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Data Management // Big Data Analytics
01:00 PM

Where Analytics Projects Go Wrong

Of all the things that can go wrong with an analytics project, it appears that failure to properly define the business problem is the runaway leader.

Walk the exhibit floor at any tech trade show and you will won't be able to ignore the noise about big data and analytics. The solution vendors virtually shout, "Pick me, pick me. You can't go wrong."

But "going wrong" with an analytics project may not even reach the point of vendor selection. It appears that the real pitfalls for a data project are independent of technology choice.

Credit: Pixabay
Credit: Pixabay

So far, the voting in our current All Analytics Unknown Document 281807 makes it clear that respondents see the biggest threat to a project's success in the earliest of stages, identifying the business problem that needs to be solved. As of this morning, 40% of voters listed the problem definition process as the greatest danger point for an analytics project. It was followed by "data sourcing" at 25.7%.

Of course, this doesn't mean that just any data technology will do the job. Rather, two-thirds of our voters seem to be saying that if you don't get off on the right foot with the right data for the right business problem, what happens after that -- technology choice, delivering business results, etc. -- just doesn't matter so much. Mess up the first stage and nothing that follows will fix the mistake.

When it comes to identifying the business problem, one trend I've noticed in numerous case studies is that defining the problem takes hard work. In many instances, the real problem isn't the one that seems most apparent. Instead, that real problem is a subset or a dependency of what seems to be an obvious issue.

How does that play out in real life?

Take the case of slow sales in a given region. The immediate management assumption is that the local sales force is doing something wrong, and the executives will want data showing just what the sales team is doing wrong in comparison with what other teams are doing. However, some initial exploration could lead the analytics pros to look beyond the actual sales strategy.

The problems could be highlighted in customer social media posts complaining about product pricing, or in tech support calls that indicate product quality issues. So, the real problem could be in customer satisfaction rather than in the sales strategy or talent.

I know, it seems logical that a manager should take a holistic view of the problem and work to narrow down the real issues. But, have you ever witnessed a manager ignoring logic?


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