The data curious have many options to ease into deeper understanding

By Ariane Bernard


New York City, Paris


I was at dinner with a former colleague of mine a few  weeks ago. This former colleague is no longer working in news publishing but instead leads a network of design schools. At one point, my friend tells me, “Oh you should create a product management curriculum for my students.”

“Before you do anything, have them learn Google Analytics” was my immediate suggestion for that curriculum. 

I actually feel this is a great first step for so many careers these days — and certainly for many news media careers, too.

Yes, for editorial roles and certainly for most careers on the business-side of news. Even if you think you don’t need it to work in your daily functions, it really is a transformative skill to have an understanding of what data is meaningful in your business and be able to answer some of your own questions for how your business works and how your users (customers) take it in.

This dovetails with something that’s been on my mind lately: How do we support folks at media companies (and this may be you, dear reader, or the person at the next desk over) who would like to augment their knowledge about data — from being data curious to having more in-depth understanding of how data works, how we collect it, how we decide what to collect, how metrics are created, how we analyse data and identify its blind spots … all the way to having a fuller understanding of how we can use data in more advanced applications like machine learning or predictions.  

So, this week I am looking at (formal) training in analytics for non-data folks.

As always, I’d love to hear from you — in particular, if you know what you wish you did have an opportunity to learn. What is the course you wish your company offered? Or that you’d like to find online? I’d love to know — and my inbox is at to start this conversation.

All my best, Ariane

The case for formal self-training for the data curious

For most people (anywhere), getting a good handle on descriptive analytics is the dimension of data that is going to be the most useful the most readily available in their day jobs. 

What descriptive analytics means: having access to data that describes the world and helps you zero on actual, demonstrated behaviours at scale. 

Using online resources to guide a self-taught education in descriptive analytics is most useful for the data curious.
Using online resources to guide a self-taught education in descriptive analytics is most useful for the data curious.

It’s not necessarily the most showy use of data ever — that would be prescriptive analytics, when analytical data is churned to suggest what may happen in the future and a set course of action is proposed to get there. But you gotta learn to walk before you can run, and any business, or person, who is starting on a journey with data will necessarily have a first step that will involve understanding more about how their business works or what their users do with their products. And there’s tons of value in this. 

There are several paths you could take, of course, to augmenting your understanding of analytics. But today, I want to suggest a path that is available to almost anyone because it doesn’t rely on the particular circumstances of your employment: deciding to take a self-guided course in analytics. 

Now you may say — well, it is perhaps a bit extreme to make this your first suggestion because this seems like a pretty significant commitment. But I think when folks who may call themselves data curious (many of you who receive this newsletter in fact!) are at a stage where they want to clarify and significantly grow their knowledge, it’s hard to do this without a bit of more formal learning. Because more casual learning (a blog post here or there) may just never give you the specific missing blocks that would be transformational for you. 

I’ve come to this conclusion after a few months leading the Smart Data Initiative for INMA because there has been a recurring feature in many of the conversations I’ve had with folks with a demonstrated interest in our topic here but who come from non-data backgrounds. 

At one point in our conversation, we’re talking about some data subject or another, and this person  will ask a question that actually rests on a fundamental of analytics or data engineering — like, “But why doesn’t X happen” or “But how do we know this.” And I’ve realised these are precisely the things that blog posts, no matter how insightful, don’t usually cover. 

That’s because while these aren’t necessarily super complex things, they don’t neatly fit into 1,000 words — a standard blog post. And because identifying these missing blocks is going to be pretty hit-or-miss. 

Think of it this way: All our non-formal knowledge (about anything) is a kind of Swiss cheese. Even for two people with the same amount of knowledge they have organically picked up here and there — from conferences, to Webinars, or reading relevant blog posts — the pieces of knowledge each person is missing are not going to be the same. 

So, as someone who does ask myself — every other week in fact — what could be an interesting topic for a news media person who works with data, one thing that’s become more and more obvious to me is that there aren’t a few obvious holes in the Swiss cheese where everyone is needing the same piece. For the most part, it’s a case of “We don’t know what we don’t know” — very hard to guide someone to their missing piece. 

Because the data curious are all missing different pieces of the data puzzle, self-guided training is the most efficient.
Because the data curious are all missing different pieces of the data puzzle, self-guided training is the most efficient.

I also remembered something that James Robinson, who is a director for data products at The New York Times, said to me about trying to bring data closer to non-data folks at The New York Times.

James was detailing how the NYT’s vast collection of analytics tools was creating a new problem for users — figuring out what tool was useful to learn about what problem. So, James told me, they wrote a guide to explain the various tools and their use. Except this didn’t prove to solve the issue. Instead, James said, “The problem is not that we don’t have a good map of the jungle. It’s that we need the jungle to be a well-attended garden.”

Self-training is learning to orient ourselves through the jungle in the first place — whether we’re given a guide or not — and data and analytics aren’t particularly easy jungles (to James’ point). So while we can collectively hope the data product managers and tool builders of the world may work at making the jungles more like gardens, until that happens, we have to figure out how to better walk through the jungle on our own.

Self-driven learning options for the data curious

If you are interested in checking out some of options for self-driven analytics training as a data-curious person, let me tell you … there are options.

