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".

An interactive report that executives will ignore until they ask for the same data… in an Excel sheet.

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

Hoping two systems eventually agree on reality.

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

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

SQL’s rebellious younger sibling.

Metadata management to keep track of your ever-growing data jungle.

Absolute chaos agents.

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

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

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

Because not every department deserves full database access.

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

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

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

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

Renting someone else’s servers but paying more.

“Yes, our data platform supports SQL. That’s not a selling point.”

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

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

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

When you want fast answers and minimal thinking.

The reason your reports make no sense.

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

Because bad data leads to bad decisions and lots of excuses.

500 commits in 3 hours. No documentation and no survivors.

XML’s cooler, slightly less annoying cousin.

Teaching computers to recognize patterns so they can pretend to be smart—until they overfit and fail.

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

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

Data’s glow-up into something actually useful.

Rules everyone agrees on but nobody follows.

The dashboards and reports that will be outdated within a week.

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

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

Because sometimes, you actually want long-winded responses.

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

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

Running the same weekly report with slightly different date filters.

Because JSON wasn’t painful enough.

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

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

Stripping away identities because privacy lawsuits are expensive.

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

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

The thing everyone blames but nobody fixes.

Slapping AI on the same old nonsense.

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

Turning raw data into fancy charts that people ignore.

Trying to convince non-technical people that data matters.

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

A structured way to describe data relationships (or overcomplicate things).

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

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

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

Moving data to the cloud—hopefully without breaking everything.

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

Protecting user info while secretly monetizing it.

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

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

A digital breadcrumb trail for when things inevitably go wrong.

Preparing for disasters that will still somehow surprise you.

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

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

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

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

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

A structured way to work with large datasets.

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

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

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

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

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.

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

Your code, but only when someone remembers it exists.

Idea-vomiting buzzword dispenser.

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

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

The bare minimum dressed up like a competitive edge.

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

The magic behind neural networks—basically, trial and error on steroids until the model gets it right.

Tweaking a button color and calling it "strategy."

Rules about data that everyone agrees on but nobody follows.

“We ran the same SQL query but indexed a column, so now it’s 2% faster.”

Slicing and dicing data until it fits your argument.

When your system crashes but pretends it never happened.

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

Because mistakes were made.

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

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

Because manually checking your code is for the weak.

Talking to inanimate objects because humans are worse.

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

Because someone needs to process transactions in real-time.

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

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

Because spreadsheets just don’t scale.

The programming language everyone pretends to know.

Feeding your data pipeline a never-ending buffet.

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

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