Data analysis is becoming less of a niche skill and more of a common requirement for jobs and roles of all shapes and sizes. Over the past 20 years, data has gone from relatively scarce to so abundant we aren’t sure what to do with it.
Gathering and analyzing data is a now part of most jobs within most organizations, either to better understand your role, to measure your results or to guide you in what to do next.
Unfortunately, the accessibility and ubiquity of data has led to an increased number of amateur mistakes made in analyzing it—so if you want to improve your own analytic abilities and guard against these mistakes, you need to understand them.
These are some of the most common mistakes made by newcomers and non-experts in the data analytics world:
1. Allowing bad data to compromise your conclusions
If your data is bad, even the best data analyst in the world can’t save it from leading to bad conclusions. Bad data comes in many varieties, such as unwittingly duplicated records, illegitimate sources, and old data that haven’t been sufficiently updated. If you want to make accurate conclusions, you have to start by studying the data at the foundation of those conclusions.
2. Blindly trusting data visualizations
Data visualization is reshaping the industry, putting powerful and intuitive pattern-recognition tools in the hands of everyday users. At a glance, you can get a feel for how a trend is manifesting or get a quick answer to your question—but blindly trusting these data visualizations can blind you to variables that are getting skipped over, and skew your perceptions.
3. Studying limited variables
If you only focus on one example or one variable, your conclusions will be off the mark; for example, do you think studying a single email you sent would lead to an accurate conclusion of your communication habit? If you judge everything based on one or two key metrics, you’ll prevent yourself from seeing the big picture.
4. Incorporating all variables
Conversely, it’s a bad idea to try and look at every variable or metric you collect—especially in today’s era of big data. Taking this broad approach can make you focus on the noise, instead of the signal, and prevent you from seeing the most important patterns in your data set.
5. Falling victim to confirmation bias
Confirmation bias is one of the most prominent cognitive biases in data analytics, and one of the hardest to compensate for. Essentially, the idea is that if you have a preconceived idea of what your conclusions will be, you’ll disproportionately favor evidence that supports those conclusions. Guard against this by specifically challenging your assumptions and prioritizing objective evidence.
6. Neglecting outliers
Outliers are pieces of data that don’t fit with the rest of your set. It’s easy to write these off as an insignificant blip on the radar—such as a surveyed customer who didn’t take the survey seriously or a flaw in your data recording. However, this can be a crucial mistake; outliers often lead to important conclusions you’d miss by just studying the averages.
7. Prioritizing outliers
On the other hand, if you zoom in to your outliers too far, you could favor an individual over the group, skewing your conclusions the other way. You need to retain a balanced approach if you want to truly understand what’s going on.
8. Letting self-serving bias blind you
Self-serving bias is our natural tendency to credit ourselves with our successes, and blame our failures on external variables beyond our control. If you apply this faulty reasoning to data analysis, you may mistakenly credit your company or team for “good” things, and disproportionately label the “bad” things as being random hiccups or someone else’s fault.
9. Staying isolated
You aren’t the only person analyzing and studying data. One of the worst mistakes you can make is to stay isolated and not learn from those around you. Keep reading data analysis news and stay up on the latest trends, and challenge yourself to learn more about data science every day.
Talk to other people about your methodologies, and be willing to hear feedback about your habits. The more you expose yourself, the more experience you’ll get by proxy, and the broader your perspectives on data will be.
You can’t read a list like this and become a better data analyst overnight. Nor do you likely have the time to go back to school and study statistics until you’ve rebuilt your skills from the ground up. Instead, you have to treat your data analytics skills as an ongoing process, identifying what you’re doing wrong as you’re doing it, and gradually making changes to your approach that lead you to better, more sound practices.
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