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".
Technologically frozen in 1995. Still thinks "the cloud" is for rain and refuses to click anything newer than Solitaire.
The universal answer to every data question, forever and always.
The Costco of structured data.
When you want fast answers and minimal thinking.
Keeping unauthorized users out - until someone shares a password.
When your company trends on Twitter for all the wrong reasons.
The family tree of your data, assuming you can track it.
Rules about data that everyone agrees on but nobody follows.
Checking your data before it embarrasses you.
Following data laws just enough to avoid fines.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.
The magic that makes your slow queries slightly less slow.
The awkward middle child of structured and unstructured data.
Sifting through data, hoping for something insightful.
“Your data reports need to be better, but we won’t give you more resources.”
Hoping two systems eventually agree on reality.
Like conducting a symphony, but with way more screaming.
Handpicking quality data like it’s fine wine.
Stalking customers, but make it “data-driven.”
How much pain your system can handle before collapsing.
“Yes, our data platform supports SQL. That’s not a selling point.”
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
The reason your database admin hates you.
Double-checking data before it makes a fool of you.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
Fake data used for training models when real data is too sensitive, messy, or non-existent.
The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
Hacking yourself before someone else does.
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
Because raw data is just too ugly.
Collecting data the unethical-but-effective way.
The never-ending battle between hackers and IT teams running on coffee.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
A table that tells you how often your model gets things right (or, more realistically, how often it screws up).
Because bad data leads to bad decisions and lots of excuses.
Granting permissions based on job roles, not personal favorites.
The underappreciated hero who turns messy data into charts and makes everyone else look good.
The unlucky souls tasked with keeping data under control.
The fine art of deciding who gets in and who gets a "403 Forbidden."
All the missing data that everyone pretends doesn’t exist.
Making sense of numbers so businesses can pretend to be data-driven.
Treats your dashboards like a digital coloring book.
The terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
A fancy term for “don’t let hackers steal our stuff.”
The one number we stare at while ignoring the iceberg.
Because manually checking your code is for the weak.
A free tool for tracking website traffic—until privacy laws step in.
“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”
A marketing term for "we kinda fixed the Data Lake problem."
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
“This data connector technically works, but barely.”
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
“We made a pretty chart—please pretend it changed your decision-making.”
Because mistakes were made.
Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.
Where we test new models and hope no one deploys them to production by accident.
No one understands the report, but we’re pretending we do.
The behind-the-scenes data that keeps everything (barely) organized.
Slapping AI on the same old nonsense.
Because spreadsheets just don’t scale.
Guessing with data—because flipping a coin isn't "data-driven."
Because manually moving data is for people who hate themselves.
The delicate art of begging people to care.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
The one dashboard we all agreed on… until someone else made a new one with different numbers.
Because JSON wasn’t painful enough.
Keeping multiple copies of your data in sync.
A last-minute meeting because someone didn’t read the dashboard.
The dashboard everyone ignores until an executive asks for it.
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
The secret sauce that makes data searchable, understandable, and actually useful.
“We need better numbers, but we don’t want to change anything.”
Because reading rows one at a time is for chumps.
Checking if your security is solid—or just wishful thinking.
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
Making sure your app doesn’t make users want to throw their devices.
Turning numbers into narratives people might actually remember.
Data about your data—because keeping track of what your numbers mean is harder than it should be.
A/B testing’s overachieving cousin.
The reason healthcare companies fear data leaks.
The bare minimum dressed up like a competitive edge.
The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.
Because “I think this field means…” shouldn’t be part of data analysis.
Fancy PowerPoint slides no one follows.
Sorting data into neat categories, only for users to ignore them.
Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
“I forgot to check the dashboard before this meeting.”
That thing developers ignore until the database breaks.
When one team gets credit for your analysis, and you get nothing.
Creates JIRA tickets to track their JIRA tickets while drowning in chaos.
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.
When bad data leads to even worse decisions.
A checklist of rules to follow… until regulations change again.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Because “I have no idea where this data came from” is not a great answer.
Getting access to the full raw data without documentation or guidance.
Digging through massive datasets, hoping to strike gold.
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