AI did not end the data literacy crisis. It changed it. As machines move from providing information to interpreting it and recommending what to do, the scarce skill is becoming judgment: knowing what to trust, what to question, and what decisions are still ours to make.
Evidence does not make decisions. People interpret it, weigh it, challenge it, and make judgments. A courtroom reveals what leaders can learn about evidence, expertise, confidence, and data-informed decision-making.
Feeling sure doesn't mean you're right. Learn five simple ways to question what feels true, spot what might be influencing you, and make better decisions about what to believe.
You already use data every day without calling it that. This first journey names what data is, separates it cleanly from opinion, and plants the ladder the whole track climbs: from data to wisdom.
She checked the forecast and grabbed an umbrella. She saw her phone say the train was four minutes out, so she left. She picked the podcast with the shortest run time for her walk. Before we go further, how much data has Shannon used?
Most people think data is something that lives at work, in dashboards and reports and the charts other people build. So when a course opens with the word "data," it can feel like the start of something technical and far away. It is not. By the time Shannon sat down at her desk, she had already read a rain percentage, a train arrival time, and a list of podcast lengths, and she had made a real decision on each one.
That is the quiet truth this whole track is built on. You do not need to learn how to use data from scratch. You already do it, constantly, in choices too small to notice: which route is faster, which show is worth two hours, whether the milk is still good by the date on the carton. Data is not a workplace tool you have yet to learn. It is something you already read and act on every day, and the skill is simply doing it more deliberately. This first journey names what data actually is, separates it cleanly from opinion, and shows you the ladder that every journey after this one climbs.
Five of Shannon's everyday choices. For each, decide whether it ran on data (facts or numbers she read) or was just a feeling (a mood or a habit with nothing behind it).
Complete this part to continue.
Here is the whole definition, and it is smaller than you might expect. Data is raw facts and figures collected from the world: a number, a date, a rating, a measurement, a yes or a no. The temperature was three degrees. The receipt was £4.20. The app shows 812 ratings. None of those tell you what to do. They are just true things, recorded.
What makes data useful is also what makes it easy to confuse with something else. Sitting right next to every fact are two things that are not data: an opinion ("this coffee is amazing") and an interpretation ("so this is the better shop"). Both feel like information. Both can be worth listening to. But neither is a raw fact collected from the world, and if you cannot tell them apart, everything built on top gets shaky. A decision that treats one person's opinion as if it were a measured fact is a decision resting on sand.
Five statements Shannon heard while shopping. For each, decide whether it is data (a raw fact) or not data (an opinion or an interpretation).
Complete this part to continue.
If opinions are not data, are they useless? Not at all. An opinion is where a lot of data starts. The trick is what happens next. One person saying "this blender is too loud" is a single perspective: real, but not something you can rely on. Collect that same judgment from three hundred people in a consistent way, count it, and something changes. "Two hundred and fourteen of three hundred testers rated the noise two stars or lower" is no longer one voice. It has been gathered, measured, and counted, and now it is data.
That is the move worth remembering: systematic collection and measurement is what turns a perspective into data. The opinion did not stop being an opinion. It got collected the same way, from many people, and counted, until the pattern across all of them became a fact you can stand on. This is also why a single glowing review and a rating built from a thousand of them are not the same kind of thing, even though they can sit inches apart on the same screen.
Five statements. For each, decide whether it is still just perspective (one voice) or whether it has been collected and counted into data.
Complete this part to continue.
Data on its own rarely tells you anything. "£12,000" is a fact, but it is silent. Add one piece of context, "sales were £12,000 last month, which is 25% below target," and it starts to speak. That small step, from a bare fact to a fact that means something, is the first climb in a ladder this whole track is built around.
The ladder has a name, and it is the name over the door: Data to Information to Knowledge to Understanding to Wisdom. Turning Data Into Wisdom is not a slogan, it is the path. Data is the raw fact. Add context and it becomes information. Connect information to what you already know and it becomes knowledge. Grasp why, and it becomes understanding. Act well on it, again and again, and that is wisdom. Every journey after this one moves you a rung or two up. You are standing on the bottom rung right now, and the only reason data matters at all is that it is where the climb begins.
Five statements. For each, decide whether it is raw data (a bare fact) or whether it has climbed a rung (context added, so it now means something).
Complete this part to continue.
Shannon is choosing between two coffee makers. She has pulled up the listings, read a few reviews, and texted her sister. Now six statements are swirling in her head, and they are not all the same kind of thing. Some are data, some are opinion, and one is an interpretation she has already started to draw. Sorting them is the whole skill of this journey: before you decide anything, know which of the things in front of you is actually a fact.
For each statement, choose whether it is data (a raw fact), an opinion (one person's judgment), or an interpretation (a conclusion drawn from facts).
You know what data is. Next in the track: The Shapes Data Takes, where the same everyday facts turn out to come in different types and formats, and knowing which is which is what lets you actually work with them.
