Zacharias 馃悵 Voulgaris

1 year ago 路 2 min. reading time 路 ~10 路

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Data Literacy in the Age of Data Analytics

Data Literacy in the Age of Data Analytics

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It was about thirty years ago when an uncle of mine, who was around 70 at the time, asked my older nephew (who was one year older than me), to explain to him computers. His rationale was that in the year 2000, anyone who doesn鈥檛 understand computers would be considered illiterate. From that moment onwards, I had a profound respect for my uncle. How many people his age, especially in the countryside of Greece, were that open-minded?

Perhaps computer literacy was enough in the 2000s when certificates like ECDL (the ACDL equivalent for Europe) were all the rage, and people of all ages were flocking to enroll in some private computer school to learn about Windows and MS Office. I hate to admit it, but I was among those people, even though, at that time, I was enrolled in a technical university so computer proficiency would be a given to any potential employer. Still, I wished to challenge myself, like my uncle before me. In any case, now we're in the 2020s, and computer literacy is more or less a given. What could be the next big thing that would be considered essential in the years to come? My money is data literacy, especially if you are in a data-driven field.

Let me get this straight, though: to avoid any misunderstandings: most fields today are data-driven or are in the process of becoming data-driven. After all, data is in abundance, and this trend is not slowing down; quite the contrary. With new technologies like the Internet of Things (IoT) and all sorts of telemetry online, data is truly abundant. The term Big Data from the 2010s points to that fact, though what was considered to be big back then is probably medium today! The fact that A.I. systems are taking on more and more alludes to the fact that big data isn't just a nice to have but a necessity for many industries that wish to leverage this technology. After all, an A.I. system thrives when it has access to lots of data. Some would consider enormous datasets a given whenever you'd use A.I. systems.

But data literacy is more than just having lots of data. It's a matter of discernment and appreciation. Just like Art is more than just a plethora of imaginative designs on a medium; it's a discipline that many people devote years to study so that they can understand this facet of culture well enough. And that's just the beginning. If you want to be a true artist, you need to continue studying it for many years after that, constantly practicing it. Data literacy isn鈥檛 any different. Perhaps less imaginative and more logical, but still, the similarities are evident.

Data literacy involves lots of different verticals (or vectors as some people would refer to them). In the following article, I'll attempt to highlight the most important of them in my experience. Till then, feel free to let me know your thoughts on this matter as well as what angles of it you'd be more interested in exploring. Cheers!

Whenever I don't write articles on beBee or explore the limits of what data science can do, I write on my blog, foxydatascience.com. Feel free to check it out when you have a moment.

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Comments

Ren茅e 馃悵 Cormier

1 year ago #1

Marketing requires data, and lots of it. The days of marketing without metrics are gone. When it comes to marketing, anything that can be measured, should be measured, and anything that can't be measured should not be taken seriously. That's my opinion, at least. 

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