Data analytics done wrong: Avoid sowing the seeds of failure

Health goals CSC Blogs

Enterprises that launch analytics programs with the expectation that their businesses will be transformed often are disappointed with the results, which in turn leads to disillusionment and cynicism about analytics.

But in most cases, it’s the enterprise that undermines analytics, rather than the other way around. As analytics consultant and expert Meta Brown writes in Forbes, “Analytics failures nearly always have the same root cause: there was never a realistic plan for success.”

Effective planning involves several crucial elements, including defining a business problem, designing a strategy, determining a process and setting a goal. Sort of like how enterprises are supposed to do everything!

“Approach analytics as you would any other business investment,” Brown advises. “Make a business case, and a plan for success. It’s the surest way to avoid analytics failure.”

Another part of being realistic — particularly for enterprises that are new to analytics — is to start with a smaller project in order to learn the ropes and gain experience. This will give the next, more significant analytics initiatives a better chance for success because you have something on which to build.

One of the major challenges with any analytics initiative is figuring out which data will help enterprises understand a problem and lead them toward a solution. “Decide what you have to do to get the data, and do it,” Brown writes.

Planning and precision are essential to an analytics program, she says:

“Prepare a summary of the business problem and its impact on the organization. Define goals for the analytics project, keeping them reasonable and modest (you can always be a hero by exceeding them). Make a rough plan of the analysis to be done.”

And just in case Forbes readers didn’t get the message, Brown reiterates, “Let me say this again: Write down every element of your plan, including the goals.”

The planning and preparation work done in the early stages of an analytics program can mean the difference between success and failure. That extra effort up front can pay off exponentially down the road.


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