What It Means to Be Data Literate



The Data Journalists handbook defines data literacy as, “the ability to consume for knowledge, produce coherently and think critically about data.” It goes on to say that “data literacy includes statistical literacy but also understanding how to work with large data sets, how they were produced, how to connect various data sets and how to interpret them.”

At General Assembly, we’d like to imagine a world where you don’t need a Ph.D. in Statistics to have a data-informed conversation about your business, your health, or your life in general. Over the past year, we’ve embarked on the journey to build a more data literate world through education offerings that meet the diverse needs of our students.

In building these courses, we’ve sought advice from data scientists, analysts, and hiring managers to determine the critical skills you need to become data literate in today’s workforce. We discovered that it isn’t just a concrete list of skills, but a mindset geared towards data—a way of approaching problems beyond “gut instincts.” 

Here, we’ve proposed a few simple questions that will help you start to view the world through the lens of data.

1. What insights are you searching for?

Raw data can be intimidating. Before you dive into any set of data, make sure you understand what kinds of insights you’re looking to derive from the data. Perhaps you want to know how much traffic to your website is growing month over month? Or maybe you are searching for an apartment and want to understand how your current rent compares to the market average. By first thinking critically about the right questions to ask, data will seem less intimidating and even fun to dig into.

2. What’s the source of the data?

A huge part of understanding how to read and interpret data is knowing when it’s misleading or flat out phony. To do this, you should approach any conclusion that claims to be drawn from data analysis with an air of skepticism—always ask, “How and where was the data collected?” If a number seems extraordinary, don’t accept it at face value. Investigating the source will help you determine whether or not the data is biased or misleading. 

3. What’s the context?

Data on its own is meaningless. It’s the insights derived from that data that gives it value and empowers informed decision making. Data analytics, or the discovery and communication of meaningful patterns in data, enable you to confidently take action or make a decision by bringing context to data. As a business, you can use analytics to track performance and growing trends across your website, consumer behavior, or advertising and marketing campaigns.

Take the following table. At a 25% conversion rate, you might think Pinterest is a boon for your business:

Screenshot 2016-02-18 23.59.32

But by comparing the conversation rate against the raw data…you’ll see a different story.

Screenshot 2016-02-18 23.59.38

So, how do you add context to make your data more meaningful?

A few simple tactics will get you started in the right direction:

  1. Use raw numbers in combination with rates & averages (like we did above).
  2. Compare against industry averages.
  3. Compare against time (% vs. sequential, %vs. last year, % vs. average).
  4. Ask yourself if the results are statistically significant (How actionable are your results? How confident are you that you will continue to see different long-term response rates?)

As data is becoming an increasing part of our lives, it should be a way of thinking about the world—and making it better. By asking the right questions, knowing how and where your data was collected, and giving context to your data, you will be well on your way to using data to make better decisions at work and in life.  

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