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".
Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.
Finding insights in data—or just realizing what’s missing.
When your data is so bloated no one knows what to do with it, but it sounds impressive.
Getting machines to do the boring stuff for you.
Making teams promise they won’t break each other’s data pipelines.
“This data connector technically works, but barely.”
“This report is valid until next quarter, when everything changes.”
All the missing data that everyone pretends doesn’t exist.
Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.
A fancy term for “don’t let hackers steal our stuff.”
The underappreciated hero who turns messy data into charts and makes everyone else look good.
The badge that says “We take security seriously” (but still have breaches).
Running the same weekly report with slightly different date filters.
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"
Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.
Like a Data Lake, but with regret control.
Moving data to the cloud—hopefully without breaking everything.
A last-minute meeting because someone didn’t read the dashboard.
Renting someone else’s servers but paying more.
The Data Lake’s evil twin.
Proof that a company probably takes security seriously.
The thing everyone blames but nobody fixes.
Hoping two systems eventually agree on reality.
Brings structure to chaos with dbt and a folder hierarchy that could win awards.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
The mess left behind when shortcuts meet data analytics.
The one dashboard we all agreed on… until someone else made a new one with different numbers.
A minor data visualization tweak that gets presented as groundbreaking.
How much pain your system can handle before collapsing.
Grouping users to prove that trends aren’t just luck.
Double-checking data before it makes a fool of you.
A measure of how spread out your data is—basically, how weird or normal your numbers are.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
Copying data from one mistake to another.
Data’s glow-up into something actually useful.
The IT version of “Ctrl+Z” for disasters.
Trust no one, verify everything. Paranoia as a security strategy.
Because bad data leads to bad decisions and lots of excuses.
Holding onto data just long enough to avoid legal trouble.
Stalking customers, but make it “data-driven.”
Because “I have no idea where this data came from” is not a great answer.
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.
The reason your reports make no sense.
Because sometimes, you actually want long-winded responses.
Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.
Because “whatever naming convention feels right” is not a strategy.
Turning data into a fixed-size mess—useful for passwords, not so great if you ever need to reverse it.
Checking if your security is solid—or just wishful thinking.
The fight over who actually controls the data mess.
Digging through massive datasets, hoping to strike gold.
Keeping unauthorized users out - until someone shares a password.
Invisible data hero who's seen SQL horrors that would make junior devs cry.
Fixing data mistakes before they embarrass you.
Because reading rows one at a time is for chumps.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
What you just got assigned because you asked a question in the meeting.
When processing big data was still cool.
Teaching models with labeled data—kind of like school, but for algorithms.
Bridging the gap between development and IT operations.
A central place for data that everyone fights over.
Tracking data’s dramatic journey from birth to deletion
The magic behind neural networks—basically, trial and error on steroids until the model gets it right.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
Idea-vomiting buzzword dispenser.
The fine art of deciding who gets in and who gets a "403 Forbidden."
The fantasy of having the same data everywhere at the same time.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
The programming language everyone pretends to know.
The legal hoops companies jump through to keep your data kinda safe.
“Let’s keep slicing the data until we find something that supports our assumption.”
The behind-the-scenes details of how data was collected.
Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?
The universal answer to every data question, forever and always.
Like conducting a symphony, but with way more screaming.
Data about your data—because keeping track of what your numbers mean is harder than it should be.
Making database queries run faster—because no one likes waiting 10 minutes for an SQL query to finish.
A free tool for tracking website traffic—until privacy laws step in.
Ignoring that data quality issue until it causes real problems.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
Feeding your data pipeline a never-ending buffet.
Finding out where all the secrets are hiding before someone else does.
Load first, transform later—modern data integration in action.
Making data look important in executive meetings.
When bad data leads to even worse decisions.
When real-time isn’t worth the hassle.
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.
Your code, but only when someone remembers it exists.
The awkward middle child of structured and unstructured data.
Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.
The unlucky souls tasked with keeping data under control.
The frustrations of explaining, again, why two reports don’t match.
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.
Deciding where to spend time, money, and energy—usually wrong.
Blueprints for security that companies try to follow.
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