The Most Common Data Analysis Mistakes

Data analysis is the trendiest career skill to possess, and for good reason: it's valuable. Not only can keen analytical skills skyrocket your earning potential, it can also increase your company's earnings as well. No wonder it's such a trend.

But there is more to data analysis than meets the eye. It's definitely a "science," but most people underestimate the needed "art" required to be a successful data analyst. Simply executing a set of memorized instructions when it comes to data analysis is just not going to cut it. This is the most common data analysis mistake--taking the data at face value.

This mistake is pervasive and often the deciding factor when ranking one's skill within the field of data analysis. You should never take the data as-is.

As Jason Kulpa, CEO of tech company UE.co states, “In order to become an expert data analyst and avoid this common mistake, you need to develop a critical eye. Do you question your data? Do you sort, clean, and filter your data? There is an art to asking whether the data set makes sense as a whole or whether it should be split into subsets. And, above all, does your data appear to be valid and the best representation of what you hope to analyze?”

The point of data analysis is to gain useful information, spot trends and anomalies, predict, and model. If you begin your analysis with sub-par data, you can never gain results beyond that level. It's a guarantee that if you take your data as it is, at face value, without scrutinizing it first, you will gain little insight.

Data analysis is a booming field, but it's still quite new and filled with inexperienced people. You can quickly make your way to the top if you avoid this common mistake that most novice analysts make. Always question your data and start your analysis with the best possible set you can acquire in order to achieve the best possible results.