The 7 Most Common Mistakes in Data Analytics

Data analytics is an increasingly popular field for modern businesses. It can be applied to nearly any department, and if used properly, can help you better understand your business while simultaneously finding ways to improve efficiency. Given a big enough data set, you can learn more about your customers’ preferences, how you’re spending money, and even how your employees are working. It’s essentially a gateway to everything you’ve ever wanted to know.

However, these benefits are all contingent upon one major caveat: that you’re analyzing the data properly. Despite its prevalence, most companies are mishandling their data analytics strategy, in one way or another.

Common Data Analytics Mistakes

These are some of the most common mistakes to watch out for when incorporating data analytics into your company:

1. Failing to clean your data

If you’re like most businesses, you’re collecting data from multiple sources. You’re relying on many different people contributing information, and your systems are far from perfect. Accordingly, if you want to form reliable conclusions, you’ll need some way to clean your data. That means weeding out duplicate entries, reformatting improperly formatted entries, and getting rid of data points that are no longer relevant. This is an intensive process, but a necessary one if you want to have faith in your conclusions.

2. Ignoring outliers

It’s tempting to boil all your data down to one or two high-level insights, and thanks to the plethora of number-crunching tools we have available to us, it’s easy to do it. For example, you might look at the average behavioral patterns across all your demographics or evaluate high-level trends in your purchasing department. However, merely looking at averages and high-level trends will blind you to the presence of outliers—data points that fall far outside what’s “normal” for a given category. Sometimes, outliers can help you learn something valuable about your data set that you couldn’t learn with a surface-level analysis.

3. Ignoring context

Sometimes, your data will only make sense when you interpret it within a certain context. Neglecting that context could lead you to a false assumption, or a bad takeaway. For example, if you notice a slowdown in sales, you might start to blame the sales team for sluggish results. However, if the slowdown occurs in line with an increase in prices, or in line with a seasonal shift in historical buying patterns, it may make perfect sense.

4. Comparing apples and oranges

Under ideal conditions, you’ll be able to run experiments and compare different situations by changing a single variable. This way, you can tie any changes you notice to that variable; for example, you might notice an increase in consumer spending with good weather. This is comparing apples to apples. If you compare apples to oranges, by comparing data sets with too many different variables, your conclusions become far less reliable.

5. Measuring the wrong things

What are the key performance indicators (KPIs) for the subject you’re investigating? If you’re looking at the wrong metrics, you won’t be able to form any reasonable conclusions about your subject matter. For example, if you’re trying to determine whether your marketing campaign is effective, but you’re only looking at a metric like time spent on-page, you won’t get the full picture of the situation—nor will your conclusions be reliable.

6. Relying too much on visuals

Data visuals like graphs and charts are becoming more popular since they simplify what could otherwise be very complex and difficult-to-interpret data sets. They’re especially helpful in communicating complex ideas to people who may otherwise struggle to comprehend them. However, over-relying on these visuals can cause you to overlook important factors that can get lost in such a high-level analysis.

7. Not making your insights actionable

You may form an accurate conclusion with your data analytics strategy, but that conclusion won’t mean much if it isn’t actionable. The purpose of data analytics is to gather insights that can help you change or improve some aspect of your business. If you’re not tying your insights to actions, you’re not getting any value. Always make sure you’re making your insights actionable.

Making Everyone on the Team a Data Analyst

Data is constantly getting easier to collect, and easier to analyze thanks to the amazing tools available to business owners. Every department and every role on the team is starting to have a vested interest in gathering and crunching data. Accordingly, it’s on businesses to spend more time educating and training the team on how to analyze data properly; in other words, every person on your team should be a data analyst in some way. Pay close attention to the mistakes you make along the way (since it’s impossible to avoid all mistakes), and commit to ongoing learning to keep improving your approach to data analytics.

Featured image source: Freepik