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".

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

Scrambling data so only the right people (hopefully) can read it.

The reason your reports make no sense.

When processing big data was still cool.

Getting the most out of your budget before the CFO notices.

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

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

A statistical way to check if two things are related or if your data is just messing with you.

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

When a relational database is too much effort.

Because JSON wasn’t painful enough.

Granting permissions based on job roles, not personal favorites.

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

When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.

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

Blueprints for security that companies try to follow.

Because manually checking your code is for the weak.

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

Treats your dashboards like a digital coloring book.

XML’s cooler, slightly less annoying cousin.

Keeping unauthorized users out - until someone shares a password.

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

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

Deploying apps without touching infrastructure (until something breaks).

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

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

When economics meets statistics and things get extra nerdy.

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

Stopping data leaks before they make headlines.

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

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

Talking to inanimate objects because humans are worse.

That thing you forgot to set up before the system crashed.

Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.

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

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

Hiding sensitive data so developers don’t see what they shouldn’t.

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

When your system crashes but pretends it never happened.

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

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

Trying to convince non-technical people that data matters.

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

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

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

The awkward silence between launch and someone actually using it.

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

Digging through massive datasets, hoping to strike gold.

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

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

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

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

Checking your data before it embarrasses you.

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

The reason your computer fan sounds like a jet engine.

Making complex queries expensive since forever.

The lazy friend of machine learning—it just looks at its closest neighbors and copies them.

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

That thing developers ignore until the database breaks.

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

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

The delicate art of begging people to care.

Moving data to the cloud—hopefully without breaking everything.

The mess left behind when shortcuts meet data analytics.

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

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

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

The reason healthcare companies fear data leaks.

A fancy term for “don’t let hackers steal our stuff.”

Feeding your data pipeline a never-ending buffet.

Bridging the gap between development and IT operations.

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

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

Idea-vomiting buzzword dispenser.

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

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

Following data laws just enough to avoid fines.

Keeping data within borders—because governments say so.

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

Turning raw data into fancy charts that people ignore.

Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.

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

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

Your code, but only when someone remembers it exists.

A strategic delay tactic used to avoid commitment in meetings with more than three directors present.

Like moving houses, but with more downtime and crying.

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

When real-time isn’t worth the hassle.

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

The serial focus assassin. Everyone knows at least one.

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

Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.

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

Because someone needs to process transactions in real-time.

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

A minor data visualization tweak that gets presented as groundbreaking.

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

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

The thing everyone blames but nobody fixes.

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