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.
When you can’t commit to a single cloud provider.
Europe’s way of reminding companies that data privacy matters.
“Here’s what you should do, but no one actually follows.”
Fine-tunes LLMs like they’re sourdough starters. Has five GPU credits left and no intention of using them responsibly.
Slicing and dicing data until it fits your argument.
A chaotic attempt to explain why the numbers don’t match across reports.
Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.
Cutting down the number of variables in your dataset—because sometimes, less is more (especially in Excel).
Deploying apps without touching infrastructure (until something breaks).
“Will this dashboard break when more than 5 people refresh it at once?”
Because someone needs to process transactions in real-time.
The behind-the-scenes details of how data was collected.
Wants to monitor every client blink without a clue what to do with it.
Pay a monthly fee to lose your files in someone else’s basement.
Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.
That thing you forgot to set up before the system crashed.
The bare minimum dressed up like a competitive edge.
Redefines success metrics faster than politicians backpedal after an election.
A table that tells you how often your model gets things right (or, more realistically, how often it screws up).
Finding out where all the secrets are hiding before someone else does.
Sharing resources and pretending everything is fine.
The law that keeps finance teams on their toes.
When one team gets credit for your analysis, and you get nothing.
The mess left behind when shortcuts meet data analytics.
Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.
Absolute chaos agents.
Microsoft’s favorite way to make bar charts look really dramatic.
Like a Data Lake, but with regret control.
Cutting back on data storage costs until everything runs painfully slow.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
The one number we stare at while ignoring the iceberg.
Frankenstein’s monster made of expensive software.
Protecting user info while secretly monetizing it.
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
Making pretty charts so people think the data makes sense.
The IT version of “Ctrl+Z” for disasters.
A gradient boosting algorithm that wins Kaggle competitions—because sometimes brute force just works.
Human API who communicates in endpoints and considers UIs a moral weakness.
A minor data visualization tweak that gets presented as groundbreaking.
The delicate art of begging people to care.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Google’s way of making your SQL queries cost a small fortune.
When your system crashes but pretends it never happened.
When economics meets statistics and things get extra nerdy.
Granting permissions based on job roles, not personal favorites.
A statistical way to check if two things are related or if your data is just messing with you.
“We need to filter this data in every way possible until it agrees with us.”
Bridging the gap between development and IT operations.
Teaching models with labeled data—kind of like school, but for algorithms.
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
A fancy word for "number we use to see if our model sucks or not."
“Let’s keep slicing the data until we find something that supports our assumption.”
Digging through massive datasets, hoping to strike gold.
Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.
500 commits in 3 hours. No documentation and no survivors.
Hiding sensitive data so developers don’t see what they shouldn’t.
Like conducting a symphony, but with way more screaming.
Ignoring that data quality issue until it causes real problems.
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
A/B testing’s overachieving cousin.
Workflow automation, so you don’t have to babysit data pipelines.
The theoretical version of your data that reality refuses to match.
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
Slapping AI on the same old nonsense.
Transforming categorical data into numerical form—because computers just don’t get words.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
Handpicking quality data like it’s fine wine.
Making sure data doesn’t become a dumpster fire.
Metadata management to keep track of your ever-growing data jungle.
When your company trends on Twitter for all the wrong reasons.
The difference between well-structured data and a digital black hole.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
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.
Brings structure to chaos with dbt and a folder hierarchy that could win awards.
Hacking yourself before someone else does.
The magic that makes your slow queries slightly less slow.
The Data Lake’s evil twin.
Blueprints for security that companies try to follow.
“Yes, our data platform supports SQL. That’s not a selling point.”
Because JSON wasn’t painful enough.
The legal hoops companies jump through to keep your data kinda safe.
Because “whatever naming convention feels right” is not a strategy.
Predicting all the ways data can ruin your day.
The unlucky souls tasked with keeping data under control.
The art of making sure analysts don’t work with garbage.
A digital breadcrumb trail for when things inevitably go wrong.
Getting the most out of your budget before the CFO notices.
Tracking data’s dramatic journey from birth to deletion
Moving data from one mess to another.
A fancy term for “don’t let hackers steal our stuff.”
Because sometimes, you actually want long-winded responses.
Lives in a command line, thrives in mayhem. Breaks things just to make them better. Somehow delivers magic at 2 AM.
The dashboards and reports that will be outdated within a week.
This query better finish before the meeting, or I’m in trouble.
“Your data reports need to be better, but we won’t give you more resources.”
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
Preparing for disasters that will still somehow surprise you.
Keeping data within borders—because governments say so.
Metrics that executives obsess over (but don’t always understand).
The reason your software updates faster than you can blink.
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