5 Reasons Why You Suck at Predictive Analytics

reasons why you may suck at Predictive Analytics

We see it every day… Customer X embarked on a grand in-house initiative to implement Big Data analytics. $X million and Y years later, they can predict nothing, aside from the fact that their consultants seem to get fatter with every passing month, and the engineering guys keep asking for more time and bigger budgets. It’s understandable… These days, cutting through all the noise related to big data analytics can be difficult. The bloggers, analysts, and consultants certainly make it sound easy, yet the road to successful predictive analytics implementations seems to be littered with the corpses of many a well-intentioned executive.

Many of our customers come to us, after they have spun their wheels in the mud trying to implement big data projects on their own or with the help of the talking heads. Below is a list of what I believe to be the top reasons for failed projects with buzzwords omitted:

  1. Data Science and Engineering alignment Fail: Or… The fear that engineering will cannibalize data science. After all, “If I can automate Data Science, why do I need the Data Scientists?”, the reasoning goes. Aligning both camps is difficult in larger organizations, as turf-wars will erupt.  Analytics software should seek to include the data science day-to-day activities rather than exclude them.
  2. Your data sucks: Nothing can save you here. If your vendor/manufacturer is providing shoddy data, you won’t be able to predict or analyze anything. Any consultant that tells you otherwise, is selling fertilizer, not analytics. It is best to reach out to your data generator / vendor and work with them to fix the root of the problem.
  3. You hired IBM: Watson does well with Jeopardy questions, but sadly couldn’t even predict the most recent round of IBM layoffs.
  4. You build when you should buy: Predictive Analytics is really hard, and chances are that it’s not your core competency, so why are you bringing all of this in-house? The real short term costs of implementing and maintaining custom software, data science groups, engineering groups, and infrastructure costs, can easily eat away millions of dollars, and you’re placing really big bets on being able to hire the high-level talent to pull it off.
  5. Operations Misalignment: Predictions are useless unless there is a someone or a some-thing to act on the results. It’s important to make operations a partner in the initiative from the onset. Increasing operational efficiency is the goal here, so really… Operations is the customer. A tight feedback loop with ongoing implementation between both camps is a must.

And so that’s the gist of it – 5 bullets forced out of me at our marketing department’s insistence. As much as I enjoy mocking the hype-masters in the industry, these days I find myself extremely busy helping build a real startup, solving real-world problems, for the Fortune 500, for real dollars. 😉