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"Computers are useless. They can only give answers."

That famous quote comes from an unlikely source: artist Pablo Picasso. If you think statistics is a subject for the numbers geeks, you're missing the point. It's about so much more than that. Back in the days when computer storage space was precious and costly, the phrase "fact-based decision-making" hadn't been born yet. Managers were prized for their "golden gut," or the ability to make decisions with good outcomes in spite of a lack of information. Today most companies understand the value of managers with superior quantitative abilities, coupled with leadership, organization, communication and other key skills. The age of "quant" is responsible for the proliferation of MBA's, as well as job notices that declare, "MBA preferred" as a qualification. While those who understand the more subjective art of decision-making may decry a quantitative approach as mechanical and limited, it's hard to argue with the facts.

Plan

In this age of technology, you might assume that decision-making has been largely relegated to computers, but this is not the case. As Picasso pointed out, computers "can only give answers." While this sounds like the whole enchilada of making good decisions (if you have the answers, what else do you need?) it's not even close. The Plan stage incorporates understanding the question, first and foremost. That's what draws the line between an entry-level report jockey and a rock-star analyst (and by analyst here, I'm including managers who are using analysis). One person answers questions; the other asks. If you have access to the right data, answering questions is the easy part. Knowing what questions to ask is what defines the superior business person and it is what defines excellent management skills. This is not the part of the equation that can be "farmed out." Neither the computer nor a technical whiz kid can serve as replacement for the experience, judgement, and business acumen required to ask the right questions.

If you are using experimental design to create advertising campaigns or using a stats package to predict returns on financial products or leveraging web analysis software to optimize your website's ROI, you understand the complexity and technical know-how required to turn data into metrics. But you also know this isn't the end game. Just search on the term "web analytics" and you will find article after article about companies that have reams of metrics and data that they don't know what to do with. The actual "doing" of statistics is the easy part, relatively speaking.

Report

Interpreting the results of any analysis is an area fraught with danger. This is where the best planned analytic project can fall apart. Drawing inaccurate or illogical conclusions will yield disastrous results, from wasted effort and investment to a failed business model. Statistics are like nature: you can believe whatever you want to, but it doesn't change the truth. Knowing how to take analysis and turn it into good decisions is far more critical than selecting the right software package. Yet companies will scrimp, nickel and dime on employees and their training, all the while launching six month committees and appropriating seven figure budgets to buy the right web analytics/business intelligence/statistics/[insert your company's pet project here] software.

Act

You might believe that execution made as a result of analysis is the last step in a linear process, but here again is a place where many companies lose. Having a cyclical process with measurement plans built into the implementation phase creates an environment where a company can make decisions an organic part of the process and, as a result, remain competitive and in touch with the customer base. Payoff: better ROI, loyalty, market leadership, etc.

So, while you may be able to hire great analysts and buy the latest great technical solution for the "doing" part, there's no substitution or shortcut for talented, experienced, bright decision-makers who know what questions to ask, what metrics to track, how to interpret analysis and who are absolutely committed to implementing plans that include systems for measuring their effectiveness along the way.

Lather, rinse, repeat. Forever.

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"Computers are useless. They can only give answers."