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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.

Stability.

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.