Let’s imagine we have observations of every day in your life. By using machine learning, we can predict your happiness based on the available variables. We do this by observing results from the past, where we detect the ‘hidden’ rules between combinations of the variables. For instance, if you were happy wearing a “blue shirt + beige pants” combination on a non-rainy workday, it would be recommended to try this outfit again if such a day occurs again. Over time, your brain has developed the ability to make this decision within a split second.

Another technique which mimics the brain is simulation. Suppose we have the choice between the outfits “green shirt + white pants”, “yellow vest + swimsuit”, “red polo + black shorts”. When we need to decide which one to wear on the next day, we can picture ourselves wearing those outfits. Or we test what our previously made machine learning model predicts as “happiness indication”. Based on the outcome, we are able to choose the best outfit.

Using these techniques we can find the best outfit for the coming day. But in some cases, the effort of running these models consciously every day might decrease your happiness more than the occasional bad judgement of clothes. However, in business, large costs come into play when a bad judgement call is made. So using these techniques are actually a huge benefit in organizational decision making.

2. Grocery shopping (pattern mining)

Another example. You are relaxing at home on your nice big sofa, when suddenly you feel hungry. You manage to pull yourself up and stumble to your refrigerator to grab a bite. Unfortunately, it is completely empty, so you go to the store to buy some of your favorite food. But what are you going to buy? Your brain solves this question by performing an algorithm that is known as pattern mining.

Suppose you like buying common products like cheese, ham or marmalade. It is very likely, that you are also buying bread, since you (and many others) know they taste well together. And people that buy bread also buy milk most of the time, because most people in the Netherlands are raised that way and think those products work well together. Everybody makes these connections between products in their head. Think of eggs and bacon, pasta and tomato sauce, etc. It is an example of pattern mining.

By the way – supermarkets would be very happy with this information. It could significantly increase their profits. For example, if they put a huge discount of 50% on ham, they not only get rid of extra stock, but it is likely that more bread and milk will be bought as well. That means a slight increase of the prices on those products results in more money. To figure out which groceries most people buy together, supermarkets keep record of the receipts of their customers.

Finding relations in a constructive and data driven way can result in some valuable hidden gems in almost any branch. Using a smart algorithm that detects all probabilities and power of the relations, these gems can be discovered. For instance, if ham/marmalade/cheese is bought, people will always buy bread. Apparently, milk is always bought together with ham. So, we can deduce that if we increase the demand for ham, the demand for bread and milk will very likely go up as well. A very interesting data science business case.

3. A night in the club (speech recognition)

A third example. Imagine it is finally weekend. You meet up with friends and have a few beers. When relocating to your favorite club, it is so noisy that you can only understand half of what your friends are saying. Despite this discomfort, everyone is having the time of their lives, so you decide to stay there and let your brain handle the speech recognition algorithm in the conversations with your friends.

This means you hear part of a sentence and auto-complete it yourself. Suppose you hear: “Hey, … I get another … for you?”. You automatically fill in the blanks and make an educated guess that you were asked whether you need a refill, so you reply with a nodding gesture and point to your empty glass. But now we want to capture this trick of your brain with actual data. Speech can be represented as sound waves with a certain frequency.

And although every person has a unique voice, their sound waves are quite similar. If we collect enough of these sound samples and the words or sentences they represent, we can predict what someone says by checking which sound pattern is most similar to the pattern this person produces. Even if there are words missing from a sentence, which results in missing data, the algorithm is still able to find the most probable message. This technique can be used to learn robots or voice assistants to respond to human commands, which is not only cool, but also valuable.

Conclusions

These examples show that you use data science every day. You have access to the most powerful algorithms, hidden inside you. Data science is about replicating the logic which is already present in the human brain. So the truth is – we don’t use data science to create complicated models. We use data science to resemble reality. How can your business benefit from that?

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