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Why the soft side of analytics is so hard to manage

By Arnold Mark Wells

We all love the “hard” side of analytics. What we often struggle with as analysts is what you might call the “soft” side of analytics, which is always more challenging than the “hard” stuff. Here are a few of the reasons why.

Many times, the problem is not insufficient data, defective data, inadequate data models or even incompetent analysis. Often, the reason that better decisions are not made in less time is that many companies of all sizes have some, if not many, managers and leaders who struggle to make decisions with facts and evidence . . . even when it is spoon-fed to them. One reason is that regardless of functional or organizational orientation, some executives tend not to be analytically competent or even interested in analysis. As a result, they tend to mistrust any and all data and analyses, regardless of source.

In other situations, organizations still discount robust analysis because the resulting implications require decisions that conflict or contrast with “tribal knowledge,” institutional customs, their previous decisions or ideas that they or their management have stated for the record. Something to keep in mind is that at least some of the analysis may need to support the current thinking and direction of the audience that is analytically supportable if you want the audience to listen to the part of your analysis that challenges current thinking and direction.

Understanding the context or the “why?” of analysis is fundamental to benefiting from it. However, there are times when the results of an analysis can be conflicting or ambiguous. When the results of analysis don’t lead to a clear, unarguable conclusion, then managers or executives without the patience to ask and understand “why?” may assume that the data is bad or, more commonly, that the analyst is incompetent.

Perhaps the most difficult challenge an organization must overcome in order to raise the level of its analytical capability is the natural hubris of senior managers who believe that their organizational rank defines their level of unaided analytical insight. Hopefully, as we grow older, we also grow wiser. The wiser we are, the slower we are to conclude and the quicker we are to learn. The same ought to be true for us as we progress up the ranks of our organization, but sometimes it isn’t.

If these are the reasons for the organizational malady of failing to fully leverage analytics to make higher quality decisions in less time, what is the remedy?

For the analyst, I recommend the following:

Put yourself in the shoes of the decision-maker. Try to step back from the details of your analysis for a moment and ask yourself the questions he or she will ask.
Engage your decision-maker in the process. Gather their perspective as an input. Don’t make any assumptions. Ask lots of questions. They probably know things that you don’t know about the question you are trying to answer. Draw them out. Schedule updates with the decision-maker, but keep them brief and focused on essentials. Ask for their insight and guidance. It may prove more valuable than you think.
Take time to know, explore and communicate the “why?” of your analysis. Why is the analysis important? Why are the results the way they are? To what factors are the results most sensitive and why? Why are the results not 100 percent conclusive? What are the risks and why do they exist? What are the options?
Make sure you schedule time to explain your approach and the “why?” Your decision-maker needs to know beforehand that this is what you are planning to do. You will need to put the “why”? in the context of the goals and concerns of your decision-maker.
Consider the possible incentives for your decision-maker to ignore your recommendations and give him or her reasons to act on your recommendations that are also consistent with their own interest.
“A picture is worth a thousand words.” Make the analysis visual, even interactive, if possible.
Consider delivering the results in Excel (leveraging Visual Basic, for example), not just in a Power Point presentation or a Word document. In the hands of a skilled programmer and analyst, amazing analysis and pictures can be developed and displayed through Visual Basic and Excel. Every executive already has a license for Excel and this puts him or her face-to-face with the data (hopefully in graphical form as well as tabular). You may be required to create a Power Point presentation, but keep it minimal and try to complement it with Excel or another tool that actually contains the data and the results of your analysis.

Frustration with your decision-making audience will not help them, you or the organization. Addressing them where they are by intelligently and carefully managing the “soft” side of analytics will often determine whether you make a difference or contribute to a pile of wasted analytical effort.

Mark Wells ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it ) is a principal with End-to-End Analytics. For 20 years, he has consulted with the management of global supply chains in the chemicals, medical devices, consumer goods, high tech, automotive and retail industries, as well as the public sector. Wells holds an MBA (focusing in operations management and operations research) from Drexel University. He publishes a supply chain action blog on supply chain and analytics, from which this article is adapted. He is an INFORMS member.