[Forbes] Where Big Data Projects Fail

</div><div style="clear:both">Where Big Data Projects Fail

Failure can happen for many reasons, however there are a few glaring dangers that will cause any big data project to crash and burn. Based on my experience working with companies and organizations of all shapes and sizes, I know these errors are all too frequent. One thing they have in common is they are all caused by a lack of adequate planning.

So, in no particular order, here are some of the most common causes of failure in business big data projects that I’ve come across.

Not starting with clear business objectives

It’s easy to get caught up in hype – and Big Data has certainly been hyped. When so many people (including me) are shouting about how earth-shatteringly important it is, and anyone not on board is likely to sink, it isn’t surprising that a lot of people start with the “how” without first considering the “why”.

What people who fall into that trap often failed to appreciate is that analytics in business in about problem solving – and first you need to know what problem you are trying to solve.

I worked with an airline which had thrown itself into a range of Big Data projects with great enthusiasm – cataloguing and collecting information on everything from meal preferences to the impact delays would have on drinks orders. Another client – a retailer – had 258 separate data projects on the go when they called me in. Some were interesting – such as by mining all of their stock and purchase data they had found that a particular bottle of wine sold exceptionally well on a Tuesday, and even more so if it was raining. But so what?  The issue is that shelf-space to pre-assigned and can’t be increased for this brand for just this one day. The only option is to ensure the allocated shelf-space is regularly restocked on Tuesdays. In isolation that insight isn’t going to provide them with huge growth or positive change.

Sometimes you will get lucky and hit on an interesting insight taking this approach, but it’s highly inefficient. In fact it’s a bit like sitting an exam and not bothering to read the question, simply writing out everything you know on the subject and hoping it will include the information the examiner is looking for.’

By Bernard Marr
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Source: forbes.com

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