I asked a few people what they recommended for folks who were number-literate but not data experts. Like a marketer or a social media editor. One suggestion I heard from several people was to take training directly from Google, though this, of course, is going to be relevant to folks who work in organisations that use Google Analytics in the first place.

Data training options are plentiful, including the Google Analytics Academy and Udemy.
Data training options are plentiful, including the Google Analytics Academy and Udemy.

You have options from big online platforms like Udemy, and one data analyst told me she sometimes looked at them for fun (!!) because they were heavy on practical tips in a way that the more industrial training you could find from the Googles of the world just weren’t focused on. 

Now, I am not linking to any specific course because there are a million of them, and you may prefer courses that are very practical, while other courses are much more about concepts and a broad overview. But there are really a lot of different options for you. And then, you can take some significantly more expanded course from Google on analytics (including a certificate-granting programme) via Coursera — no prerequisite required.

This type of programme isn’t specific to our use cases of media, but here’s the thing: Analytics skills are fairly non-industry specifics. You know how folks who work on the editorial side of your company usually come from news-related fields or other newsrooms? If you ask analysts at your company what their previous employment has been, you’re likely to hear that folks have done stints at health-care companies, banks, or automakers.

This tells you something: Analytics is a fairly context-agnostic field, and knowledge you gain there is going to be useful in a range of contexts and industries. So I would certainly suggest that you look into general analytics coursework if you want to deepen your expertise. 

Alternatively to fully online courses, there are options with organisations like General Assembly and probably other similar organisations near where you are based (INMA’s members are hailing from all over the world, so it’s difficult to dig deeply into who may be offering something relevant near you. Please accept my apologies). But online courses present some of the most flexible options and, with the abundant number of courses that offer video training, it’s also not as much a pure textbook affair as it used to be.

I come from product, where you kind of have to know a lil’ bit about everything. But you also have to be clear on the fact that your commitment to knowing a lil’ bit also ends at actual full-scale expertise. A generalist product manager isn’t the person to go to for advanced analysis on your Web site or who will come up with the specifics of how your tagging plan will be implemented. But can this person pull their own simple numbers, understand how they were measured and what they mean? They should be able to do this for themselves.

A news editor isn’t the social media editor. They do not need to know how to break down their social acquisition funnel six ways from Sunday. But can they take a URL and throw it in an audience analytics platform and pull some basic numbers for how the article performed and understand enough statistics to be able to contextualise these numbers?

I think the goal is that all news people can eventually do this, yes. 

Different teams need to know different levels of data specifics.
Different teams need to know different levels of data specifics.

There are some truly outstanding deep dives that exist on the Internet but usually for some more specific angles on analytics or data. The big basic building blocks aren’t really blog posts territory. But, a more structured course of study — even a short course — will fill in the blanks for the kind of questions you may never otherwise ask. If you are a social media manager, a product manager, and generally a “numbers-minded” editor, a course on analytics is pretty foundational. 

So now, should this type of training be offered by your news organisation? I would say yes, and in fact, many organisations do offer training for non-data roles — often tied to their specific implementations of some common analytics tools or their own custom tools if they have any.

Media companies like Gannett, Schibsted, The Times (UK), and NZZ in Switzerland are all examples of organisations that have long built out more academic training for folks around the organisation to level up. And some organisations also add to this training by teaching more general analysis skills like how to become a spreadsheet power-user (as a self-identifying spreadsheet fan, this made me extremely happy).

But while large media organisations have both the resources and in-house expertise to provide this type of enrichment, I would absolutely encourage folks who don’t have access to substantial training “at home’” to look to these Internet resources. While they are not stamped with the uses cases of media, they will feel very relevant.

In fact, even for folks who hail from larger organisations with training programmes, following an online course has one big advantage: Such training is generalist in nature and it can be a great complement to the more focused training provided in-house. If your organisation happens to offer some form of reimbursement for continued education, perhaps this is also covered. And if you worry that your manager may not be convinced that acquiring these skills would be relevant to your current job, I hope this blog post can help you make the case.

Further afield on the wide, wide Web

A couple of interesting links that have absolutely nothing to do with each other, so I’m not even going to try to pretend that they do:

Our next community call: Wednesday, July 6

Our next INMA Webinar is on July 6, at 10 am ET. We will take a look at the Digital News Report 2022 from the Reuters Institute. Our guest is Kirsten Eddy, post-doctoral research fellow at the Reuters Institute for the Study of Journalism, University of Oxford, and co-author of the report.

She will take us through a read of this (deep, deep) report with an eye for our specific interests in data. As always, these Webinars are free to you as an INMA member (or if your company is a corporate member), so join one and all!

About this newsletter

Today’s newsletter is written by Ariane Bernard, a Paris- and New York-based consultant who focuses on publishing utilities and data products, and is the CEO of a young incubated company,

This newsletter is part of the INMA Smart Data Initiative. You can e-mail me at with thoughts, suggestions, and questions. Also, sign up to our Slack channel.

About Ariane Bernard

By continuing to browse or by clicking “ACCEPT,” you agree to the storing of cookies on your device to enhance your site experience. To learn more about how we use cookies, please see our privacy policy.