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

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

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

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

The dashboards and reports that will be outdated within a week.

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

Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.

Predicting all the ways data can ruin your day.

Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.

A statistical way to check if two things are related or if your data is just messing with you.

Ignoring that data quality issue until it causes real problems.

The reason your computer fan sounds like a jet engine.

When economics meets statistics and things get extra nerdy.

Shows up after work's done to sink regulatory fangs into your launch plans.

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

Hacking yourself before someone else does.

A central place for data that everyone fights over.

The numbers that make up your analysis—sometimes useful, sometimes just noise.

The mess left behind when shortcuts meet data analytics.

When you want fast answers and minimal thinking.

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

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

When one team gets credit for your analysis, and you get nothing.

“This report is valid until next quarter, when everything changes.”

Guards their "secret metric" like it's launch codes when it's really just page views in a trench coat.

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

Because reading rows one at a time is for chumps.

The constant struggle to keep data clean, secure, and useful.

The thing everyone builds but nobody documents.

Slicing and dicing data until it fits your argument.

Turning data into a fixed-size mess—useful for passwords, not so great if you ever need to reverse it.

The illusion of structure in your chaotic data world.

The family tree of your data, assuming you can track it.

That thing developers ignore until the database breaks.

Checking if your security is solid—or just wishful thinking.

A digital breadcrumb trail for when things inevitably go wrong.

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

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

The easiest SQL query that someone still wants to call a "data-driven insight."

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

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

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.

Making database queries run faster—because no one likes waiting 10 minutes for an SQL query to finish.

Proof that a company probably takes security seriously.

The awkward silence between launch and someone actually using it.

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

Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.

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

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

Because manually moving data is for people who hate themselves.

Trust no one, verify everything. Paranoia as a security strategy.

Because someone needs to process transactions in real-time.

Getting machines to do the boring stuff for you.

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

“Yes, our data platform supports SQL. That’s not a selling point.”

Sharing resources and pretending everything is fine.

“Can you analyze all our data from the last 10 years for a report we’ll ignore?”

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

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

The serial focus assassin. Everyone knows at least one.

Teaching computers to recognize patterns so they can pretend to be smart—until they overfit and fail.

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

Sifting through data, hoping for something insightful.

The buzzword architects love, but engineers fear.

Lives in a command line, thrives in mayhem. Breaks things just to make them better. Somehow delivers magic at 2 AM.

Moving data from one mess to another.

Poking around in your data to find trends, outliers, and problems before they ruin your model.

The reason your software updates faster than you can blink.

Fancy PowerPoint slides no one follows.

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

Absolute chaos agents.

Because raw data is just too ugly.

Holding onto data just long enough to avoid legal trouble.

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

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

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

Tweaking the settings of your machine learning model—kind of like adjusting the seasoning in a bad recipe.

When you can’t commit to a single cloud provider.

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

Because JSON wasn’t painful enough.

The legal hoops companies jump through to keep your data kinda safe.

Because manually checking your code is for the weak.

Because not every department deserves full database access.

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

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

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

The one number we stare at while ignoring the iceberg.

When real-time isn’t worth the hassle.

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

Where structured data goes to drown.

When leadership changes the KPI goal after you’ve already built the report.

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

The never-ending battle between hackers and IT teams running on coffee.

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

A group of overworked data engineers and analysts thrown together to fix a reporting disaster.

Keeps the data stack humming so analysts can pretend it’s “just a quick query.”

Because spreadsheets just don’t scale.

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

Bridging the gap between development and IT operations.

Rules everyone agrees on but nobody follows.

Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.

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

Preparing for disasters that will still somehow surprise you.