You’re a hotshot manager. You love your dashboards and you keep your finger on the beating pulse of the business. You take pride in using data to drive your decisions rather than shooting from the hip like one of those old-school 1950s bosses. This is the 21st century, and data is king. You even hired a sexy statistician or data scientist, though you don’t really understand what they do. Never mind, you can proudly tell all your friends that you are leading a modern data-driven team. Nothing can go wrong, right? Incorrect. If you don’t pay attention, data can drive you off a cliff. This article discusses seven of the ways this can happen. Read on to ensure it doesn’t happen to you.

## 1. Pretending uncertainty doesn’t exist#

Last month, your favourite metric was 5.2%. This month, it’s 5.5%. Looks like things are getting better – you must be doing something right! But is 5.5% really different from 5.2%? All things being equal, you should expect some variability in most of your metrics. The values you see are drawn from a distribution of possible values, which means you can’t be certain what value you’ll be seeing next. Fortunately, with more data you would be able to quantify this uncertainty and know which values are more likely. Don’t fear or ignore uncertainty. Embrace and study it, and you’ll be on the right track.

## 2. Confusing observed and unobserved quantities#

Everyone agrees that the future is uncertain. We can generate forecasts with varying degrees of confidence, but we never know for sure what’s going to happen. However, some people tend to ignore uncertainty in forecasts, treating the unobserved future values as comparable to observed present values. For example, marketers often compare customer lifetime value with the cost of acquiring a customer. The problem is that customer lifetime value relies on a prediction of the net profit from a customer (so it’s largely unobserved and uncertain), while the business has much more control and certainty around the cost of acquiring a customer (though it’s not completely known). Treating the two values as if they’re observed and known is risky, as it can lead to major financial losses.

## 3. Thinking that your data is correct#

Ask anyone who works with data, and they’ll tell you that it’s always messy. A well-known saying among data scientists is that 80% of the work is data cleaning and the other 20% is complaining about data cleaning. Hence, it’s likely that at least some of the figures you’re relying on to make decisions are somewhat inaccurate. However, it’s important to remember that this doesn’t make the data completely useless. But if something looks too good to be true, it probably isn’t true. Finally, it’s highly unlikely that the data is always correct when you like the results and always incorrect when the results aren’t favourable, so don’t use the “guy on the internet said our data isn’t 100% correct” excuse to push back on inconvenient truths.

## 4. Believing that your data is complete#

No matter how big you are, your data doesn’t capture everything your customers do. Even Google and the NSA don’t have a full view of what people are up to in the non-digital world, and they can’t completely read our minds (yet). Most businesses have much less data than the big tech companies, and they look a bit silly trying to explain customer behaviour using only the data they have. At the end of the day, you have to work with the data you can access, but never underestimate the effectiveness of obtaining more (relevant) data.

## 5. Measuring the wrong thing#

Maybe you recently read an article emphasising the importance of real metrics, like daily active users, as opposed to vanity metrics like number of signups to your service. You therefore decide to track the daily active users of your product. But have you thought about whether this metric is relevant to what you’re trying to achieve? If you run a business like Airbnb, where transactions are inherently infrequent, do you really care if people don’t regularly log in? You probably don’t, as long as they use the product when they actually need it. Measuring and trying to optimise the wrong thing can be very risky. Indeed, deciding on metrics and their measurement can be seen as the hardest parts of data science.

## 6. Not recognising your unconscious incompetence#

To quote Bertrand Russell: “One of the painful things about our time is that those who feel certainty are stupid, and those with any imagination and understanding are filled with doubt and indecision.” Not recognising the extent of your ignorance when it comes to data is pretty common among those with no training in the field, which may lead to illusory superiority. This may be exacerbated by the fact that those who do know what they’re doing tend to talk a lot about uncertainty and how there are many things that are simply unknowable. My hope is that this short article would help people graduate from unconscious incompetence, where you don’t even recognise the importance of what you don’t know, to conscious incompetence, where you recognise the need to learn and rely on expert advice.