'Non-data people'

We're pigeonholing others - and ourselves, too


In data, more than in any other functional area, we segment people based on whether they’re in or they’re out — ‘data people’ vs. ‘non-data people’.

While there are obvious reasons for this, it causes a divide that makes me uneasy.

If we want to create data-centric cultures within our companies, we should be blurring the lines between the two worlds.

One foot in, one foot out

As a kid, I used to straddle the Canada/US border, thinking it was just the coolest thing that I was in two countries at once.

Many times over, I’ve felt like that in my work.

I’ve always had one foot on either side of the line.

I’m a ‘business person’. I studied Finance in college. I’m obsessed with EBITDA and generating business value and building companies. I love a good high-level, conceptual debate about corporate strategy or go-to-market approach or tactics for building an enterprise sales team.

But I’m also a ‘data person’. I studied Math in college, too. I vividly remember learning Bayes’ theorem and analyzing my first A/B test and being awed by the power of statistics being applied in a company at scale. I used to thrive on late nights debugging my SQL code. I’d love to discuss the implications of ELT vs. ETL with you just as much as I’d love to spar on corporate strategy or macroeconomics.

As people working in the field of data, why do we delineate so clearly between data people and non-data people?

With data becoming such a critical input to, like every aspect of business in this day and age, isn’t it kind of concerning that we draw such a clear line in the sand?

We don’t talk about ‘non-sales’ or ‘non-product’ people in the same way we talk about ‘non-data’ people.

And there are a lot of people out there like me, who aren’t necessarily in the weeds running Python and building models day-to-day, but still have a keen interest in data.

As ‘data people’, we should be empowering everyone to get comfortable with data, regardless of job title.

We should be working to expedite the cultural shift from the ‘us vs. them’ world of today to a world where everyone in an organization is capable of engaging in data-related work, to varying degrees of depth.

Why is there such a divide?

  1. Established norms of Nerds vs. Everyone Else

    In my ‘data person’ work, I’ve often been reminded of the vibe in my AP science classes in high school.

    We were so damn cliquey that we had Calculus Parties after our big tests, which was basically a self-proclaimed exclusive group of 17-year-olds getting drunk in whoever’s-parents-were-gone’s house.

    I hate to say it, but us ‘data people’ can be a little nerd-snobby sometimes.

    I do wonder if we have a bit of a ‘nerds’ vs. ‘everyone else’ inferiority complex from our pasts occasionally bleeding into the workplace.

  2. Data work isn’t always the most accessible

    Before the ‘data people’ flip over their (pivot) tables in uproar, hear me out.

    Data work is… not that simple. And we don’t always do a great job of communicating it simply.

    We keep inventing new terms like ‘Reverse ETL’, for crying out loud.

    And, to data teams’ credit, of course they don’t have time to build pretty explanations simplifying what they do.

    They’ve got JIRA boards full of tickets dating back to 2018 to get to first, thank you.

  3. The rest of the business has their own priorities, too

    They don’t study what the Data Engineer does or what SQL stands for or what this weird Snowflake thing people keep talking about is… because, well, they have their OKRs to hit.

    They just need last month’s ARR by customer segment for an urgent meeting in 20 minutes, please.

    ‘Non-data people’ need support to become data literate — and, when that doesn’t happen, the knowledge divide increases as data work becomes more and more advanced.

How do we merge the two worlds?

  1. Celebrate data work. Celebrate it widely & loudly. 🎉

    How many data team presentations have you had at your company’s All Hands in the past year?

    If you’re like most companies, the data team is probably under-indexed in visibility. Common knowledge of what what they’re doing and how it fits into the big picture is a good first step.

    Your data team is doing critical work. Surface it. Amplify it.

  2. Make space for ‘non-data people’ to ask the dumb questions 🙋‍♂️

    Don’t just tell — show. Find a really good translator internally who understands both sides.

    Show data work in the context of the one thing your business audience critically cares about, give them the space, and boy — will they engage.

  3. Break down silos & re-think accountability 💥

    I’m a big proponent of end-to-end accountability. I envision a future where the data capability is more deeply embedded within the business, and we don’t have this ‘service provider’ delineation that is typical of data teams today.

    But, to be realistic, that future is probably years out for most companies.

    For now, have a quick think about your org structure and processes. Addressing distinct separation of teams & improving collaboration can lead to better cross-pollination of perspectives.

Whether you’re a self-proclaimed ‘data person’ through-and-through, you’re someone who lives totally outside the wacky world of data, or you’re kind of straddling the line like me — I’d love to hear if any of this resonated.

Reply to this email or hit me up on Twitter @AnnikaSays. 👋