Data literacy is essential to navigating all things technical
DeepSeek, Chat GPT, LLMs, hallucinations, machine learning, AI, chat bots, social media tracking, TikTok data mining, Facebook’s targeted ads, phones listening to us … Talk about data permeates every bit of our lives, so it is understandable that we might not understand the particulars of how all of those technologies work – let alone the intersection of them.
Data. Data is where they all intersect, and it’s the fuel that all apps run on.
You don’t have to be a data scientist to understand the basics about data, but data literacy is a must have for the future. Even the Army is talking about it, and, apologies to my government peeps, the federal government isn’t known for being cutting edge.
Understanding data fundamentals is essential to making good business decisions, so let’s explore what data literacy is.
Data literacy is more than pie charts and graphs
I used to teach people to read. In my first training session to be a literacy volunteer, I spotted one of my brother’s ex-girlfriends in a group class led by my trainer. We graduated from the same high school at the same time – yet 5 years later, we were on opposite sides of a very wide divide.
Merriam-Webster defines being literate as being able to read and write.
What I learned from teaching literacy is that it’s about more than the ability to read – it’s about the ability to understand and make decisions based on what you read. After all, no one learns to read for the love of knowing a word.
My last client was a young man imprisoned for selling drugs. He had a baby on the way, and he desperately wanted to be a father to his daughter. He knew how to read and write, but he had trouble putting it all together for comprehension. We spent most of our time together writing a letter to the judge, pleading for leniency in his sentencing. He was not successful, and we stopped working together when he was transferred to a larger prison. I think of him – and his daughter – often.
So the first time I encountered the phrase health literacy, I immediately got that it was about hearing and understanding what you need to know to make decisions for yourself. If you do not understand the implications of the information they give you, your ability to make those decisions may be given to someone else. For example, when a loved one of mine had a head injury, medical staff wouldn’t let them consent to procedures, even though they were talking cogently – I had to do it. This is how important it is to know enough to be able to make sound decisions, not just enough to understand the words in a sentence.
The same is true of data literacy, which the Data Literacy Project says will be the most critical workforce skill by 2030.
Not knowing puts you on the wrong side of the literacy divide, like the one my brother’s girlfriend and I found ourselves staring across.
Data literacy includes spotting quality data
So what is data literacy, really? Wikipedia defines it as “… the ability to read, understand, create, and communicate data as information.” Some definitions, like this one from Tableau, are simple, but light on details. Gartner’s definition is straightforward and emphasizes how important context is to data literacy – much like understanding details about health issues is critical to making health decisions. Unesco’s attempt references communication and understanding. Some definitions emphasize that data literacy involves evaluating data to make data-driven decisions.
All of these definitions are correct, and Tableau and Gartner add a lot of detail that help you understand how to apply data literacy and the skills you need to do so.
But these definitions lack an acknowledgement of how important data quality is to comprehension. You must have good data to base decisions on, and you must be able to identify good and bad data. As IBM puts it, “Data quality measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness and fitness for purpose.”
Since I think understanding data quality is essential to data literacy, I define data literacy as:
The ability to: recognize good (and bad) data, understand how it relates to other data, connect how data relates to your specific field or domain, and be able to interpret data in a way that lets you understand something that you need to know.
As we’ve already discussed, literacy is about the ability to read/hear, understand and make decisions based on the information you have. But I think the larger answer to the question of what data literacy is lies on a continuum and changes a bit with roles – think of the difference of being able to read and write well enough to get an A on a college essay and writing a novel. There’s competency at one end, and there’s fluency at the other.
C-suite members should be able to parse information from their experts and make decisions based on it. I’d equate this with being able to write a solid college essay. The folks providing the data with which to make decisions likely are technologists and should be fluent in data.
All points on the spectrum should understand why data is important, the general principles around what data can do, what makes good data, and how it drives productivity and functionality.
Here’s a handy comparison chart.
Chart compares word literacy with data literacy.
Why data literacy is important
Data is important to us only because there is something that it tells us that we need to know. To paraphrase Professor Richard W. Hamming’s famous quote, “The purpose of [data] is insight, not numbers.” Data literacy moves us beyond Excel sheets and PowerPoint graphs to learning something meaningful.
I tend to work with clients that are high up in their organizations because studies show that the C-suite has to drive, or at least approve, change. Yet, I have been assigned projects to analyze and present on data findings that were in no way meaningful. Worse, they were misleading. Don’t get me wrong, you have to analyze data, but the act of gathering and analyzing data does not mean that it will yield meaningful results.
This can be for a number of reasons, most commonly because we have bad or inconsistent data, we gathered the wrong data, or we’re asking the wrong question for our intended purpose. For the C-suite in particular, understanding how data works, what constitutes good data and how to interpret your results is an invaluable skill.
My next few blog posts will explore the data literate organization and how data illiteracy and magical thinking lead to bad projects with misleading results. Be sure not to miss them, and reach out with any questions.