How random is our daily life?

Maybe you’re riding the subway one morning on your way to work and by chance in the crowd of people you see someone you know, someone who you last saw 20 years ago. What a coincidence! Another situation: you rush to get to your local bakery. Despite the large number of customers, the baker always has enough of the delicious, fresh bagels and rolls that you want. Coincidence? Two completely different situations, yet we use “chance” when talking about both of them. But are they really random?

Uwe Weiss_final

It was in the 17th century that the term chance first appeared, when it was used to describe something that happens without an apparent cause. But in an age when what we do is being more and more influenced by big data, computer models and algorithms, how much “chance” remains? How random is our daily life? And do we even want to have random experiences?

For enterprises, unforeseen situations obviously bring with them unforeseen risks, which — depending on the situation — can have serious effects on sales. And in fast-moving times like ours, risks can have dramatic effects on the entire enterprise. Wanting to be able to predict the future is nothing new, and in the past people used various methods, such as reading tea leaves and gazing into crystal balls in an attempt to foresee events. But as a business person, I don’t want to rely on tea leaves or a crystal ball or even an educated guess because that would really make life in a digitalized world a risky game of chance.

In order to eliminate this chance factor, more and more enterprises are using algorithms. These clear action rules that serve to solve a problem or a class of problems consist of multiple, well-defined, individual steps. But when we talk about algorithms, most people think of Amazon, Google, or the NSA scandal. Not many talk about the algorithms that are already playing a role in our everyday life.

But let’s get back to the bakery. Of course the owner (as well as other business operators) wants to offer his customers the best range of goods during opening hours, but he can’t really know how many people will want which product (a sunflower seed bagel, for example). The bakery doesn’t want to have too much left over, while at the same running out of stock during the day can mean lost sales or even customers.

Some would argue that the bakery could just plan what to have in stock on the basis of experience. However a forecasting algorithm includes in its analysis a staggering number of factors — and these can quickly add up to 60 or 70 — that increase customer demand for certain baked goods. This has to be done for all the store’s locations; something that would be impossible for one person to do, even for a single product. A newly developed cyclic-boosting algorithm can support bakeries in forecasting all product/location combinations accurately, in order to prevent overstock as well as out-of-stock situations.

So can we really say that the algorithm defines our lives? It will certainly make life more and more technologized and digitalized, but ultimately it’s the customers who decide if and when they’ll buy a sunflower seed bagel. Would you like to know more about how value is gained from data and which paths lead to a data-driven enterprise? If so, I’d like to recommend our new e-book, which uses 10 data stories to show how enterprises use predictive applications to successfully master digital change.

Uwe Weiss Uwe Weiss

CEO , is the visionary at Blue Yonder . His aim is to bring together the top class in Data Science and Enterprise software in an international market leader .

Big Data, Predictive Applications