Data literacy, as the name suggests, is the ability to read and interpret data. It’s a skill that can be applied across many fields and industries.
Data science is an interdisciplinary field whose core principles are data collection and analysis. Data scientists are often tasked with collecting and analyzing large data sets for business or research. Their analysis can range from simple to complex.
You, like everyone else, are biased. Your brain is a human being’s most powerful tool, but it can be tricked into making poor choices. It’s important to acknowledge that you have biases—and then try to correct them.
One of the most common ways people become biased is through repetition: if you’ve been told something before, you’re more likely to believe it than if someone tells you something for the first time. You are also more likely to believe something if it comes in a format that fits your preferences—for example, someone tells you he loves data visualization because he likes visualizations and graphs.
Finally, when we hear information from people we trust (like family members or coworkers), our brains tend to accept what they say as true without question; this makes us vulnerable to misinformation from trusted sources such as politicians or news outlets.
When evaluating a source, it’s essential to understand the origin and how it collected the data. You should also be familiar with how the data was processed. This includes:
We remember the first and last things we see, hear and read. This is known as the primacy effect (the first step) and the recency effect (the last thing). Our ability to recall things in this order is called the serial position effect.
For example: If you’re a salesperson who usually has 10 customers per day, you might think that 9 out of 10 customers are satisfied with their services — but that’s not always true! The customer satisfaction survey may be limited by its design or how it was conducted, so we need another way to understand who our most valuable customers truly are.
Visualization tools can be used in several ways, so it pays to have a clear idea of what you want to achieve before you start making your own. It’s also worth noting that while some people are naturally gifted at creating data visualizations, most of us need a little practice.
A good starting point is learning the basics of Tableau and Excel (or PowerBI) since these are popular and easy-to-use programs with enough functions for most users. But even if you’re not interested in becoming a specialist user of these programs, many aspects will help improve your skills overall.
In addition, there’s no reason “basic” shouldn’t mean beautiful—and there are some great resources for beginners who want something more than fundamental!
It would be best if you focused on telling stories with your visualizations; this is probably the most important skill for any data scientist or visualization designer because it ties back into our discussion about humans being storytelling animals above all else.
As part of this process, it helps to understand how humans react differently depending on how much information is presented visually rather than verbally.
Visualizations are used to simplify the complex and show relationships between concepts, trends in data, and outliers.
For example, show how several factors are related (e.g., if you have several variables that affect sales). Or you could show how one or two particular dimensions affect another dimension (e.g. how product quality affects customer satisfaction). You might also be interested in showing trends over time (e.g. what is happening with your sales each month?) or highlighting anomalies such as extreme values (e.g. which customers are most likely to buy expensive items?)
Transparency means the data is clear and easy to understand. A good visualization provides an immediate understanding of the meaning behind the numbers. It should not require extra effort from the reader; instead, it should be intuitively obvious what’s happening at a glance.
A good example of transparency is a bar chart showing sales revenue over time. When you look at this chart quickly, you understand that there were two periods where revenues increased sharply (and then decreased slightly). Still, overall revenues were steady throughout this period of time. This type of simple “story” tells more than just a straight-up table with rows of numbers would do.
When presented with just the raw numbers from this same data set (i.e., without any visual aids), many people won’t automatically know what they mean!
Insightful means we learn something new by looking at our data through different lenses/metaphors (e.g., bar charts vs. line graphs). Data visualization doesn’t have its language yet as math or music does; therefore, we need multiple ways to encode thoughts into images so others can interpret them as well!
Using various types of visualizations helps us uncover connections between concepts. Hence, they make sense rather than being separate entities existing only within one domain while unrelated to everything else around them.
Summing Up
To become data literate, you must learn how to interpret visualizations. The best way to do this is by taking advantage of online resources. If you want an overview of the basics, check out an infographic on reading a chart or graph. You can also find additional resources through blog posts on data literacy and how it affects your business.
