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.

Corporate for "I forgot what this is about but I need to make noise before someone notices".

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

Turning monolithic problems into distributed chaos.

Translating raw data into real-world meaning so it’s actually useful.

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

Moving data from one mess to another.

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

Like a Data Lake, but with regret control.

Keeping data within borders—because governments say so.

Proof that "we'll fix it later" never actually means later.

Ignoring that data quality issue until it causes real problems.

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.

Wants to monitor every client blink without a clue what to do with it.

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.

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

Data’s glow-up into something actually useful.

When a relational database is too much effort.

The reason your reports make no sense.

The magic that makes your slow queries slightly less slow.

“This data connector technically works, but barely.”

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

Blueprints for security that companies try to follow.

Copying data from one mistake to another.

When one team gets credit for your analysis, and you get nothing.

For when the cloud is just too far away.

The reason your computer fan sounds like a jet engine.

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

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

The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.

Stripping away identities because privacy lawsuits are expensive.

Running the same weekly report with slightly different date filters.

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

Corporate deity whose random breakfast thoughts outrank your entire research department.

Because not every department deserves full database access.

Making data look important in executive meetings.

The reason your database admin hates you.

Extract, transform, load—the classic data pipeline approach.

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

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

The serial focus assassin. Everyone knows at least one.

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

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.

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

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.

When everyone agrees on what to pretend to care about.

Because “whatever naming convention feels right” is not a strategy.

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

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

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

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

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

Slapping AI on the same old nonsense.

How much pain your system can handle before collapsing.

The thing everyone builds but nobody documents.

Because someone needs to process transactions in real-time.

The buzzword architects love, but engineers fear.

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

Because winging it with data governance isn’t a long-term strategy.

Retiring an old dashboard but keeping the dataset running ‘just in case.’

When you want fast answers and minimal thinking.

A passive-aggressive way to say “this will be your problem soon.”

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

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

Because spreadsheets just don’t scale.

Slicing and dicing data until it fits your argument.

The fine art of deciding who gets in and who gets a "403 Forbidden."

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

Feeding your data pipeline a never-ending buffet.

When search meets machine learning and everyone gets confused.

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

Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.

Deploying apps without touching infrastructure (until something breaks).

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

All the missing data that everyone pretends doesn’t exist.

Where your data has commitment issues.

Because manually checking your code is for the weak.

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

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

Finding out where all the secrets are hiding before someone else does.

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

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

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

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

When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.

Making your inefficient queries slightly less embarrassing.

Deciding where to spend time, money, and energy—usually wrong.

The unlucky souls tasked with keeping data under control.

The programming language everyone pretends to know.

Someone else’s computer, but shinier.

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

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

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

The reason healthcare companies fear data leaks.

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

A central place for data that everyone fights over.

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

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

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

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