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.

“Let’s keep slicing the data until we find something that supports our assumption.”

The bare minimum dressed up like a competitive edge.

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

When you want fast answers and minimal thinking.

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

Making your inefficient queries slightly less embarrassing.

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

The frustrations of explaining, again, why two reports don’t match.

Turns their poor planning into your emergency with Slack messages that induce cardiac events.

Fluent in stakeholder management, and can turn vague requests into scarily accurate dashboards. Built half the team's workflows on vibes and somehow made it work.

Idea-vomiting buzzword dispenser.

Making complex queries expensive since forever.

Like moving houses, but with more downtime and crying.

Checking if your security is solid—or just wishful thinking.

The buzzword architects love, but engineers fear.

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

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

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

Copying data from one mistake to another.

A statistical method that updates what you believe based on new data—just like changing your opinion after checking Yelp reviews.

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

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

Keeping unauthorized users out - until someone shares a password.

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

The dream every company sells but never actually delivers.

Absolute chaos agents.

When real-time isn’t worth the hassle.

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

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

The illusion of structure in your chaotic data world.

Frankenstein’s monster made of expensive software.

Fixing data mistakes before they embarrass you.

Getting access to the full raw data without documentation or guidance.

Because someone needs to process transactions in real-time.

Fancy PowerPoint slides no one follows.

The universal answer to every data question, forever and always.

Telling you whether your results matter or if they’re just a fluke—like winning the lottery.

Making sure data doesn’t become a dumpster fire.

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

Because not every department deserves full database access.

Keeping track of all the ways hackers can ruin your day.

Stripping away identities because privacy lawsuits are expensive.

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

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

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

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

Saving progress so your system can crash at a later, more inconvenient time.

Sharing resources and pretending everything is fine.

Renting someone else’s servers but paying more.

Someone else’s computer, but shinier.

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.

Keeping multiple copies of your data in sync.

Tweaking the settings of your machine learning model—kind of like adjusting the seasoning in a bad recipe.

A digital breadcrumb trail for when things inevitably go wrong.

Your code, but only when someone remembers it exists.

The science of making sense of data—assuming it’s not lying to you.

Stopping data leaks before they make headlines.

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

Because reading rows one at a time is for chumps.

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

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

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

Turning numbers into narratives people might actually remember.

When everyone agrees on what to pretend to care about.

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

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

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

Deploying apps without touching infrastructure (until something breaks).

Making pretty charts so people think the data makes sense.

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.

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

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

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

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

For when the cloud is just too far away.

Digging through massive datasets, hoping to strike gold.

Like a Data Lake, but with regret control.

The difference between well-structured data and a digital black hole.

The one number we stare at while ignoring the iceberg.

The key metrics leadership suddenly decided to care about this quarter.

How much pain your system can handle before collapsing.

The easiest SQL query that someone still wants to call a "data-driven insight."

When your company trends on Twitter for all the wrong reasons.

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

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

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 theoretical version of your data that reality refuses to match.

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

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

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

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

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

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

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

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

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

Blueprints for security that companies try to follow.

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

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

Making data look important in executive meetings.