Although your schooling will largely prepare you for your career as a data analyst, nothing can take the place of real-world experience. Some things have to be learned through trial and error, but others are well-documented enough that they can be avoided easily enough. Familiarize yourself with the most common mistakes made by data analysts to ensure your career gets off to a fantastic start.
1. Avoid Melodrama
During presentations, a data analyst is expected to help make sense of the information being presented. However, you should try to let it speak for itself as much as possible. When analysts editorialize too much, they often plant incorrect ideas in employers’ heads. Just because something looks pretty significant doesn’t mean it should be blown up into a huge deal. Look for ways to present the data from various angles to provide the biggest picture possible, and avoid making big, dramatic statements about anything you present.
2. Calibrate Time Series Properly
All too often, data analysts give little thought to the time series they use, and their data is misconstrued or rendered utterly meaningless. For example, using a daily time series when trying to depict trends across periods of months or years doesn’t make sense. Important trends and other information may go undetected when an inappropriate time series is used. When the best one is used, however, it’s easier to find useful questions to explore. Your employer can then elect to dig deeper to unearth information that they can use to improve the business.
3. Be Succinct
Avoid overloading your audience with excessive amounts of data. As a data analyst, it’s natural to get excited about the data you uncover. You may feel like every last piece of information is crucial to your presentation. However, if you go too far, your audience will lose interest, and any decent points you’re making will be lost to them. When putting a presentation together, make it as simple and effective as possible. Aim to get your points across quickly and easily. Organize everything in an easy-to-digest manner that is actually useful to your audience.
4. Don’t Be Too Different
This problem is especially common among new data analysts, who often feel compelled to make themselves stand out from the crowd by employing offbeat techniques. Now is not the time to be cute or clever. Your employer wants you to find and use data in a way that helps them improve their bottom line. Switching around the way things are normally done only leads to confusion. For example, be consistent about colors. Red is universally understood to be negative or bad, so don’t use it to convey positive data.
5. Don’t Be Too Simple
You were just admonished not to overwhelm your audience with too much data, but the opposite phenomenon can be equally bad. Make sure you have enough data to justify whatever point you’re trying to convey. Just because scant amounts of data suggest a trend doesn’t mean it actually exists. Even people who are completely unversed in reading and understanding charts understand that small amounts of data don’t mean a whole lot. You may be excited about what you’ve uncovered, but hold off until you have a lot more data to back it up. Otherwise, you may inadvertently lead your employer astray, and that can be disastrous for your career.
Data analysts are numbers people. Some are naturals at presenting their findings, and others struggle a little. Either way, by avoiding the mistakes above, you will be well ahead of the curve when compared with other new analysts.