9 Consequences of Data Integration Mistakes

Data Integration tools have become vital cogs in an enterprise world dominated by the expansion of data, providing valuable means for companies collect data from outside sources. Given the complexity with how integration software has evolved in recent years, there are a wide variety of ways for enterprises to slip up and make mistakes. Successful integration allows data-driven organizations to better understand and retain their customers, support collaboration between different departments, reduce project timelines with automated development, and much more.

The more businesses lean on Data Integration solutions, the more they rely on their results. In this presentation, IBM, a market leader in this software sector, outlines nine common consequences of misusing or mishandling Data Integration. It’s crucial to sidestep these pitfalls.

 

 

Timothy King
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Timothy King

Editor, Data and Analytics at Solutions Review
Timothy leads Solutions Review's Business Intelligence, Data Integration and Data Management areas of focus. He is recognized as one of the top authories in Big Data, and the number-one authority in enterprise middleware. Timothy has also been named one of the world's top-75 most influential business journalists by Richtopia.
Timothy King
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