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.
Corporate for "I forgot what this is about but I need to make noise before someone notices".
Stopping data leaks before they make headlines.
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
Because spreadsheets just don’t scale.
The behind-the-scenes details of how data was collected.
“I forgot to check the dashboard before this meeting.”
Because SQL SELECT wasn’t fancy enough.
The family tree of your data, assuming you can track it.
Extract, transform, load—the classic data pipeline approach.
Stalking customers, but make it “data-driven.”
The one dashboard we all agreed on… until someone else made a new one with different numbers.
Grouping users to prove that trends aren’t just luck.
The magic that makes your slow queries slightly less slow.
The badge that says “We take security seriously” (but still have breaches).
“Your data reports need to be better, but we won’t give you more resources.”
Sorting data into neat categories, only for users to ignore them.
Like moving houses, but with more downtime and crying.
“Here’s what you should do, but no one actually follows.”
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"
Pay a monthly fee to lose your files in someone else’s basement.
“This report is valid until next quarter, when everything changes.”
A fancy word for "number we use to see if our model sucks or not."
Holding onto data just long enough to avoid legal trouble.
The dashboard everyone ignores until an executive asks for it.
Goes to every conference and is part of every newsletter. Needs an intervention.
The magic behind neural networks—basically, trial and error on steroids until the model gets it right.
The theoretical version of your data that reality refuses to match.
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
Redefines success metrics faster than politicians backpedal after an election.
Turning numbers into narratives people might actually remember.
A free tool for tracking website traffic—until privacy laws step in.
When everyone agrees on what to pretend to care about.
Where your data goes to sleep.
Because raw data is just too ugly.
Making sure your app doesn’t make users want to throw their devices.
Stripping personal details so data looks anonymous (but isn’t always).
The difference between well-structured data and a digital black hole.
The secret sauce behind databases that actually perform.
When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.
Turning monolithic problems into distributed chaos.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
The dashboards and reports that will be outdated within a week.
The alarm system for when hackers come knocking.
Because well-managed data is the difference between insights and chaos.
Protecting user info while secretly monetizing it.
The chaos of switching from Excel to an actual BI tool.
Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.
Feeding your data pipeline a never-ending buffet.
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
Getting the most out of your budget before the CFO notices.
Saving progress so your system can crash at a later, more inconvenient time.
When leadership changes the KPI goal after you’ve already built the report.
When search meets machine learning and everyone gets confused.
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 science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
Poking around in your data to find trends, outliers, and problems before they ruin your model.
Because manually moving data is for people who hate themselves.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
Load first, transform later—modern data integration in action.
Shipping code faster than your team can fix bugs.
The key metrics leadership suddenly decided to care about this quarter.
A structured way to describe data relationships (or overcomplicate things).
Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.
The frustrations of explaining, again, why two reports don’t match.
When processing big data was still cool.
A statistical method that updates what you believe based on new data—just like changing your opinion after checking Yelp reviews.
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Metrics that executives obsess over (but don’t always understand).
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
Proof that a company probably takes security seriously.
A central place for data that everyone fights over.
Corporate deity whose random breakfast thoughts outrank your entire research department.
“I have 10 dashboards to fix and zero time for your ad-hoc request.”
The art of making sure analysts don’t work with garbage.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
The buzzword architects love, but engineers fear.
When real-time isn’t worth the hassle.
Splitting your database into smaller disasters.
Double-checking data before it makes a fool of you.
A fancy way of saying, “Re-use that old SQL query, but make it look fresh.”
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
Fixing data mistakes before they embarrass you.
Rules everyone agrees on but nobody follows.
Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
Because finding the right dataset shouldn’t feel like a scavenger hunt.
Keeping unauthorized users out - until someone shares a password.
The easiest SQL query that someone still wants to call a "data-driven insight."
Making teams promise they won’t break each other’s data pipelines.
“Yes, our data platform supports SQL. That’s not a selling point.”
A statistical way to check if two things are related or if your data is just messing with you.
When you want fast answers and minimal thinking.
Frankenstein’s monster made of expensive software.
The programming language everyone pretends to know.
Where your data has commitment issues.
Urban data dictionary powered by
