What's "Fundamental" in Data?

Last week, I joined up with Robin Hunt and Gini von Courter (fellow LinkedIn Learning Instructors) to launch a new series we're calling "Data Fundamentals Unpacked". In this LinkedIn Live series, we'll be appearing live in the LinkedIn feed every month or so to talk about "timeless and timely topics" from all over, under, and around the topic of data.

Check out our first conversation, where we talked about pivot tables, starting off with the simple question - are they actually fundamental or not? And then we talked a bit about COVID data–an interesting topic, to be sure.

In addition to this being a really fun conversation, it got me thinking about everything I do. What is fundamental? What must EVERYONE know in order to do this work that we do with data? And it's a surprisingly complicated question.

Everyone works with data these days. You may simply need to bubble up insights from a short survey of a couple dozen participants at a company event to find out what works and doesn't work. You don't need advanced analytical or statistics skills to do that. Or you may be a data analyst digging deep into large data sets to uncover highly nuanced findings to scrape an additional 0.5% of profits out of a multi-million dollar process.

Either way, one of the MOST fundamental skills we all need is data literacy. Sure, the level of data literacy comes in varying degrees, but you must be able to think critically about data, and speak the language of data (and data visualization) to succeed.

And data literacy is in surprisingly short supply. A survey on data literacy from Qlik found that just 24% of business decision-makers consider themselves data literate. In other words 3 out of 4 DON'T!!!! That is a crazy number! We're not talking about entry-level employees, or factory line workers who maybe don't need these skills. This is among business decision-makers!

I teach data literacy and think a lot about what the term means and how to enhance data literacy skills. And part of it is simply reminding people of things they already know and just sometimes forget to apply when working with data.

Robin, Gini, and I decided to make that the next timeless topic of this series. But we're taking a spin in it. We're going to focus on how data workers can encourage and share data literacy with their colleagues so their work is better understood and appreciated in their organizations.

If you have any ideas for a timely topic we should discuss, let me know! And make sure you join us for this conversation!

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