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.

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

Moving data from one mess to another.

A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.

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

Creates JIRA tickets to track their JIRA tickets while drowning in chaos.

The stuff hackers (and marketers) dream about stealing.

When your model suddenly starts making terrible predictions because the real world refused to stay the same.

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

Keeping secrets… until someone forgets to lock the database.

Bridging the gap between development and IT operations.

The dashboard everyone ignores until an executive asks for it.

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

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

The awkward silence between launch and someone actually using it.

Stripping away identities because privacy lawsuits are expensive.

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.

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

When you pivot data just to confirm what you already knew.

Microsoft’s latest “one tool to rule them all” attempt—until the next one.

“This data connector technically works, but barely.”

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

When processing big data was still cool.

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

Because not every department deserves full database access.

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

The art of torturing data until it confesses something useful—or at least makes a nice chart.

Where structured data goes to drown.

Teaching machines to "think" so they can replace humans (but mostly just generate weird chatbot responses).

Because JSON wasn’t painful enough.

Running the same weekly report with slightly different date filters.

Keeping unauthorized users out - until someone shares a password.

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

Translating raw data into real-world meaning so it’s actually useful.

Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.

Europe’s way of reminding companies that data privacy matters.

DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.

Saving progress so your system can crash at a later, more inconvenient time.

Finding out where all the secrets are hiding before someone else does.

Because winging it with data governance isn’t a long-term strategy.

The difference between well-structured data and a digital black hole.

Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.

A table that tells you how often your model gets things right (or, more realistically, how often it screws up).

Making sure standard data values stay standard—good luck with that.

The magic behind neural networks—basically, trial and error on steroids until the model gets it right.

Following data laws just enough to avoid fines.

A job posting for a data analyst who can also engineer pipelines and train AI models.

Finding insights in data—or just realizing what’s missing.

XML’s cooler, slightly less annoying cousin.

Helping engineers understand how data flows, transforms, and actually works.

“Your data reports need to be better, but we won’t give you more resources.”

The mess left behind when shortcuts meet data analytics.

When you want fast answers and minimal thinking.

Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)

Deciding where to spend time, money, and energy—usually wrong.

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

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.

Metrics that executives obsess over (but don’t always understand).

The Data Lake’s evil twin.

How much pain your system can handle before collapsing.

Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.

Because spreadsheets just don’t scale.

Deploying apps without touching infrastructure (until something breaks).

Workflow automation, so you don’t have to babysit data pipelines.

Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"

The secret sauce behind databases that actually perform.

When search meets machine learning and everyone gets confused.

Rules everyone agrees on but nobody follows.

When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.

The law that keeps finance teams on their toes.

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

Pay a monthly fee to lose your files in someone else’s basement.

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

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

Protecting user info while secretly monetizing it.

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

The serial focus assassin. Everyone knows at least one.

Because bad data leads to bad decisions and lots of excuses.

The one number we stare at while ignoring the iceberg.

“Here’s what you should do, but no one actually follows.”

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

A measure of how spread out your data is—basically, how weird or normal your numbers are.

Goes to every conference and is part of every newsletter. Needs an intervention.

The fine art of deciding who gets in and who gets a "403 Forbidden."

Fixing data mistakes before they embarrass you.

Handpicking quality data like it’s fine wine.

500 commits in 3 hours. No documentation and no survivors.

A chaotic attempt to explain why the numbers don’t match across reports.

The behind-the-scenes details of how data was collected.

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

Tweaking a button color and calling it "strategy."

A fancy term for “don’t let hackers steal our stuff.”

“I have 10 dashboards to fix and zero time for your ad-hoc request.”

The dashboards and reports that will be outdated within a week.

Hoping two systems eventually agree on reality.

Because manually checking your code is for the weak.

Like a Data Lake, but with regret control.

“Throw some data models at the wall and see what sticks.”

When your system crashes but pretends it never happened.

The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.

Keeping data within borders—because governments say so.

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.