REVIEW: Be Data Literate (Second Edition) by Jordan Morrow

The first edition of “Be Data Literate” by Jordan Morrow was published in 2021, and this second edition, hot on its heels, has been updated for 2024 to include more on data literacy skills in the age of AI. Indeed, the preface ends with a wish from the author that “through data literacy and AI literacy, you prepare yourself to be strong, competitive, and help your organisation succeed in a new age of AI”. As a passionate advocate of data literacy and data fluency (incorporating data storytelling), this is where I find myself in my current role at my organisation. It’s important to note that, whatever your views and preferred uses on the three data terms I’ve just mentioned, it’s this that is focused on throughout this book, rather than the adjacent field of data visualisation itself. Indeed, later on in the book he also asserts the importance of understanding that data literacy is not data science.

We follow a really useful structure—once the concept of data literacy is defined as the ability to read, work with, analyse, and communicate with data, these strands are discussed in more detail with examples throughout a typical organisation. How does your R&D team need to read data? Or your Executive team? Do your IT team need to be able to work with data? Your Sales and/or Marketing teams? (spoiler alert—yes, of course!). Does your Product team, or your Exec team need to be able to analyse data? And how might your Data Science team, or your Finance team, need to communicate with data? Chances are, at least one of these examples will hit close to home in terms of your own corporate setup and needs to work together with greater levels of data literacy.

We learn the three C’s of data literacy: curiosity, creativity and critical thinking. These concepts resonate strongly with me—as a practitioner of data visualisation (often far removed from the business situation) my own ethos also revolves around these categories. Indeed, I publicise the themes of my own book with a talk entitled “How Curiosity leads to Creativity”. Whatever the circumstance, we always learn, appreciate and understand more with a curious and creative mindset. And these three Cs lead to a crucial fourth C—data culture. Every chapter and concept introduced implies an improvement in company data culture, and that is so often the holy grail we’re seeking!

So, given the likely audience of this review, and the background of the review’s author, how does data visualisation come into play, does it have a role in data literacy? In citing two of the most iconic visualisations to have stood the test of time in our field, Jordan considers John Snow’s 1854 Broad Street cholera outbreak map and Charles Joseph Minard’s 1869 visualisation of Napoleon’s March on Russia. The key word used in respect of data visualisation is simplification with data visualisation defined as a simplified approach to studying data.

Charles Joseph Minard’s Napoleon’s March on Russia (Source: Open Culture)

The comparison between a well presented dataviz and a table of 100,000 rows and 50 columns of data is marked indeed! And Minard’s visual allows us to simplify the details of Napoleon’s march. Snow’s visual allowed the local community to reach a simple conclusion—gathering water from the same spot by the same people was a key reason for the proliferation of the cholera outbreak. By halting this practice, a community was able to halt an outbreak of cholera and prevent further disease. From these iconic examples we conclude that simplification is the key to presenting data that encourages curiosity. Then we’re on the track of the three Cs!

John Snow’s Cholera Map (Source: Wikipedia)

And what of AI? The book’s revised version addresses the emergence of ChatGPT since late 2022. The clear takeaway is that every concept of data and data literacy has parallels with AI and AI literacy. AI can help us with every level of data analytics (descriptive, diagnostic, predictive and prescriptive), so enhancing your, or your organisation’s AI literacy reaches to all facets of data and data literacy. The data literacy parallel is that, in the same way that we don’t all need to know how to code, it behoves us all to have data literacy skills, so we don’t all need to know how to code AI, we all benefit from having the skills to utilise AI efficiently. The book offers confirmation, if it were needed, that anyone implementing data fluency or data literacy initiatives should now do so in a way that’s unbreakably linked to AI literacy, and offers simple steps to help us on the way.

The book’s subtitle is “The data literacy skills everyone needs to succeed”—it’s a book that explains and enhances my belief that not only is it important for me as a data professional, but important for everyone who comes across or interacts with data (and now AI) in their lives. In other words, everyone!

You can purchase Be Data Literate (Second Edition) from the publisher’s site, Amazon, or wherever you like to buy your books.