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".

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

Because SQL SELECT wasn’t fancy enough.

Tweaking a button color and calling it "strategy."

Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"

Sharing resources and pretending everything is fine.

The Costco of structured data.

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

The serial focus assassin. Everyone knows at least one.

Because “I think this field means…” shouldn’t be part of data analysis.

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

Redefines success metrics faster than politicians backpedal after an election.

Sorting data into neat categories, only for users to ignore them.

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

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

When processing big data was still cool.

Making sure your servers aren’t crying for no reason.

This query better finish before the meeting, or I’m in trouble.

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

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

When your data is so bloated no one knows what to do with it, but it sounds impressive.

The one dashboard we all agreed on… until someone else made a new one with different numbers.

The terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.

Because finding the right dataset shouldn’t feel like a scavenger hunt.

A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.

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

Blueprints for security that companies try to follow.

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

Removing errors, duplicates, and someone else’s bad decisions.

Teaching models with labeled data—kind of like school, but for algorithms.

Keeping data within borders—because governments say so.

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

Absolute chaos agents.

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

Copying data from one mistake to another.

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

When your system crashes but pretends it never happened.

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

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

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

Someone else’s computer, but shinier.

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

Hacking yourself before someone else does.

“I have 10 dashboards to fix and zero time for your ad-hoc request.”

A strategic delay tactic used to avoid commitment in meetings with more than three directors present.

When search meets machine learning and everyone gets confused.

When economics meets statistics and things get extra nerdy.

Moving data to the cloud—hopefully without breaking everything.

Convincing everyone that my version of the dashboard is the truth.

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

Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.

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 Data Lake’s evil twin.

Following data laws just enough to avoid fines.

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

Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?

Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.

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

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

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

Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.

Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.

The thing everyone blames but nobody fixes.

Organizing data at a scale where things will go wrong.

Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.

That thing developers ignore until the database breaks.

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

Holding onto data just long enough to avoid legal trouble.

Making complex queries expensive since forever.

The delicate art of begging people to care.

Like a Data Lake, but with regret control.

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

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

Moving data from one mess to another.

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

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

Because well-managed data is the difference between insights and chaos.

The lazy friend of machine learning—it just looks at its closest neighbors and copies them.

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

“We’ll consider all possible factors… except the ones that make us look bad.”

A fancy word for "number we use to see if our model sucks or not."

A chaotic attempt to explain why the numbers don’t match across reports.

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

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

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

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

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

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

Like moving houses, but with more downtime and crying.

Checking your data before it embarrasses you.

A checklist of rules to follow… until regulations change again.

Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.

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

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

Keeping secrets… until someone forgets to lock the database.

Cutting back on data storage costs until everything runs painfully slow.

Workflow automation, so you don’t have to babysit data pipelines.

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

Code for “this could’ve been a Slack message.”

Because mistakes were made.

Making sure data stays trustworthy—or at least looks like it.

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