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.
When a relational database is too much effort.
Granting permissions based on job roles, not personal favorites.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
Guards their "secret metric" like it's launch codes when it's really just page views in a trench coat.
The alarm system for when hackers come knocking.
Shoving a half-baked feature into the project at the last minute.
Renting someone else’s servers but paying more.
A structured way to work with large datasets.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
Google’s way of making your SQL queries cost a small fortune.
Tweaking a button color and calling it "strategy."
The legal hoops companies jump through to keep your data kinda safe.
A digital breadcrumb trail for when things inevitably go wrong.
Data’s glow-up into something actually useful.
Slicing and dicing data until it fits your argument.
A table that tells you how often your model gets things right (or, more realistically, how often it screws up).
Digging through massive datasets, hoping to strike gold.
The go-to event for data professionals who want to rethink how governance is done. Join experts reimagining the future of what AI-readiness looks like
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
The chaos of switching from Excel to an actual BI tool.
Treats your dashboards like a digital coloring book.
Trying to convince non-technical people that data matters.
Redefines success metrics faster than politicians backpedal after an election.
Keeping data within borders—because governments say so.
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
“Yes, our data platform supports SQL. That’s not a selling point.”
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
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.
The law that keeps finance teams on their toes.
Checking your data before it embarrasses you.
All the missing data that everyone pretends doesn’t exist.
Finding out where all the secrets are hiding before someone else does.
Turning monolithic problems into distributed chaos.
When everyone agrees on what to pretend to care about.
The magic behind neural networks—basically, trial and error on steroids until the model gets it right.
Like moving houses, but with more downtime and crying.
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
“We need to filter this data in every way possible until it agrees with us.”
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?
Making sense of numbers so businesses can pretend to be data-driven.
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.
Proof that a company probably takes security seriously.
Frankenstein’s monster made of expensive software.
Because manually checking your code is for the weak.
A passive-aggressive way to say “this will be your problem soon.”
Poking around in your data to find trends, outliers, and problems before they ruin your model.
The awkward silence between launch and someone actually using it.
Handpicking quality data like it’s fine wine.
When you want fast answers and minimal thinking.
Metadata management to keep track of your ever-growing data jungle.
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
Stripping personal details so data looks anonymous (but isn’t always).
A statistical way to check if two things are related or if your data is just messing with you.
Making sure data doesn’t become a dumpster fire.
Keeping unauthorized users out - until someone shares a password.
The IT version of “Ctrl+Z” for disasters.
Absolute chaos agents.
Ignoring that data quality issue until it causes real problems.
The science of making sense of data—assuming it’s not lying to you.
Keeping secrets… until someone forgets to lock the database.
Saving progress so your system can crash at a later, more inconvenient time.
Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.
Stalking customers, but make it “data-driven.”
The serial focus assassin. Everyone knows at least one.
Because SQL SELECT wasn’t fancy enough.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
The family tree of your data, assuming you can track it.
Making pretty charts so people think the data makes sense.
The theoretical version of your data that reality refuses to match.
Convincing everyone that my version of the dashboard is the truth.
Hoping two systems eventually agree on reality.
Where your data has commitment issues.
Following data laws just enough to avoid fines.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
Helping engineers understand how data flows, transforms, and actually works.
A job posting for a data analyst who can also engineer pipelines and train AI models.
The awkward middle child of structured and unstructured data.
Idea-vomiting buzzword dispenser.
“We’ll consider all possible factors… except the ones that make us look bad.”
Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.
The programming language everyone pretends to know.
Where your data goes to sleep.
Translating raw data into real-world meaning so it’s actually useful.
The stuff hackers (and marketers) dream about stealing.
The reason your reports make no sense.
Preparing for disasters that will still somehow surprise you.
Making teams promise they won’t break each other’s data pipelines.
The dashboard everyone ignores until an executive asks for it.
When two teams argue over whose data is right until they both give up.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
Fancy PowerPoint slides no one follows.
A checklist of rules to follow… until regulations change again.
Stripping away identities because privacy lawsuits are expensive.
Doing more work with fewer complaints—on a good day.
A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.
The reason your computer fan sounds like a jet engine.
Invisible data hero who's seen SQL horrors that would make junior devs cry.
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