multiple sources into a single, unified view. This consolidated view can then be used to support various business needs, such as decision making, analysis, and reporting.
Data integration is typically performed using some type of ETL (extract, transform, load) tool or data management platform. These tools extract data from the source systems, apply any necessary transformations (such as cleansing or aggregation), and then load the resulting data into a target system (usually a data warehouse or data lake). The entire process can be automated so that it runs on a schedule (e.g. daily, weekly, monthly) with minimal manual intervention.
There are many benefits of data integration, including:
-Improved decision making: Having all of the relevant data in one place makes it easier and faster to find the information you need to make decisions.
-Improved accuracy: Data that is consolidated from multiple sources is more likely to be accurate than data from a single source. This is because each source may have its own way of recording and storing data, which can lead to inconsistencies. By consolidating the data into one target system, these inconsistencies can be resolved.
-Increased efficiency: Automating the process of extracting and loading data frees up time that can be spent on more value-added activities such as analysis and reporting.
