Creating a culture of data literacy

The ‘Moneyball’ principle of data culture

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I was invited to speak at Ireland’s ‘National Analytics Summit’ in November, which initially came as a surprise, because I have … no training in data. I was a mediocre math student. I can’t use Python, R, and frankly I’m not even that great at excel. What little statistical analysis I can actually do myself is self-taught and pretty low-rent, in the grand scheme of things.

But I am a data enthusiast. That counts for more than you might think, and has put me in positions where I have led large teams of people responsible for data, led by data, creating the frameworks for data, and always infecting others with my weird, amateur passion for data. Of course, it doesn’t always go down as well as I’d like.

My argument to the group at the summit is that depending on your your company size, you are likely to see greater gains by focusing on your data enthusiasts than affording data experts. In short, you can Moneyball your data culture, to a certain extent.

Let me explain.

Data, for me, is about identifying opportunity and building enthusiasm about opportunity. I’ve mostly worked in content-first organisations, but one of the nice thing about data is that regardless of whether you’re trying to get users to spend more time with your content, or buy more actual physical products, or do something else, the concepts are all the same. You end up splicing and dicing spreadsheets, building a hypothesis out from a clear, empirical starting point, gathering context to allow you to test that hypothesis, and you’ve got something to return to, to show you if it’s worked. Data moves the needle in workplace debates in a way that mere opinion cannot. It has a great way of sucking the wind out of the sails of emotion and ego. So when you’re trying to build enthusiasm for a concept or an initiative, data is a great starting point.

Here’s a recent example.

DEAD RUSSIAN OLIGARCHS

A few weeks ago I was trying to convince a newsroom of 20-somethings that the story of how many Russian oligarchs were dying mysterious deaths in 2022 was worth remarking on. I shared the link to the Wikipedia page tallying the deaths but that didn’t sell it to the Gen Z crowd. So I took the deaths, grouped them by month in a spreadsheet, visualized it and added some emojis to get my point across.

Opinions were swayed, and we worked on the story. Because I can’t help myself, I posted an imperfect version on the subreddit r/dataisbeautiful where it drew scorn and praise in equal measure. The haters were neatly grouped in the ‘This thread is called r/dataisbeautiful, and this is FUGLY’ camp. But the beauty of this viz wasn’t in its aesthetic, it lies in its efficiency.

• It took 10 minutes to make.
• It gets a single point across very cleanly, and with the appropriate absurdity.
• It is universally comprehensible.

This, as it happens, was our formula for success when I build the data journalism team at Vocativ. More on that in a bit.

The data-to-google-sheets-to-visualization path is one I tread daily. I’ve convinced scores of other people to join me for minor, regular, almost casual forays into performance data to find and explain opportunity. Many of those people, like me, have little or no data experience. And often, the visualizations like the one above are really just the starting point of the conversation — they serve to highlight the question I’m asking.

While I have worked with amazing, highly-skilled data teams — at CBS News and WWE in particular — I’ve found that at the startup level, the greatest gains materialize when more people across the board are drawn into the data processes and gain confidence to participate. In Moneyball parlance — a lot of people who can consistently get on base wins you more games than a small number of big hitters.

If you’re not familiar with the book/movie, here’s the plot in a nutshell: Billy Beane is manager of the Oakland Athletics, the poorest team in baseball. He can’t compete with the big bucks of the Yankees and Red Sox, who are throwing money at talent to buy home-run hitters in droves. But Beane crosses paths with a data wonk (‘Peter Brand’ — based on Paul Podesta) who says that scouts are hiring on outdated metrics. You don’t need home-run hitters, he says, you want players who consistently get on first base. Those are the players who win games. And the bonus is: those players are undervalued, and come really cheap.

Think about this from the perspective of someone trying to hire a great data team. Not everyone has the deep pockets of the big firms or well-funded startups. We can’t all just send up a bat signal and hire the pick of the bunch, pulling money from a VC war chest to hire a big-hitting data scientist, like Bobby from the Big Leagues here.

https://twitter.com/realBobbyHealy/status/1579526564437315585

For smaller firms, cultivating a culture of data literacy and curiosity can bolster whatever data talent you can afford, and give you greater surface area for luck. More eyes on your data means you miss fewer of the opportunities lurking in your data. And even at bigger firms, pockets of data literacy have a way of breaking data & analytics teams out of their cubicles and silos. But it’s not an overnight process. For me, I went from data-allergic to chief data enthusiast in four steps.

Dangle The Carrot 🥕

To those who are strangers to it, data can be intimidating and boring. Somehow, you have to hook people on it. This happened for me when I joined Storyful back in 2010. I was pulled in by Gav Sheridan, a fellow blogger who opened up the world of data to me and others. He ran a blog called TheStory.ie wherein, among other things, he would obtain government datasets by FOIA, clean and organise them, and then publish them online to a network of hacks, tipsters and busybodies who might point out an odd number here, an out-of-place name there. These were the story opportunities, lurking in the data.

It was so simple, but it was SO POWERFUL. Gav and his collaborators sniffed out corruption, ended more than one political career. All it took to start the ball rolling in any of those investigations, to find the opportunity, to get you on first base, was the basics of data cleaning in Google Docs and some applied curiosity.

