Data is a meme... and that's a good thing

🤔 What’s a meme?

Take a look at top posts in r/dataengineering and you’ll notice a trend: memes dominate. While expected on Reddit, LinkedIn has started to look eerily similar. Professional circles only recently adopted memes as a communication style, but they’ve quickly gained traction. Anecdotally, I’ve noticed that a larger share of my interactions (DMs, texts, mentions) have become meme-oriented, but maybe that’s just part of being a millennial.

Memes have been around for quite a while, though they’ve only gained steam in the past decade or so. At the core of a meme is an idea, usually tinged with comedy, that conveys a relatable message. The power of memes comes in their stark simplicity: a meme can traverse complex ideas yet make them available to a broad audience.

Today, many data creators are opting for short, catchy headlines, which can make LinkedIn look and feel quite a bit like Reddit. Don’t get me wrong, there are clear differences: the anonymity of Reddit lends itself to unconstrained commentary, which can be helpful for an honest opinion but is also responsible for some of the worst parts of the internet. 💀 😂

Unfortunately, this means a larger share of content is full of gimmicks. Influencers are attempting to build an audience and monetize it, while others drone on without many new ideas. It seems most have lost sight of the goal: to generate value through discourse and knowledge-sharing.

I incline to remain a “purist” and eschew such content as trite and shallow. I have a bias toward more time- and effort—intensive endeavors: composing an argument, writing, revising, and the like. However, an unfortunate truth is that long-form content will always be limited by audience size. From my experience in product & analytics, I know what a small share of the population truly engages: I suspect .1 to 1% of LinkedIn users actually read an article they see.

Memes and “LinkedIn-one-liners,” by contrast, are much lower friction. A catchy hook with ”… see more” and it’s off to the races.

While it may produce annoying haiku-like “LinkedIn” prose, I claim this provides indirect value to the data community. We’re all familiar with the evils of content algorithms, but they excel at surfacing viral content. Of course, asking for viral content is sort of like a genie granting your wish— you never know what you’ll get and it’s likely something you didn’t want in the first place.

🤷‍♂️ Short-form isn’t that bad

The upside to it all? Some of this viral content is quite good, useful stuff. I’m far too old for TikTok (or at least that’s what I tell myself), but some acquaintances have relayed to me that they’ve learned quite a bit through the platform (and TikTok’s marketing team is running with that idea).

Similarly, while a good bit of LinkedIn will always be self-promotion, humble brags, and influencers, I’ve also found it to be an excellent platform for discovering and learning new information. The small sparks of curiosity that I get from a new post can turn into projects, which turn into blog posts, implementations at work, new connections, etc.

A few weeks ago I saw Daniel Beech’s post about DuckDB vs. Polars. Well, I saw a shorter one-line post that made me excited for the full piece. That led me to experiment with DuckDB the same way he did— and led to some pretty awesome feature discovery (blog forthcoming) and a better understanding of what DuckDB is. 🦆

In turn, I’ve used this content in a few personal presentations, including a lightning talk, and it’s helped me share some cool, exciting stuff in the data space— the whole point of knowledge-sharing and community! Furthermore, it’s opened my eyes to how I can use DuckDB to be a better engineer.

Similarly, I’m often impressed by Khuyen Tran’s insightful tidbits. Both her consistency and quality are admirable. The information she’s shared has helped me to both learn and affirm existing knowledge.

The fact that something is a meme or comes in a short-form, digestible post doesn’t exclude it from providing value. As humans, we’re prone to judge books by their covers. From exploring new music (where I’m always drawn to interesting album art) to dating and relationships, first impressions matter. We can do our best to correct this bias, but our evolution is pretty compelling.

Unfortunately, as content becomes easier to create, volume explodes. This can feel distracting, noisy, and even stressful. Sifting through memes, ads, and self-promoters is a chore that can be mentally draining. AI will only accelerate this trend. The challenge will continue to be filtering the signal from the noise: finding valuable, unique resources that improve pique your interest, and move you closer towards your goals (like building community!)

I’ve found inspiration by engaging with memes and have discovered new, high-quality, ideas. While it creates more work in the “discovery” stage, short-form content appeals to those with a smaller amount of time/attention, while providing an opportunity to engage in conversation, read a companion article, and experiment with new ideas.

🏗️ Building community

If we think about what it means to build a community, we first must define what a community is. Communities are driven by commonalities among groups of people. Much has been written about what a “meme” is, so I won’t belabor the point, but memes simplify ideas in catchy, relatable ways.

It’s this relatability that’s interesting to me— in the data space, memes build a shared context and make us feel like we’re not alone. Perhaps related to the prevalence of imposter syndrome (a term I’m not a fan of, but that’s a hot take for another time) or the subsequent “smallness” of the data world, memes have helped me relate to the data community and feel like I’m a part of something. Moreover, memes are fun: who doesn’t like joining in on a joke or poking fun at a concept?

If we can use memes to build our community, they’re serving the data space. If memes can generate discussion, highlight flaws, and spur innovation, they’re valuable— could a disruptive company or new tool result from a meme? The power of a single idea is often understated.

Yes, there are those peddling solopreneurship, trying to sell their brand, and generally serving clickbait. That may provide negative value to newcomers or distract even seasoned professionals, but this is only a side effect. Pairing long-form content with memes creates a shared context and makes ideas more accessible.

I vote that we embrace meme culture to the extent it generates discussion, sparks learning, and promotes long-form content. Given that data (as a field) is much newer than software, we still have a ways to go. But, like recent advancements in data tooling, I see the community being pulled along in pace, partially thanks to memes.

Through posts like this, I aspire to give back to the data community by sharing perspectives and generating discussion. If memes and short-form content can help me achieve that, it’s simply dogma to ignore them. This post was a bit more meta than usual, but I think the meme idea is an interesting one. I’d love to hear your thoughts/ideas & feedback. Thanks 🤘

— Matt

P.S. Here are a few fun memes for your time 😃