Technologically frozen in 1995. Still thinks "the cloud" is for rain and refuses to click anything newer than Solitaire.

Corporate for "I forgot what this is about but I need to make noise before someone notices".

An interactive report that executives will ignore until they ask for the same data… in an Excel sheet.

Because not every department deserves full database access.

“We made a pretty chart—please pretend it changed your decision-making.”

Collecting data the unethical-but-effective way.

Running the same weekly report with slightly different date filters.

The stuff hackers (and marketers) dream about stealing.

Organizing data at a scale where things will go wrong.

When two teams argue over whose data is right until they both give up.

The delicate art of begging people to care.

Just because two things happen together doesn’t mean one caused the other. Like, eating more cheese doesn’t actually make you better at math.

Getting the most out of your budget before the CFO notices.

A checklist of rules to follow… until regulations change again.

Because finding the right dataset shouldn’t feel like a scavenger hunt.

The bare minimum dressed up like a competitive edge.

Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?

Protecting user info while secretly monetizing it.

A minor data visualization tweak that gets presented as groundbreaking.

Feeding your data pipeline a never-ending buffet.

Because sometimes, you actually want long-winded responses.

A strategic delay tactic used to avoid commitment in meetings with more than three directors present.

Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.

The dream every company sells but never actually delivers.

Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.

The awkward silence between launch and someone actually using it.

When real-time isn’t worth the hassle.

Someone else’s computer, but shinier.

“I haven’t looked at the data yet, but I will… eventually.”

When everyone agrees on what to pretend to care about.

The lazy friend of machine learning—it just looks at its closest neighbors and copies them.

A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.

Where we test new models and hope no one deploys them to production by accident.

Fluent in stakeholder management, and can turn vague requests into scarily accurate dashboards. Built half the team's workflows on vibes and somehow made it work.

Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.

Cutting back on data storage costs until everything runs painfully slow.

When a relational database is too much effort.

A structured way to describe data relationships (or overcomplicate things).

Retiring an old dashboard but keeping the dataset running ‘just in case.’

Google’s way of making your SQL queries cost a small fortune.

Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.

Ignoring that data quality issue until it causes real problems.

The thing everyone blames but nobody fixes.

A/B testing’s overachieving cousin.

The science of making sense of data—assuming it’s not lying to you.

Turning raw data into fancy charts that people ignore.

Keeping data safe from hackers, leaks, and bad employees.

Sharing resources and pretending everything is fine.

Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.

Making sense of numbers so businesses can pretend to be data-driven.

Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.

Guessing with data—because flipping a coin isn't "data-driven."

Automating code merges so your team doesn’t go crazy.

Corporate deity whose random breakfast thoughts outrank your entire research department.

Stopping data leaks before they make headlines.

When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.

A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.

“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”

All the missing data that everyone pretends doesn’t exist.

The reason your reports make no sense.

Metadata management to keep track of your ever-growing data jungle.

Data that refuses to fit into neat tables—think text, images, and the chaos of the internet.

A digital breadcrumb trail for when things inevitably go wrong.

Because SQL SELECT wasn’t fancy enough.

The unlucky souls tasked with keeping data under control.

Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.

Proof that "we'll fix it later" never actually means later.

Hoping two systems eventually agree on reality.

Frankenstein’s monster made of expensive software.

Talking to inanimate objects because humans are worse.

It’s not just a conference—it’s a group hug wrapped in YAML. No fluff, no gatekeeping—just real talk from data practitioners sharing their learnings and strategies.

Renting someone else’s servers but paying more.

A flowchart-like model that makes decisions—think "choose your own adventure" but with math.

No one understands the report, but we’re pretending we do.

Because “I think this field means…” shouldn’t be part of data analysis.

Shoving a half-baked feature into the project at the last minute.

That thing developers ignore until the database breaks.

Data’s glow-up into something actually useful.

Getting access to the full raw data without documentation or guidance.

The programming language everyone pretends to know.

Tracking data’s dramatic journey from birth to deletion

“Will this dashboard break when more than 5 people refresh it at once?”

Making sure your servers aren’t crying for no reason.

“We ran the same SQL query but indexed a column, so now it’s 2% faster.”

A marketing term for "we kinda fixed the Data Lake problem."

Fine-tunes LLMs like they’re sourdough starters. Has five GPU credits left and no intention of using them responsibly.

Fake data used for training models when real data is too sensitive, messy, or non-existent.

When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.

Making teams promise they won’t break each other’s data pipelines.

The badge that says “We take security seriously” (but still have breaches).

“This dashboard is broken, but let’s not discuss it in front of leadership.”

A last-minute meeting because someone didn’t read the dashboard.

The theoretical version of your data that reality refuses to match.

Transforming categorical data into numerical form—because computers just don’t get words.

Sorting data into neat categories, only for users to ignore them.

Extract, transform, load—the classic data pipeline approach.

Bridging the gap between development and IT operations.

Data about your data—because keeping track of what your numbers mean is harder than it should be.

Brings structure to chaos with dbt and a folder hierarchy that could win awards.

The key metrics leadership suddenly decided to care about this quarter.

Keeping track of all the ways hackers can ruin your day.

Redefines success metrics faster than politicians backpedal after an election.

That thing you forgot to set up before the system crashed.