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.

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

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

Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.

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

The awkward middle child of structured and unstructured data.

When bad data leads to even worse decisions.

The magic that makes your slow queries slightly less slow.

Making sense of numbers so businesses can pretend to be data-driven.

Making complex queries expensive since forever.

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

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

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

Tracking data’s dramatic journey from birth to deletion

Hacking yourself before someone else does.

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

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

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

Hoping two systems eventually agree on reality.

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

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

Checking your data before it embarrasses you.

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

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

Rules about data that everyone agrees on but nobody follows.

Frankenstein’s monster made of expensive software.

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

A central place for data that everyone fights over.

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

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

Collecting data the unethical-but-effective way.

Because manually checking your code is for the weak.

The thing everyone blames but nobody fixes.

Brings structure to chaos with dbt and a folder hierarchy that could win awards.

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

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

Preparing for disasters that will still somehow surprise you.

Slicing and dicing data until it fits your argument.

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

Predicting all the ways data can ruin your day.

Ignoring that data quality issue until it causes real problems.

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

The thing everyone builds but nobody documents.

Blueprints for security that companies try to follow.

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

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

Keeping data within borders—because governments say so.

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

Granting permissions based on job roles, not personal favorites.

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

Keeping secrets… until someone forgets to lock the database.

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

The underappreciated hero who turns messy data into charts and makes everyone else look good.

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

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

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

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

Keeping multiple copies of your data in sync.

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

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

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

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

Getting machines to do the boring stuff for you.

Feeding your data pipeline a never-ending buffet.

Load first, transform later—modern data integration in action.

Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.

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

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

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

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

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

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

Spotting the weirdos in your data—because outliers can mean fraud, errors, or just bad luck.

When everyone agrees on what to pretend to care about.

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

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

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

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

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

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

The fight over who actually controls the data mess.

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

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

The alarm system for when hackers come knocking.

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

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

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

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

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

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.

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

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

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

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

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

Your code, but only when someone remembers it exists.

XML’s cooler, slightly less annoying cousin.

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

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

Organizing data at a scale where things will go wrong.