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.
“This report is valid until next quarter, when everything changes.”
Finding insights in data—or just realizing what’s missing.
Stripping personal details so data looks anonymous (but isn’t always).
Because not every department deserves full database access.
The IT version of “Ctrl+Z” for disasters.
A structured way to work with large datasets.
The Data Lake’s evil twin.
Invisible data hero who's seen SQL horrors that would make junior devs cry.
The fine art of deciding who gets in and who gets a "403 Forbidden."
“We ran the same SQL query but indexed a column, so now it’s 2% faster.”
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
Making sure data stays trustworthy—or at least looks like it.
Preparing for disasters that will still somehow surprise you.
Data’s glow-up into something actually useful.
Workflow automation, so you don’t have to babysit data pipelines.
Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
The secret sauce behind databases that actually perform.
Like a Data Lake, but with regret control.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
A fancy term for “don’t let hackers steal our stuff.”
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
The chaos of switching from Excel to an actual BI tool.
Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.
Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.
The dashboard everyone ignores until an executive asks for it.
Trust no one, verify everything. Paranoia as a security strategy.
Protecting user info while secretly monetizing it.
This query better finish before the meeting, or I’m in trouble.
“We made a pretty chart—please pretend it changed your decision-making.”
A last-minute meeting because someone didn’t read the dashboard.
“We’ll consider all possible factors… except the ones that make us look bad.”
Feeding your data pipeline a never-ending buffet.
Keeps the data stack humming so analysts can pretend it’s “just a quick query.”
Getting access to the full raw data without documentation or guidance.
The behind-the-scenes details of how data was collected.
Making sense of numbers so businesses can pretend to be data-driven.
Moving data from one mess to another.
Pay a monthly fee to lose your files in someone else’s basement.
How much pain your system can handle before collapsing.
“We need better numbers, but we don’t want to change anything.”
The behind-the-scenes data that keeps everything (barely) organized.
When bad data leads to even worse decisions.
Rules everyone agrees on but nobody follows.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
Microsoft’s favorite way to make bar charts look really dramatic.
Tweaking the settings of your machine learning model—kind of like adjusting the seasoning in a bad recipe.
Making complex queries expensive since forever.
Lives in a command line, thrives in mayhem. Breaks things just to make them better. Somehow delivers magic at 2 AM.
Slicing and dicing data until it fits your argument.
“This dashboard is broken, but let’s not discuss it in front of leadership.”
When your data is so bloated no one knows what to do with it, but it sounds impressive.
A statistical method that updates what you believe based on new data—just like changing your opinion after checking Yelp reviews.
The awkward silence between launch and someone actually using it.
Getting machines to do the boring stuff for you.
A minor data visualization tweak that gets presented as groundbreaking.
A marketing term for "we kinda fixed the Data Lake problem."
The programming language everyone pretends to know.
Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.
Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
Splitting your database into smaller disasters.
Human API who communicates in endpoints and considers UIs a moral weakness.
Wants to monitor every client blink without a clue what to do with it.
The reason your software updates faster than you can blink.
Because “I think this field means…” shouldn’t be part of data analysis.
Automating code merges so your team doesn’t go crazy.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
The lazy friend of machine learning—it just looks at its closest neighbors and copies them.
“Throw some data models at the wall and see what sticks.”
Because just because you can collect data doesn’t mean you should.
Collecting data the unethical-but-effective way.
The difference between well-structured data and a digital black hole.
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
Turning data into a fixed-size mess—useful for passwords, not so great if you ever need to reverse it.
Creates JIRA tickets to track their JIRA tickets while drowning in chaos.
Deploying apps without touching infrastructure (until something breaks).
Absolute chaos agents.
Grouping users to prove that trends aren’t just luck.
“I have 10 dashboards to fix and zero time for your ad-hoc request.”
Renting someone else’s servers but paying more.
A checklist of rules to follow… until regulations change again.
The theoretical version of your data that reality refuses to match.
Because someone needs to process transactions in real-time.
Where we test new models and hope no one deploys them to production by accident.
Treats your dashboards like a digital coloring book.
“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
Rules about data that everyone agrees on but nobody follows.
When you want fast answers and minimal thinking.
SQL’s rebellious younger sibling.
All the missing data that everyone pretends doesn’t exist.
Cutting back on data storage costs until everything runs painfully slow.
Frankenstein’s monster made of expensive software.
When you pivot data just to confirm what you already knew.
Because JSON wasn’t painful enough.
Making sure your app doesn’t make users want to throw their devices.
The magic that makes your slow queries slightly less slow.
The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.
Because manually moving data is for people who hate themselves.
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