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.

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

How much pain your system can handle before collapsing.

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 awkward silence between launch and someone actually using it.

Stopping data leaks before they make headlines.

The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.

Someone else’s computer, but shinier.

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

Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.

The theoretical version of your data that reality refuses to match.

Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.

The unlucky souls tasked with keeping data under control.

Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.

Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.

Sharing resources and pretending everything is fine.

“We made a pretty chart—please pretend it changed your decision-making.”

That thing developers ignore until the database breaks.

Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.

The dream every company sells but never actually delivers.

The reason your database admin hates you.

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

Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.

Running the same weekly report with slightly different date filters.

The behind-the-scenes data that keeps everything (barely) organized.

The reason your reports make no sense.

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

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

Stripping personal details so data looks anonymous (but isn’t always).

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

Keeping data within borders—because governments say so.

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

Shipping code faster than your team can fix bugs.

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

Human API who communicates in endpoints and considers UIs a moral weakness.

Keeping unauthorized users out - until someone shares a password.

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

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

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

Tweaking a button color and calling it "strategy."

“This data connector technically works, but barely.”

Organizing data at a scale where things will go wrong.

Trying to convince non-technical people that data matters.

Because SQL SELECT wasn’t fancy enough.

The Costco of structured data.

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

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

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

“We need to filter this data in every way possible until it agrees with us.”

Making sure your app doesn’t make users want to throw their devices.

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

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

Preparing for disasters that will still somehow surprise you.

Transforming categorical data into numerical form—because computers just don’t get words.

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

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

“I forgot to check the dashboard before this meeting.”

Rules about data that everyone agrees on but nobody follows.

The art of making sure analysts don’t work with garbage.

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

Predicting all the ways data can ruin your day.

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

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

Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.

Because just because you can collect data doesn’t mean you should.

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

A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.

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

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

“I haven’t looked at the data yet, but I will… eventually.”

The dashboard everyone ignores until an executive asks for it.

The law that keeps finance teams on their toes.

Proof that a company probably takes security seriously.

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

The endless cycle of finding new ways to blame bad data for bad decisions.

The fight over who actually controls the data mess.

The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).

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

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

A structured way to work with large datasets.

The chaos of switching from Excel to an actual BI tool.

Double-checking data before it makes a fool of you.

Demands data-driven decisions then overrides everything because their morning shower had "different vibes."

A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.

When real-time isn’t worth the hassle.

Doing more work with fewer complaints—on a good day.

Spotting the weirdos in your data—because outliers can mean fraud, errors, or just bad luck.

Making teams promise they won’t break each other’s data pipelines.

We built it for five people and are praying it doesn’t break at ten.

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

Microsoft’s favorite way to make bar charts look really dramatic.

A marketing term for "we kinda fixed the Data Lake problem."

Moving data to the cloud—hopefully without breaking everything.

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

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

The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.

A flowchart-like model that makes decisions—think "choose your own adventure" but with math.

The stuff hackers (and marketers) dream about stealing.

A gradient boosting algorithm that wins Kaggle competitions—because sometimes brute force just works.

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

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