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.
Proof that a company probably takes security seriously.
Where your data goes to sleep.
The serial focus assassin. Everyone knows at least one.
Slapping AI on the same old nonsense.
“Here’s what you should do, but no one actually follows.”
Organizing data at a scale where things will go wrong.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
The easiest SQL query that someone still wants to call a "data-driven insight."
The thing everyone blames but nobody fixes.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
Invisible data hero who's seen SQL horrors that would make junior devs cry.
Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.
A table that tells you how often your model gets things right (or, more realistically, how often it screws up).
A gradient boosting algorithm that wins Kaggle competitions—because sometimes brute force just works.
Handpicking quality data like it’s fine wine.
Frankenstein’s monster made of expensive software.
Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.
The never-ending battle between hackers and IT teams running on coffee.
Helping engineers understand how data flows, transforms, and actually works.
The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
Because manually checking your code is for the weak.
A fancy term for “don’t let hackers steal our stuff.”
The art of making sure analysts don’t work with garbage.
The law that keeps finance teams on their toes.
Bridging the gap between development and IT operations.
Because JSON wasn’t painful enough.
Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.
The fine art of deciding who gets in and who gets a "403 Forbidden."
The Costco of structured data.
The stuff hackers (and marketers) dream about stealing.
Human API who communicates in endpoints and considers UIs a moral weakness.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
Making sure data doesn’t become a dumpster fire.
Deciding where to spend time, money, and energy—usually wrong.
Turning numbers into narratives people might actually remember.
A structured way to describe data relationships (or overcomplicate things).
The underappreciated hero who turns messy data into charts and makes everyone else look good.
Grouping users to prove that trends aren’t just luck.
The fight over who actually controls the data mess.
A free tool for tracking website traffic—until privacy laws step in.
That thing developers ignore until the database breaks.
A statistical way to check if two things are related or if your data is just messing with you.
XML’s cooler, slightly less annoying cousin.
The IT version of “Ctrl+Z” for disasters.
A digital breadcrumb trail for when things inevitably go wrong.
A marketing term for "we kinda fixed the Data Lake problem."
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Shoving a half-baked feature into the project at the last minute.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
The science of making sense of data—assuming it’s not lying to you.
A minor data visualization tweak that gets presented as groundbreaking.
Because raw data is just too ugly.
Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"
The secret sauce behind databases that actually perform.
The fantasy of having the same data everywhere at the same time.
Automating code merges so your team doesn’t go crazy.
That thing you forgot to set up before the system crashed.
The magic behind neural networks—basically, trial and error on steroids until the model gets it right.
Teaching computers to recognize patterns so they can pretend to be smart—until they overfit and fail.
A structured way to work with large datasets.
The legal hoops companies jump through to keep your data kinda safe.
Creates JIRA tickets to track their JIRA tickets while drowning in chaos.
Transforming categorical data into numerical form—because computers just don’t get words.
A checklist of rules to follow… until regulations change again.
Running the same weekly report with slightly different date filters.
Making teams promise they won’t break each other’s data pipelines.
Saving progress so your system can crash at a later, more inconvenient time.
Making sure your servers aren’t crying for no reason.
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
Where we test new models and hope no one deploys them to production by accident.
The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.
“This data connector technically works, but barely.”
Where your data has commitment issues.
Because not every department deserves full database access.
The dashboard everyone ignores until an executive asks for it.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
The go-to event for data professionals who want to rethink how governance is done. Join experts reimagining the future of what AI-readiness looks like
Where structured data goes to drown.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
Granting permissions based on job roles, not personal favorites.
A fancy word for "number we use to see if our model sucks or not."
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.
Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
Sorting data into neat categories, only for users to ignore them.
Because “whatever naming convention feels right” is not a strategy.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
Trust no one, verify everything. Paranoia as a security strategy.
The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.
“I forgot to check the dashboard before this meeting.”
Demands data-driven decisions then overrides everything because their morning shower had "different vibes."
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
“We’ll consider all possible factors… except the ones that make us look bad.”
“Let’s keep slicing the data until we find something that supports our assumption.”
Preparing for disasters that will still somehow surprise you.
“I haven’t looked at the data yet, but I will… eventually.”
Fixing data mistakes before they embarrass you.
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