Data science requires a range of sophisticated technical skills. Don’t let that expertise get in the way of critical thinking, though, or you could end up doing more harm than good for your business partners.

As Alexander Pope said, to err is human. By that metric, who is more human than us data scientists? We devise wrong hypotheses constantly and then spend time working on them just to find out how wrong we were.

When looking at mistakes from an experiment, a data scientist needs to be critical, always on the lookout for something that others may have missed. But sometimes, in our day-to-day routine, we can easily get lost in little details. When this happens, we often fail to look at the overall picture, ultimately failing to deliver what the business wants.

Our business partners have hired us to generate value. We won’t be able to generate that value unless we develop business-oriented critical thinking, including having a more holistic perspective of the business at hand. So here is some practical advice for your day-to-day work as a data scientist. These recommendations will help you to be more diligent and more impactful at the same time.


Tell me how many times this has happened to you: You get a data set and start working on it straight away. You create neat visualizations and start building models. Maybe you even present automatically generated descriptive analytics to your business counterparts!

But do you ever ask, “Does this data actually make sense?” Incorrectly assuming that the data is clean could lead you toward very wrong hypotheses. Not only that, but you’re also missing an important analytical opportunity with this assumption.

You can actually discern a lot of important patterns by looking at discrepancies in the data. For example, if you notice that a particular column has more than 50 percent of values missing, you might think about dropping the column. But what if the missing column is because the data collection instrument has some error? By calling attention to this, you could have helped the business to improve its processes.

Or what if you’re given a distribution of customers that shows a ratio of 90 percent men versus 10 percent women, but the business is a cosmetics company that predominantly markets its products to women? You could assume you have clean data and show the results as is, or you can use common sense and ask the business partner if the labels are switched.

Such errors are widespread. Catching them not only helps the future data collection processes, but also prevents the company from making wrong decisions by preventing various other teams from using bad data.


You probably know fab.com. If you don’t, it’s a website that sells selected health and fitness items. But the site’s origins weren’t in e-commerce. Fab.com started as Fabulis.com, a social networking site for gay men. One of the site’s most popular features was called the “Gay Deal of the Day.”

One day, the deal was for hamburgers. Half of the deal’s buyers were women, despite the fact that they weren’t the site’s target users. This fact caused the data team to realize that they had an untapped market for selling goods to women. So Fabulis.com changed its business model to serve this newfound market.

Be on the lookout for something out of the ordinary. Be ready to ask questions. If you see something in the data, you may have hit gold. Data can help a business to optimize revenue, but sometimes it has the power to change the direction of the company as well.

Another famous example of this is Flickr, which started out as a multiplayer game. Only when the founders noticed that people were using it as a photo upload service did the company pivot to the photo sharing app we know it as today.

Try to see patterns that others would miss. Do you see a discrepancy in some buying patterns or maybe something you can’t seem to explain? That might be an opportunity in disguise when you look through a wider lens.


What do we want to optimize for? Most businesses fail to answer this simple question.