Data integration solutions focus on bringing together data from multiple sources, such as databases, applications, and external data streams, into a single source of truth. Service providers use tools such as ETL (extract, transform, load) and API integrations to create a unified view of data across an organization.
With the market becoming more competitive than ever, organizations need to embrace big data and all its benefits. Data integration helps in managing all of these giant datasets to provide complete and accurate information. One of the most common use cases of data integration is in the management of business and customer data. It helps to support business intelligence and advanced analytics with a complete picture of financial risks, key performance indicators (KPIs), supply chain operations, and other important business processes.
Another important role of data integration is in the IT environment to provide access to data stored on legacy systems. There are a number of modern big data analytics environments (eg: Hadoop) that are not compatible with the data in legacy systems. Data integration can help bridge that gap between valuable legacy data with popular business intelligence applications.
Perhaps the greatest challenge to data integration methods is to integrate the data stored in legacy systems or mainframes.
There are a number of new systems today generating different types of data from a multitude of sources – IoT devices, cloud, sensors, etc.
For any organization to flourish, it cannot always depend on its own internal data. There are a number of external sources that organizations have to take in in order to stand out from their competition.
Although you may already be using data integration solutions for your organization, there is the unfortunate trap of using the “wrong” type of software.
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