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.
Corporate for "I forgot what this is about but I need to make noise before someone notices".
The family tree of your data, assuming you can track it.
The reason your software updates faster than you can blink.
“This report is valid until next quarter, when everything changes.”
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
A structured way to describe data relationships (or overcomplicate things).
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.
Finding insights in data—or just realizing what’s missing.
Stripping away identities because privacy lawsuits are expensive.
Stripping personal details so data looks anonymous (but isn’t always).
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.
Protecting user info while secretly monetizing it.
Trust no one, verify everything. Paranoia as a security strategy.
Making teams promise they won’t break each other’s data pipelines.
The legal hoops companies jump through to keep your data kinda safe.
A statistical way to check if two things are related or if your data is just messing with you.
The awkward middle child of structured and unstructured data.
The key metrics leadership suddenly decided to care about this quarter.
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
The dashboards and reports that will be outdated within a week.
Running the same weekly report with slightly different date filters.
A last-minute meeting because someone didn’t read the dashboard.
Grouping users to prove that trends aren’t just luck.
Transforming categorical data into numerical form—because computers just don’t get words.
Where your data goes to sleep.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
When one team gets credit for your analysis, and you get nothing.
The reason healthcare companies fear data leaks.
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.
“We need better numbers, but we don’t want to change anything.”
The awkward silence between launch and someone actually using it.
The alarm system for when hackers come knocking.
Making sure your app doesn’t make users want to throw their devices.
A marketing term for "we kinda fixed the Data Lake problem."
Copying data from one mistake to another.
Creates JIRA tickets to track their JIRA tickets while drowning in chaos.
For when the cloud is just too far away.
Like moving houses, but with more downtime and crying.
The stuff hackers (and marketers) dream about stealing.
A minor data visualization tweak that gets presented as groundbreaking.
Slapping AI on the same old nonsense.
Preparing for disasters that will still somehow surprise you.
Organizing data at a scale where things will go wrong.
Tracking data’s dramatic journey from birth to deletion
When two teams argue over whose data is right until they both give up.
The behind-the-scenes data that keeps everything (barely) organized.
Fine-tunes LLMs like they’re sourdough starters. Has five GPU credits left and no intention of using them responsibly.
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
Collecting data the unethical-but-effective way.
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.
Someone else’s computer, but shinier.
“Let’s keep slicing the data until we find something that supports our assumption.”
Because “I have no idea where this data came from” is not a great answer.
Because JSON wasn’t painful enough.
Where we test new models and hope no one deploys them to production by accident.
The art of making sure analysts don’t work with garbage.
The thing everyone builds but nobody documents.
Proof that a company probably takes security seriously.
Keeping secrets… until someone forgets to lock the database.
Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.
What you just got assigned because you asked a question in the meeting.
Because sometimes, you actually want long-winded responses.
That thing you forgot to set up before the system crashed.
Hoping two systems eventually agree on reality.
Predicting all the ways data can ruin your day.
Because spreadsheets just don’t scale.
Feeding your data pipeline a never-ending buffet.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
Bridging the gap between development and IT operations.
“We ran the same SQL query but indexed a column, so now it’s 2% faster.”
Demands data-driven decisions then overrides everything because their morning shower had "different vibes."
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
Digging through massive datasets, hoping to strike gold.
Load first, transform later—modern data integration in action.
Because well-managed data is the difference between insights and chaos.
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
The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
Stopping data leaks before they make headlines.
The lazy friend of machine learning—it just looks at its closest neighbors and copies them.
The Data Lake’s evil twin.
The science of making sense of data—assuming it’s not lying to you.
Proof that "we'll fix it later" never actually means later.
Because bad data leads to bad decisions and lots of excuses.
The law that keeps finance teams on their toes.
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
Because raw data is just too ugly.
Convincing everyone that my version of the dashboard is the truth.
Turning data into a fixed-size mess—useful for passwords, not so great if you ever need to reverse it.
When your company trends on Twitter for all the wrong reasons.
Teaching models with labeled data—kind of like school, but for algorithms.
Shows up after work's done to sink regulatory fangs into your launch plans.
Lives in a command line, thrives in mayhem. Breaks things just to make them better. Somehow delivers magic at 2 AM.
The fight over who actually controls the data mess.
The fantasy of having the same data everywhere at the same time.
Renting someone else’s servers but paying more.
Automating code merges so your team doesn’t go crazy.
Talking to inanimate objects because humans are worse.
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