Five Pitfalls That Can Derail Your Big Data Project

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Some of the data you need for your Big Data projects is clearly identified, such as transactional data used or generated by business applications. However, more of this data is hidden in log files, manufacturing systems, desktops or various servers; this is what we call “dark data." Some of it is even going to waste in the exhaust fumes of IT. This “exhaust data” from sensors and logs is purged after a certain amount of time, or never stored in the first place. All of it is potentially relevant. Don’t restrain your project to the first category: Inventory dark data, and deploy collection mechanisms for exhaust data, so that they become value contributors as well.

Most organizations are still in the early stages of Big Data adoption, and few have thought beyond the technology angle of how Big Data will profoundly impact their processes and their information architecture. Whether Big Data projects are past the pilot stage and being deployed in production, or still on the horizon, they require strategic thinking and adequate planning to avoid some now-typical pitfalls that tend to get in the way of success for big data projects.

Talend recently identified five key areas companies should monitor to avoid pitfalls that can derail big data projects, and ensure those projects generate value as they move past the early pilot stages.

 

Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

 
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