Data integration involves a series of steps and processes that brings together data from disparate sources and transforms it into a unified and usable format. Here's an overview of how a typical data integration process works:

• Data source identification: The first step is identifying the various data sources that need to be integrated, such as databases, spreadsheets, cloud services, APIs, legacy systems and others.


• Data extraction: Next, data is extracted from the identified sources using extraction tools or processes, which might involve querying databases, pulling files from remote locations or retrieving data through APIs.


• Data mapping: Different data sources may use different terminologies, codes or structures to represent similar information. Creating a mapping schema that defines how data elements from different systems correspond to each other ensures proper data alignment during integration.


• Data validation and quality assurance: Validation involves checking for errors, inconsistencies and data integrity issues to ensure accuracy and quality. Quality assurance processes are implemented to maintain data accuracy and reliability.


• Data loading: Data loading is where the transformed data is loaded into a data warehouse or any other desired destination for further analysis or reporting. The loading process can be performed by batch loading or real-time loading, depending on the requirements.


• Data synchronization: Data synchronization helps ensure that the integrated data is kept up to date over time, whether via periodic updates or real-time synchronization if immediate integration of newly available data is required.


• Data governance and security:When integrating sensitive or regulated data, data governance practices ensure that data is handled in compliance with regulations and privacy requirements. Additional security measures are implemented to safeguard data during integration and storage.


• Metadata management: Metadata, which provides information about the integrated data, enhances its discoverability and usability so users can more easily understand the data’s context, source and meaning.


• Data access and analysis: Once integrated, the data sets can be accessed and analyzed using various tools, such as BI software, reporting tools and analytics platforms. This analysis leads to insights that drive decision making and business strategies.