Applying AI to Enterprise Data Integration

AI in data integration can easily analyze data in all formats, and generate more accurate data models and pipelines, besides automating data transformation mapping creation in the extract-transform-load (ETL) process, thus highlighting key trends.

Fremont, CA: In the coming years, current technological developments and the expanding IT sector are expected to drive the global artificial intelligence (AI) in the data integration segment.Businesses tend to generate large amounts of data from traditional data sources such as social media posts, streaming data, non-traditional data sources such as file system data, ERP, CRM, and RDMS. This ongoing data generation requires efficient data integration methods to develop insights. Preparing data takes enormous resources and time, so an efficient way to process and analyze data in less time is needed. AI in data integration is considered an efficient strategy to implement automation in the data preparation activity and use efficient data analysis of big data.

The conventional data integration was not able to deal with a large amount of data and unstructured data to generate valuable data insights. AI in data integration can quickly analyze the data of all formats to generate more accurate data models and data pipelines and can also automate the data transformation mapping creation in the extract-transform-load (ETL) process. The hidden trends can be easily identified from the large data sets.

Besides, AI can also automate data transformation processes using pre-built data integration templates and system metadata catalogs. Microsoft Corporation, Amazon Web Services, Google, IBM Corporation, Adeptia Inc., Oracle Corporation, Talend Inc., SAP SE, Snaplogic are the leading players in global AI in the data integration market. These market players are making significant investments in technological innovations and are actively using machine learning technology and AI to solve complex data integration challenges.