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.

“Here’s what you should do, but no one actually follows.”

Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.

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

The reason your reports make no sense.

Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.

When you want fast answers and minimal thinking.

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

“Can you analyze all our data from the last 10 years for a report we’ll ignore?”

The delicate art of begging people to care.

Moving data from one mess to another.

A data point that’s way off from the rest—could be an error, or could be the next big discovery.

Keeping unauthorized users out - until someone shares a password.

Tracking data’s dramatic journey from birth to deletion

Because JSON wasn’t painful enough.

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

The thing everyone blames but nobody fixes.

The illusion of structure in your chaotic data world.

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

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

Turning raw data into fancy charts that people ignore.

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

When economics meets statistics and things get extra nerdy.

Because manually checking your code is for the weak.

Making complex queries expensive since forever.

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

Holding onto data just long enough to avoid legal trouble.

The fantasy of having the same data everywhere at the same time.

When processing big data was still cool.

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

Like a Data Lake, but with regret control.

Redefines success metrics faster than politicians backpedal after an election.

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

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.

What you just got assigned because you asked a question in the meeting.

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

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

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

Goes to every conference and is part of every newsletter. Needs an intervention.

A fancy way of saying, “Re-use that old SQL query, but make it look fresh.”

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

Absolute chaos agents.

Trying to convince non-technical people that data matters.

Renting someone else’s servers but paying more.

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

The awkward middle child of structured and unstructured data.

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

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

The secret sauce that makes data searchable, understandable, and actually useful.

“We need better numbers, but we don’t want to change anything.”

SQL’s rebellious younger sibling.

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

Following data laws just enough to avoid fines.

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

Data’s glow-up into something actually useful.

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

Predicting all the ways data can ruin your day.

Someone else’s computer, but shinier.

A last-minute meeting because someone didn’t read the dashboard.

Hacking yourself before someone else does.

A free tool for tracking website traffic—until privacy laws step in.

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

“Throw some data models at the wall and see what sticks.”

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

A minor data visualization tweak that gets presented as groundbreaking.

Handpicking quality data like it’s fine wine.

When your model suddenly starts making terrible predictions because the real world refused to stay the same.

The badge that says “We take security seriously” (but still have breaches).

Getting machines to do the boring stuff for you.

Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.

Because “I have no idea where this data came from” is not a great answer.

Keeps the data stack humming so analysts can pretend it’s “just a quick query.”

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

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.

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

Shoving a half-baked feature into the project at the last minute.

Metrics that executives obsess over (but don’t always understand).

Copying data from one mistake to another.

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

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

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

The Costco of structured data.

Load first, transform later—modern data integration in action.

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

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

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

Deploying apps without touching infrastructure (until something breaks).

Organizing data at a scale where things will go wrong.

Fixing data mistakes before they embarrass you.

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

“Your data reports need to be better, but we won’t give you more resources.”

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

The buzzword architects love, but engineers fear.

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

The reason healthcare companies fear data leaks.

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

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

Hoping two systems eventually agree on reality.

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

Rules about data that everyone agrees on but nobody follows.

Automating code merges so your team doesn’t go crazy.