Business Intelligence

What’s wrong with the term data literacy? Here’s an alternative

Data Literacy is another buzzword that gives me some discomfort. I prefer the term data fluency, and I’ll explain why later. In the meantime, I thought I’d share some context on why I dislike the term data literacy because it hides much wider questions and problems. I don’t think data literacy is a very fair way of looking at how people interact with data. There’s the obvious point that data literacy makes people think of illiteracy, making the accusation that people are illiterate with data. I actually don’t think that’s true. I think sometimes people can choose to ignore the data that doesn’t fit with the narrative, and let me tell you a difficult and harrowing story.

The Hand Detectives

This post has been inspired by the Hand Detectives. Now, this is the work of Professor Dean Sue Black, who is one of the world’s foremost experts in forensic archaeology. In 2006, Professor Dame Black was contacted by Metropolitan Police in London, after evidence showed that a young girl had accused her father of abuse. So in order to prove her story, this young girl had the presence of mind to record the abuse one night by leaving Skype running on her laptop. Now, when the camera goes on at night, it flips into infrared mode which actually interacts with deoxygenated blood, highlighting the patterns in people’s veins. And your veins stand out like tram lines, basically, giving a unique match. The evidence showed that her father’s vein pattern had distinctive vein patterns, and it was found to identify the perpetrator. The vein patterns matched exactly what was shown on the video, and Professor Dame Black presented the evidence in court.

And the data showed that at 4.30am in the morning, someone that came into that young girl’s room, and the video showed exactly what happened. Despite the scientific evidence, the abuser was not convicted because the jury felt that the girl had not cried enough when giving evidence (not that she hadn’t cried, she hadn’t cried enough in their opinion). So, they didn’t believe her basically, in spite of the evidence, the data, and the expert witness testimony. For me, personally, that’s wholeheartedly unacceptable. I’m not sure what other explanation people found for that evidence and that data. I actually think about that girl a lot. I think how incredibly brave she was. And I think I let down she must have been – but not because of data literacy. The jury understood the data but preferred their own judgement instead.

What does this mean for data literacy?

Despite the term, data misunderstanding is not just a problem with maths understanding, confusion over statistics, or data visualisation. In that case, that jury had a range of scientific experts on hand, whom they could have asked any number of questions. The evidence was all there and it had been accepted by the jury. The data was not really questioned. What was questioned, however, was the girl’s character. I just think that’s it’s such a sad thing to happen in such a desperate situation. We are ask so much of our children to prove themselves in courts, that in spite of the evidence, they will still be questioned on their character and whether or not they cry enough.

Data literacy is part of our bigger problem, I think. People can see the data, but really ignore it, because it doesn’t fit in with our narrative. Victim-blaming is preferable, it would seem. Professor Black has gone on to create a hand library to analyse the hands in other videos in order to try and identify perpetrators. I think is incredible work. So it has had a good outcome, unfortunately, not for that girl, however. So much for data literacy.

How can we do better? Data fluency recognizes context and language in understanding data.

Data literacy assumes people are illiterate in some way. Instead of the term data literacy, I prefer the team data fluency. We can help people to become more fluent in the data that they are given. And understanding the biases that they perhaps have, when they are assessing data. Sometimes we jump to conclusions too quickly. Sometimes we don’t see or understand the data and maybe that can be a statistical problem, but that’s something that we can figure out.

What’s not so well seen, however, are the biases people bring to the data in the first place. Data literacy is a term does not acknowledge the context, which is why I see data usage as more like learning a language. You learn the words, you learn the grammar, and you also learn the context in which that language is spoken.

Data fluency is a muscle that we develop over time, taking context and understanding into account. Data understanding is more than just the numbers.

In data fluency, we take these building blocks, and we can turn them into something accurate, something meaningful, and something that has a really good impact. I’ve recorded a short video about it here, and I hope you’ll enjoy it.

I hope you enjoyed those thoughts. I’d love to hear from you. If you’d like to know more, get in touch.

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