Data visualization is an increasingly popular method used by data analysts and curious amateurs alike to study various topics and make better business decisions. And because it’s being applied to so many areas, from a general business dashboard to help you gauge your enterprise’s performance at a high level to data-generated maps designed to illustrate demographic trends, almost any entrepreneur can make use of it.
However, while there are some serious advantages to visualizing your data, there are also some drawbacks you’ll have to consider before you take the plunge.
Why Visualize Data?
Why is data visualization so important in the first place?
Convenience. Having a platform to automatically turn your gigabytes of data into a streamlined, easy-to-understand graph spares you the hours of effort it would have taken to do this by hand—even with assisting technology. Most modern data visuals can be processed and displayed in a matter of seconds, even as you change variables and request different types of displays. It also prevents the need to comb through all that data on your own.
Interpretation speed. It shouldn’t surprise you that humans process visual data faster than we process written or other forms of data. For example, how long would it take you to react to a basketball you can see flying at your face, and how long would it take you to read about an incoming basketball, understand that it’s happening to you, and then take the appropriate dodging action? Processing business data doesn’t require the same reflexes, but looking at a chart will help you understand a problem faster than reviewing the data line by line.
Memory. Visual data is also easier to remember than written or spoken data; it’s just the way our brains are wired. Accordingly, looking at a graph of information will help you retain the high-level takeaways of that information without memorizing all the tiny data points that comprise it.
Customizability. Most modern data visualization platforms are built on a foundation of customizability. You can easily change which variables are present in each of your charts, tweak the parameters of the chart, and change aesthetic qualities like colors, transparency, or style with just a few clicks. This helps you produce visuals that are more easily interpretable, or ones that suit your personal preferences.
Presentation. Finally, remember that data visuals aren’t just about your interpretation—they’re also about the interpretations of others. Data visuals make it easier to communicate concepts to your clients, your partners, and even your employees, ultimately making your messages clearer and saving you time in the process.
Unfortunately, it’s not all good news—there are some serious pitfalls that could interfere with the effectiveness of your data visuals:
Overconfidence. It’s easy to get overconfident when reviewing data in chart or graph form. When a visual layout makes it obvious that factor X Is responsible for cause Y, you may instantly ignore all other variables that might be playing a role—especially if the problem is more complicated than you realize. You have to treat data visuals as one of many tools to help you make decisions, and not the final solution to whatever problem you’re facing.
Outlier negligence. Graphs are amazing at giving you the high-level concept of what’s going on, but they tend to mask outliers. Remember, outliers (extreme, infrequent data points) are oftentimes just as important in solving a statistical problem as the high-volume averages are. Don’t let your visuals blind you to them.
Inherent user bias. Because you have direct control over how the graphs and charts are produced, your inherent biases could affect the types of reports you eventually see. For example, if you start with a bad assumption or forgone conclusion, you may unintentionally manipulate the data to show you what you want to see. Remaining objective is harder than ever, so you have to be careful and consistent in how you generate reports.
Bad data. The best visual in the world won’t help you if the quality of your data is off. Improperly gathered, categorized, or aggregated data will make even the prettiest visual inaccurate, and could lead you astray. Be sure to double-check your data gathering and selection methods before getting too far on any one path.
Abstract problems. Finally, data visuals are good at helping you see high-level trends and big-picture issues, but they’re not great at helping you fix ground-level problems. Keep this in mind when making decisions.
Still, if you understand these drawbacks, and have a plan to compensate for them, there’s no reason you can’t reap the benefits of this exciting new technology. Start experimenting with different platforms that offer interactive charts and graphs to help you visualize your raw data, and incorporate those visual insights into your decision making. As long as you do this responsibly, you’ll be surprised at how much your organization can transform.