The wrong way: Worst best practices in ‘big data’ analytics programs
Rick Sherman writes:
“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.
Rick gives seven reasons why “big data” analytics projects fail:
- “If we build, it they will come.”
- Assuming that the software will have all the answers.
- Not understanding that you need to think differently.
- Forgetting all the lessons of the past.
- Not having the requisite business and analytical expertise.
- Treating the project like it’s a science experiment.
- Promising and trying to do too much.
Seven reasons that should be raised when the NSA Money Trap project fails.
Because no one has taken responsibility for those seven issues.
Or asked the contractors: What about your failed “big data” analytics projects?
Simple enough question.
Do you ask that question?