21; or a different number

In COVID times, we are constantly bombarded with figures, statistics, and bold claims. How many people will die? On average, how long do patients stay in ICU? How many ICU bed will be needed? How long will it take to flatten the curve?

Some of these figures will turn out to be true. Others less so. And many will be structurally biased.

This is best illustrated by an innocent example: The most successful weatherman is not the one who makes the most accurate prediction of rain or shine, but the one who predicts rain a bit too often. No-one will blame him when it turns out to be a sunny day. A faulty prediction of sunshine will be less appreciated by his audience.

Similarly, it is best to over-estimate the number of ICU beds you need two weeks from now. That is just proper risk management.

Just be aware: correctly interpreting the figures you see in the news may require game theory as much as it requires statistics.

V for Variance

Turn data driven decision making into continuous learning

Most humans dislike change. And continuously ongoing change is even worse.

What does this mean for data driven decision making?

First of all, you can count on a lot of resistance when you roll-out AI-driven solutions: Can the models be trusted? Is my professionalism still valued? Will I lose my autonomy? However important to address, managing such concerns is not my topic here.

Suppose that you have made it to a full roll-out unscratched. Analytics drive key decisions. Benefits are measurable. Most likely, your decisions will become more structured. While predictive and prescriptive analytics can unlock great value, these come with a risk.


Everyone in your organization will love it when little changes. That is, unless your company is truly digital. Stability will create the suggestion that everything is under control. That targets will be met and nothing can go wrong.

By contrast, statistical models live by change. They need to observe change to predict change. And that means that you should be consciously creating the variance you need to continue learning.

Luckily, there is a lot of change that occurs naturally. Customers change their ways. Stores do not execute recommendations. Suppliers’ price hikes are charged-on to customers. You name it. Although it is a good start, this type of variance may be mightily skewed. Or not representative for what you want to learn. In other words, you most likely will need a different kind of variance. To turn data driven decision making into continuous learning, you need to have a strategy for conscious, targeted, and ongoing experimentation and testing.

Remember, remember: learn to love the unexpected.

The Data’s Advocate

How to assure that insights change business decisions

The Board has decreed that you have to become a data-driven organization. To avoid obsolescence, things need to change. The old way of doing business is no longer viable. Only Data can make you smarter.

So, there you go. An Analytics department is set-up. A Big Data platform is is put in place. Data scientists are hired. Models are fitted. Insights flood the organization. And after a while all graphs start pointing to the top-right corner. Right?

Nope: Sales wants to sell, Operations wants to operate, and Marketing wants to do whatever it is that Marketing wants to do. No-one ever wants a proper analysis. Especially if the outcome is likely to challenge the status quo. There is a shop to run, a client to manage, and a problem to fix. If Analytics does not directly help to do just that, it is deemed useless. And everyone who does not get that, frankly, does not understand the business. That’s how, in many organizations, Data is side-tracked.

The first priority, is of course to assure that your insights are relevant and focus on improving key business decisions. But that is not enough. These insights should actually change the decisions your organization makes. And frankly, many business owners are unable to take an impartial perspective with respect to unexpected challenges – especially when under pressure.

What is needed, is someone who can ensure the fact-based perspective is taken into account in decision making.

Someone needs to defend data-driven insights without looking for compromises from the start. Someone needs to be in a position to challenge Category Management on their Sales v. Margin trade-offs. Someone needs to hold firm on the risk assessment in the face of an exciting Sales opportunity.

In short: you need a Data’s Advocate.

That is not to say that Data should always prevail over ‘Gut’ or ‘Experience’, or ‘Entrepreneurship’, or whatever you call it. But the trade-off should be an explicit one. Both to assure better decisions, and to build awareness on what it means to be a data driven organization.