Data integration is the process of combining data from multiple sources into a single, unified repository. It allows organizations to access, manage, and analyze disparate data sources. Data integration is considered an important factor for effective business decision making.
By unifying disparate data sources, it allows the ease of analysis and comparison of different ‘slices’ of information. It integrates the data, regardless of the underlying database or system structure. Data integration also serves to normalize data, making it easier to compare two or more data points.
The data integration process typically involves a combination of both manual and automated processes. Examples of manual processes include the loading of data into the data warehouse or the transfer of data between programs. Automated processes include the scheduling of tasks to run on an ongoing basis, such as periodic replication of data or data extraction.
Data integration is used to support business operations and enable organizations to make more informed decisions. For example, it can provide a “360-degree view” of a customer or supplier by combining multiple sources of information. This makes it easier to anticipate market trends, forecast sales, and optimize marketing campaigns.
Data integration can be used not only within an organization’s enterprise system, but can also extend beyond the organization, providing access to real-time, external sources of data. This “extended integration” can enable an organization to benefit from the collective data of external partners, suppliers, and customers.
Data integration is becoming an increasingly important component of organization’s technology infrastructure. To ensure the success of data integration projects, organizations are encouraged to put in place proper planning, governance, and security measures, while taking advantage of the benefits of open architecture and shared standards.