How to do it:

Teach your teams how to do the absolute basics of cleaning and organising a spreadsheet. Teach them to import a .csv or other filetype, or even use the <IMPORT HTML> function. Teach them lock rows and columns, rename things, format them for visual clarity and sort them. Teach them to use conditional formatting to highlight anomalies or ranges. Make them comfortable playing with and shaping the data. This gives them permission to experiment and explore. Take time before you go deeper and start teaching them to split data out of columns, or create pivot tables. At this stage, it’s all about the gradual reveal of how a few simple actions empower them to take the lead in identifying opportunity.

Make It Visual 🎨

I started in journalism through photography, so my instinct is to make stories visual wherever possible. At Vocativ (RIP) I built and led a small but punchy data journalism team. Our remit was to put data at the heart of as many stories as possible. Although we were a tiny group, we accomplished that. I had two simple goals for that team. One was to engage as many journalists as possible in the process, so we set processes in place to train the reporters on the basics. If they could get the data to first base, we’d work with them to create a visual home run.

We were operating at that point in time where newsrooms realized their audiences were suddenly mostly mobile (rather than desktop) so our second goal was to make every visualization at-a-glance comprehensible on a mobile phone screen. (I have written about people not understanding this concept, angrily, here.) That did a few things. It freed us from the expectations of anything being interactive, which was a nice creative constraint, and it helped us create a style guide for our visualizations.

And we cranked it out. Day after day, this tiny team, aided by a group of data-curious journalists, made fun, engaging visuals from data. But we couldn’t have done it at that scale if the reporters hadn’t been enthused by what they saw in our visuals to learn the basics.

How to do it:

Start small. Have people play with the visualization tools in Google Sheets, or a tool like Datawrapper if you are more confident, to make simple charts & graphs. Remember: don’t overcomplicate it. A chart or graph only needs to explain one thing to be useful. Add a clear title/headline and a sub-headline if you like, they are often critical to the visualization’s success. Help people understand that different charts and graphs do different things. Many viz fail because they’re the wrong style for the job at hand.

Your end goal is to have a simple visualisation that stands on its own, that explains a single concept fully. If it requires a paragraph of text to explain it, it has failed (this is why there were emojis in the Russian businessman chart). A good chart should reduce the amount of explaining you have to do, reduce the amount of text you need on the page, and reduce the amount of thinking the user has to do. Don’t shy away from screengrabbing it, and then annotating it wherever you see fit to make your point cleaner.

Make it Daily 📅

When I worked at CBS News, my bosses handed the daily morning editorial meeting over to me to run, with a simple mandate: turn it on its head. Put understanding of our audience at the top, and we’ll get to the news after that.

So every morning we’d talk about what stories performed well the day before. This fell into a nice cadence, broken up by any unique or curious discoveries. Before long, the crowd and conversation in the room changed. We’d start with a chart or two and all of a sudden, editors were engaging directly with our data analysts. Senior producers (mostly this guy) would walk straight out of the control room after a long livestream and ask about the audience numbers.

Again, although we were an Adobe Omniture house. I was playing with stuff in Google Sheets. Data became part of the fabric of the day. It changed workflows, it changed product styles, it changed a lot of things.

How to do it:

Find a point in your day where it makes natural sense to incorporate some performance data. Be consistent in what you show and how you describe it, so that you build familiarity with the intended audience. But don’t be afraid to break the monotony once in a while by highlighting a new curiosity, or looking at things from another angle, or you simply find any other teachable moment from the data. Don’t just reel off KPIs, share your enthusiasm for understanding what’s under the hood. To do that, you have to demonstrate that the data keeps serving up things to be curious about, opportunities to explore and help people jump off into the details and context.

MAKE IT COUNT 💰

WWE was where my data and programming strategy work had the greatest impact on a company’s revenue. WWE was a money- and data-generating machine. Performance review decks ran to over 100 pages of charts & tables, and we had a rabbit warren of Tableau dashboards to navigate. If you love that stuff (and I do) it was great. The team behind the analysis were also fabulous to work with. And there were a few Irish names in there, to boot.

Sophisticated as the team was, I’d still come to many meetings with the data team armed with a Fisher-Price style Google Sheet and a thorny question. Data teams get frustrated when they put a bunch of work into a dashboard only to see the view metrics on that dash languish in single figures. I had some bona fides as a data nerd who actually also had their hand on the steering wheel, so it felt like my requests had a way of skirting prioritization queues because my team had a bias to action.

My team and I (I think) were seen as people who acted pretty fast on any data received. In an organisation where data was part of the everyday, a team of data enthusiasts, led by someone who empowered and prioritized data-led decisions, was able to get a lot done.

My low-level data nerdiness helped build a bridge into the data team. I bust into their silo and pulled them out in a jailbreak of sorts, together we would go on great, highly monetizable adventures.

So that’s it. Dangle a carrot — get people enthusiastic about data, show them its potential. Make it visual and make it daily — this makes it habitual and accessible. Then make it count — the silos will start to break themselves.