8 Critical Elements Of A Successful Data Integration Strategy - InformationWeek

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10/6/2016
07:06 AM
Jessica Davis
Jessica Davis
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8 Critical Elements Of A Successful Data Integration Strategy

Integrating data from multiple sources that employ different structures and schema has always posed complex, messy problems for IT professionals. Today's growing volume of data and data types made things even more complicated. Here are some key tips to help your organization integrate its increasing amounts of data.
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Best Friends: Data Integration And Application Integration
Data integration and application integration have traditionally been treated as separate efforts, but change is underway, according to Brian Hopkins, VP and principal analyst serving enterprise architecture professionals at Forrester Research. He cites some pioneering vendors that are building data integration into business process flows. 'Big data processing and cheap memory make it possible to store data in its raw or nearly raw format and do complex integration operations on it in memory and just in time,' Hopkins wrote  in a recent data integration report. Leveraging this new architecture can be less painful than the data integration effort necessary to create traditional data warehouses and even data lakes. 
(Image: Maxiphoto/iStockphoto)

Best Friends: Data Integration And Application Integration

Data integration and application integration have traditionally been treated as separate efforts, but change is underway, according to Brian Hopkins, VP and principal analyst serving enterprise architecture professionals at Forrester Research. He cites some pioneering vendors that are building data integration into business process flows. "Big data processing and cheap memory make it possible to store data in its raw or nearly raw format and do complex integration operations on it in memory and just in time," Hopkins wrote in a recent data integration report. Leveraging this new architecture can be less painful than the data integration effort necessary to create traditional data warehouses and even data lakes.

(Image: Maxiphoto/iStockphoto)

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