In college, I met a group of guys that loved to meet on a field, carry an odd-shaped ball around, and run into each other with no equipment. They called this high-intensity game rugby.
One day, they asked me if I was interested in playing. It looked simple enough and fun, so I said sure. Once we started playing, I quickly realized that while I had the physical tools needed to be proficient in this game, I didn’t understand the rules or language of rugby.
I had no idea what a lineout was. I didn’t understand why hands couldn’t be used in a ruck. Every time they yelled “SCRUM!” it took me a few seconds to realize what was coming next.
I was rugby illiterate, and it was holding me back.
It’s easy to have a similar feeling of confusion and bewilderment come over you when it comes to data and analytics. The goal of this piece is to arm you with the basic terminology needed to be data literate and unleash your analytical potential.
WHAT IS DATA LITERACY?
Data literacy means you know what data you’re tracking, why you’re tracking it, how to interpret it, and how to use it to develop new capabilities, grow sales, reduce costs, and/or do your job better/more efficiently.
Data literate people have the following skills:
- Think critically about the data analysis process and the information and insights it delivers;
- Understand various data analytic methods and tools and know how to use them;
- Know what data is appropriate to use in specific situations;
- Understand the data being looked at;
- Recognize when data is being misrepresented;
- Create and interpret basic data visualizations (charts, graphs, maps, etc.); and
- Communicate information and insights to a broader audience (executives, managers, peers, external parties, etc.). This process is also called data storytelling.
In today’s tech-centric, data-driven world, it is easy to see why being competent and comfortable with data is an essential skill.
IT ALL STARTS WITH A CORNERSTONE
Understanding the language of data is an essential foundational pillar for anyone looking to develop data literacy.
This overview is intended for what I call “casual data consumers.” These are professionals who don’t deal with advanced data and analytics projects on a regular basis. We are looking to establish a baseline level of knowledge and confidence to become more data fluent.
You don’t need to know advanced statistical, data engineering, or modeling techniques to make this happen. Consider the words of Brent Dykes, author of “Web Analytics Action Hero: Using Analysis to Gain Insight and Optimize Your Business” — “Just like people don’t need an advanced English degree to be literate, your employees don’t need advanced statistical knowledge and programming skills in Python or R to be data literate. Reading and writing skill levels are often defined by what people can or can’t accomplish in their everyday life — we must do the same for data literacy.”
DO YOU NEED TO BE DATA LITERATE?
Yes, you do. You don’t need to know the intricate minutiae, but you need to know the basics.