Analytics is all about numbers… or is it?

Analytics is about numbers. But it isn’t just about numbers. It’s also about people.

An analytically inclined manager once shared an anecdote about his troubles getting sales agents to use their laptop computers. Some of the agents had been around for decades, and the idea of using laptops wasn’t just foreign, it was unthinkable. In one particular meeting a sales agent showed up and declared that he’d finally broken down and used his laptop to write a memo. He then proceeded to demonstrate. Laying the computer on his lap, he pulled out a sheet of paper and wrote a memo with his pen, using the laptop as a writing surface.

The behavior wasn’t appropriate, but it was a sign of more than unwillingness to change. Analytics is second nature to individuals with training in data analysis, but it can be alien to those who aren’t. Today, computers are ubiquitous at all levels of education. However, an abundance of individuals remain who’ve never encountered computers in school — much less the analytics they enable.

Further, many people fundamentally aren’t comfortable with mathematics — the foundation of analytics. It’s not hard to find people who will happily share their anxiety about mathematics, from the algebra classes they failed in high school to the statistics classes they dropped in college. Mathematics comes naturally to some and not so naturally to others.

Fear of mathematics is only part of the equation. People also fear loss of control. An individual who’s responsible for the placement of containers on a cargo ship may well resent a computer telling him how to do his job. He’s done it for years and has developed considerable expertise. Why should he listen to a computer any more than he should listen to a novice?

All analytics projects impact people, and people are often the key factor in whether an analytics project is successful or not. This often comes as a surprise. After all, analytics projects must deal with computers, data quality choosing and building an appropriate mathematical model. Aren’t these greater challenges than those posed by people?

Usually not. Good analytics practitioners can understand and address technical challenges. Technical challenges can slow a project down and may require changes to models, but technical challenges rarely cause a project to collapse. Poor people management can lead to failure and damage careers.

So what can be done? People issues aren’t unique to analytics projects, but analytics projects have their own special challenges. Some thoughts to keep in mind:

• Communication. Keep stakeholders informed. Once isn’t enough. A regular flow of information is required — in both directions. Familiarity doesn’t breed contempt; it breeds familiarity. Stakeholders will come to accept an analytics project and the project team over time. If not, problems will be uncovered early and steps can be taken to rectify them.
  
  
• Keep a list of stakeholders. General attitudes of different parties can usually be discerned early in a project. Is an individual an avid supporter? Needs convincing? Dead set against the effort? A plan should be developed for every group of stakeholders, and revised and acted upon at least weekly. Individuals who are strong supporters and opponents should be ascertained and receive special treatment. Analytics projects are unique in that the results aren’t always obviously right or wrong. The people in a payroll department may like or dislike the design of new payroll software, but everyone can agree if the amounts on the checks that go out are right or wrong. An analytics project to set prices for a company’s goods may come up with different numbers than those a pricing analyst would determine. Whose prices are better, those of the pricing analyst or the analytics professional? This naturally leads into our next issue.
  
  
• Education. Practitioners with years of experience won’t accept numbers that come from a black box. The black box needs to be opened, and this can only be achieved through education. Education can be a challenge since many of the tools used by analytics professionals are, by nature, quite mathematical. Creative education is often required, where important concepts are taught without mathematical formalism. Games and interactive brainteasers can be very effective in this regard.
  
  
• Incentives. The best analytics project can be thwarted by a poor incentive structure. If a sales force receives bonuses based on volume, sales agents will work diligently to keep prices low for their customers, even if analysis of the numbers suggests raising prices. When it’s clear that incentives directly impact the success of a project, they must be addressed.
  
  
• Reassignment. In spite of a project team’s best efforts, there often remain individuals who, for one reason or another, fail to embrace the changes brought about by an analytics project. This impacts not only their own work, but also the work of those around them. The problem can be especially disruptive in an analytics project since, as discussed earlier, there’s generally no absolute right or wrong. Legitimate concerns should be aired, but when the activity is counterproductive, it’s best to make personnel changes rather than let the problem fester.

Above all, it’s important to stay focused on the ultimate goal: project success. Success is measured by an organization using better numbers — numbers that lead to lower costs or higher margins. But these numbers will only be used if they’re accepted by the organization. That’s why it’s important to address that most challenging element of most projects — the human element.