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The wrong way: Worst practices in 'big data' analytics programs

“Big data” analytics is hot. Read any IT publication or website and you’ll see business intelligence (BI) vendors and their systems integration partners pitching products and services to help organizations implement and manage big data analytics systems. The ads and the big data analytics press releases and case studies that vendors are rushing out might make you think it’s easy -- that all you need for a successful deployment is a particular technology.

If only it were that simple. While BI vendors are happy to tell you about their customers who are successfully leveraging big data for analytics uses, they’re not so quick to discuss those who have failed. There are many potential reasons why big data analytics projects fall short of their goals and expectations. You can find lots of advice on big data analytics best practices; below are some worst practices for big data analytics programs so you know what to avoid.

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BI training too important to skip

Lauren Gibbons Paul interviewed Howard Dresner and me for an article on training, which was published in SearchManufacturing ERP.com this week:

Training is the final step of a software implementation and the one everyone loves to skip. The scenario is all too common. Companies spend time up front justifying the purchase, selecting the software and implementing it and then decide they’re done -- giving short shrift to the crucial last piece.

When it comes to business intelligence (BI) software implementation, this mistake could render the whole undertaking null and void. BI training that goes beyond the tool’s technical features is crucial to helping employees gain the insights they need to do their jobs better, industry observers say.

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Speaking of BI training, some things never change. The situation is not so different from years back when I wrote this article: Training? We don't need no stinkin' training ...