In 1903, science fiction writer HG Wells reckoned there’d come a day when thinking in numbers mattered as much as reading and writing. We asked Sophia Shen from SAS about that line, and she didn’t hesitate:
“I think that time is now. We use data to make decisions every day, which bus we should take to get to school on time, what headphones should I buy, whether I should take an umbrella if the weather forecast predicts rain. We want to be data literate so we can make our lives easier.”
None of that feels like “data” in the maths-class sense. But it is, and getting comfortable reading it is quickly becoming as basic as literacy itself. We sat down with Shen for our Career Stories series to unpack what that actually means for students, and why it matters even if you’ve never written a line of code.
AI Has Already Clocked In
Data and AI aren’t some far-off future thing. They’re running quietly behind stuff you already use. As Shen puts it, hospitals use AI to analyse medical images and catch diseases early, zoos and farmers use it to monitor the health of their animals and livestock, and social platforms use it to keep your attention for as long as they can.
Which is exactly why she thinks this question matters more than people give it credit for:
“It’s very important to ask yourself, is AI helping me to achieve my goals or am I helping AI to achieve their goals?”
Worth sitting with that one for a second.
Good News: You Don’t Need to Code First
You don’t need to be a programmer to build this skill. Shen is blunt about what actually matters:
“You don’t need to be a programmer to be able to use data and AI. You just need to be curious and ask good questions. You can use AI to find information, but you do not want AI to do your thinking.”
That distinction, finding versus thinking, is doing a lot of work in that sentence. Whether you end up in tech or nowhere near it, Shen reckons understanding data will matter for whatever career you land in, so it’s worth starting now instead of waiting for uni to force the issue.
Question Everything, Especially the Confident Answers
AI tools hand you answers fast. What they don’t hand you is a reminder that those answers come from historical data, and historical data comes with bias built in. Shen’s advice here doubles as a decent life skill:
“Be critical about information you receive. Always verify the information with multiple sources. Ask yourself, can I trust this? Does this make sense?”
The more you practise asking those questions, the sharper your instincts get, and the less likely you are to take a confident-sounding answer at face value.
Where This Actually Gets You
You don’t need a job title with “data” in it to benefit from any of this. Shen made a point of not letting students off the hook on this one:
“You don’t have to have a career in technology to benefit from data and AI. Whether you want to work with animals, in sports, music, medicine, or business, understanding data and AI will be beneficial.”
It’s less a niche skill for future data scientists and more a baseline for anyone trying to make good decisions, whatever field you’re headed into.
Start Before It’s Homework
Shen’s own advice for getting started is refreshingly low-pressure: get curious, ask questions, and use the free resources sitting right in front of you. SAS runs free data literacy courses built for exactly this, no coding, no jargon, just the basics done properly.
So don’t wait for a school assignment to make you do it. Check out SAS’s free data literacy courses and start building the one skill that’ll follow you into whatever career you actually pick.
