Have you ever been in a meeting where someone presented the results of a data analysis that you didn’t completely understand, but you didn’t ask questions because everyone else in the meeting seemed to get it? If your answer is yes, you might consider spending some time improving your data literacy.
Data literacy is the ability to derive meaningful information from data. And while it does require some basic knowledge of mathematics and statistics, the good news is there are a lot of great tools and resources to help you. Understanding visualizations, the vocabulary and the language of statistical analyses are important if you are using data to drive decisions.
It could be as simple as understanding variation or as complex as utilizing a decision tree to drive actionable insights. And of course, that is assuming that the analyses are based on accurate data. Data literacy is not about knowing how to analyze data. It is about understanding the analyses that your colleagues or outside agencies present to you.
Organizations understand that by effectively integrating technology and analytics into their business processes, they can become more profitable, more efficient and deliver better customer experiences.
Gartner estimates that by 2023, “data literacy will become an explicit and necessary driver of business value." In fact, they believe that data literacy will be formally included in over 80% of data and analytics strategies and change management programs. For those of you with backgrounds in Lean or Six Sigma, you have probably been educating people in data literacy for years.
Leaders know that to remain competitive they must better utilize their data, and many have plans to develop a culture of analytics. With technology finding its way into all areas of business and knowing that more data is available than ever before, the need to understand basic data analysis concepts has become a necessary skill in most of our jobs.
Boosting your data literacy just might be the best investment you could make in yourself right now. How many of the questions below can you answer right now?
Think about it a moment, get an answer in your head or jot it down somewhere, and then click or tap on the question to see if you're right!
Quantitative refers to numbers and things you can measure objectively (think width, height, temperature or volume). Qualitative refers to characteristics that can't be easily measured and are observed subjectively (e.g. smells, tastes, textures, or color).
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When you hear "average," it is often referring to the mean — what you get if you add every number together, then divide the total by how many numbers you added. Median, on the other hand, is the middle number.
In many studies, including Lean Six Sigma projects, you might be confronted with data where the mean is not necessarily the best reflection of the average though. Think about five household incomes. The first is $140,000. The second $200,000. The third $215,000. The fourth $220,000. And the fifth $1,725,000. The mean of those numbers is $500,000.
But when we say "average," we are looking for a number that best characterizes that particular sample. $500,000 is way higher than all but one number though, and the median, $215,000 serves as a better "average."
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A scatterplot of popsicle sales and skateboard accidents may form a straight line and give you a correlation coefficient of 0.9999. Buying popsicles clearly doesn't cause skateboard accidents, though. More people ride skateboards and more people buy popsicles in hot weather, which is the reason these two factors are correlated. Only properly controlled experiments help you determine whether a relationship is causal.
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In an observational study, nothing is done to the things or people being studied. We are merely observing them as they are. In a controlled experiment, they are assigned to groups. Each group (except the control group) receives some treatment or is changed somehow (e.g. products manufactured with an alternate step in the process, people asked to abstain from drinking caffeine, etc.) and then we study how that change affects them.
Not done testing yourself?! The Khan Academy has some more background and scenarios to test yourself if you want to double-check whether to give yourself credit for this answer.
And you have already made it here to The Minitab Blog, so you have taken your first step. Keep exploring, and please contact us if you have any questions or want to learn more about solutions analytics and data-driven decision-making!
And we could train you to be lethal. Well, at problem-solving with statistical analysis at least. We encourage you to check out our upcoming and on demand webinars. Or if you are interested in investing more in your analysis skills, sign up for guided training with experts in Minitab and statistical analysis. View the Calendar
Or do you want to? Check out our openings!
Or if you already have your own thing, we get it 👍 We would still encourage you to check out our webinars and some of our more recent content on Machine Learning. If you are a statistician or data scientist, you know that even if you can answer all these questions, there is always a new method or approach to learn!
Creating a silo of data scientists won't light a path to operational excellence or business success. While there are dedicated data analysts who help people with their toughest data problems, everyone benefits when they have an appreciation for good data analysis.
Data literacy across the enterprise guarantees that data is incorporated into day-to-day decision-making. It results in better questions, deeper understanding and defensible conclusions. Improving your data analysis skills is also a great addition to your personal development plan. When we make data-driven decisions, we remove the bias and opinions that we unknowingly bring to discussions.
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