Data Literacy: a Tool for Life

Kofi Annan: [Data] Literacy is a bridge from misery to hope. It is a tool for daily life in modern society.

Oscar Wilde: "[Data] Literacy is the road to human progress and the means through which every man, woman and child can realise his or her full potential."

Mark Twain: "The man who does not read [data] has no advantage over the man who cannot read."

Margaret Fuller: "Today a [data] reader, tomorrow a leader."

You might have guessed that these quotations have been slightly adapted from statements about reading literacy. Beyond the societal impacts of a literate population, the development of knowledge and service economies have meant that literacy has been an essential tool for careers for generations. Reading literacy itself is still an issue, even in countries with overall high literacy, and directly impacts economic outcomes of families and populations. 

So now, well into the 21st century, could data illiteracy harm the economic opportunities of people and organisations? As always, it depends on the role and the industry, but generally speaking, being fluent in data will be crucial. And yes, LLMs like ChatGPT might give us new tools to interpret data, but we still need to be able to make judgments, rather than just using them as a black-box to solve all of our data problems.

In their Core Foundations for 2030, the OECD states that digital and data literacy are built on the foundations of literacy and numeracy, and that all children need these skills.The definition they use is:

Data literacy is the ability to derive meaningful information from data, the ability to read, work with, analyse and argue with data, and understand “what data mean, including how to read charts appropriately, draw correct conclusions from data, and recognise when data are being used in misleading or inappropriate ways” (Carlson et al., 2011[4]).

Measuring data and digital literacy is not as well established as measurements of reading literacy and numeracy, and most of the existing surveys are carried out by companies who are promoting data-related products. But what’s clear from all of these surveys is that business leaders think they and their staff need to be more data literate and there’s a willingness from employees to learn. The COVID-19 pandemic shone a light on the ability of the public, journalists and even politicians’ lack of data literacy.

The use of data by politicians and the media are closely tied to the latter part of the OECD definition: ‘recognise when data are being used in misleading or inappropriate ways’. There have been many articles dedicated to ugly, bad and misleading visualisations, but we all need to adopt ‘data sceptism’ as part of our data literacy armoury! This gives us some innoculation against being mislead, The precursor to this is simply to be able to ‘read’ data. Once you can read it, you can work with it and produce your own interpretations and draw conclusions. Obviously, you don’t want to keep these conclusions to yourself though. Being able to communicate your findings, tell a story with them and explain the value that can be gained through the resulting actions is essential.

So, we should be taking steps to improve our data literacy, but what should an organisation do to upskill its staff? If we want to be more data literate, we should probably start with some data. Giving your staff anything that looks like an exam should always be approached with caution - some people will always feel like such measurements will be used against them. But, it’s harder to benchmark without some kind of assessment. For this reason it makes sense to do this as part of an overall culture change, where teams are brought along on the journey, and the benefits of improved data literacy are explained. By finding the baseline, it’s much easier to identify gaps and weaknesses. It also means you can measure improvement over time.

How do we upskill staff? Well, as a starting point, don’t make it feel anything like a school maths lesson… Start with something interesting, then work back to the data. Within this there are opportunities to look at the basic skills, and then advance this to incorporate data from your own business. Generating interest in data can then be augmented with tailored training programmes (lots of these exist - depending on the resources available, using an existing toolkit may be more cost-effective than hand-crafting everything). 

One-off training alone though is not enough. There needs to be an ongoing data dialogue. This can be in the form of workshops,  hack-sessions and drop-ins. As well as support from the data team, colleagues in other teams who are already proficient can become data champions, and help encourage those who are developing their skills.