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.

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

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

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

Turning raw data into fancy charts that people ignore.

The bare minimum dressed up like a competitive edge.

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

“This report is valid until next quarter, when everything changes.”

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

“This dashboard is broken, but let’s not discuss it in front of leadership.”

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

Grouping users to prove that trends aren’t just luck.

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

The dashboard everyone ignores until an executive asks for it.

Sharing resources and pretending everything is fine.

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

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

Fake data used for training models when real data is too sensitive, messy, or non-existent.

Because SQL SELECT wasn’t fancy enough.

Because manually checking your code is for the weak.

Because not every department deserves full database access.

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

Where your data has commitment issues.

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

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

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

Data about your data—because keeping track of what your numbers mean is harder than it should be.

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

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

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

The IT version of “Ctrl+Z” for disasters.

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

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

Granting permissions based on job roles, not personal favorites.

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

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

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

The law that keeps finance teams on their toes.

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

The awkward middle child of structured and unstructured data.

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

Fine-tunes LLMs like they’re sourdough starters. Has five GPU credits left and no intention of using them responsibly.

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

The buzzword architects love, but engineers fear.

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

The awkward silence between launch and someone actually using it.

Holding onto data just long enough to avoid legal trouble.

Stripping away identities because privacy lawsuits are expensive.

The mess left behind when shortcuts meet data analytics.

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

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

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

Tweaking a button color and calling it "strategy."

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

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

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

Your code, but only when someone remembers it exists.

Moving data to the cloud—hopefully without breaking everything.

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

Like a Data Lake, but with regret control.

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

Absolute chaos agents.

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

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

When everyone agrees on what to pretend to care about.

Because mistakes were made.

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

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

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

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

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

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

Cutting down the number of variables in your dataset—because sometimes, less is more (especially in Excel).

Turning numbers into narratives people might actually remember.

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

Redefines success metrics faster than politicians backpedal after an election.

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

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

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

Treats your dashboards like a digital coloring book.

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.

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

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

Renting someone else’s servers but paying more.

The chaos of switching from Excel to an actual BI tool.

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

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

Making pretty charts so people think the data makes sense.

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

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

Slicing and dicing data until it fits your argument.

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

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

Protecting user info while secretly monetizing it.

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

Hacking yourself before someone else does.

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

Ignoring that data quality issue until it causes real problems.

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

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