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".
The Costco of structured data.
When everyone agrees on what to pretend to care about.
Keeps the data stack humming so analysts can pretend it’s “just a quick query.”
The secret sauce behind databases that actually perform.
Turning monolithic problems into distributed chaos.
“I haven’t looked at the data yet, but I will… eventually.”
Renting someone else’s servers but paying more.
Keeping multiple copies of your data in sync.
Keeping data within borders—because governments say so.
That thing developers ignore until the database breaks.
Making sense of numbers so businesses can pretend to be data-driven.
The reason your software updates faster than you can blink.
The endless cycle of finding new ways to blame bad data for bad decisions.
Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.
Running the same weekly report with slightly different date filters.
Invisible data hero who's seen SQL horrors that would make junior devs cry.
The badge that says “We take security seriously” (but still have breaches).
The never-ending battle between hackers and IT teams running on coffee.
No one understands the report, but we’re pretending we do.
Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.
The secret sauce that makes data searchable, understandable, and actually useful.
“This report is valid until next quarter, when everything changes.”
Talking to inanimate objects because humans are worse.
Getting access to the full raw data without documentation or guidance.
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
Automating code merges so your team doesn’t go crazy.
Because raw data is just too ugly.
Double-checking data before it makes a fool of you.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
Fake data used for training models when real data is too sensitive, messy, or non-existent.
Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.
The behind-the-scenes details of how data was collected.
Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.
Turning raw data into fancy charts that people ignore.
The programming language everyone pretends to know.
“We need to filter this data in every way possible until it agrees with us.”
Redefines success metrics faster than politicians backpedal after an election.
Handpicking quality data like it’s fine wine.
How much pain your system can handle before collapsing.
500 commits in 3 hours. No documentation and no survivors.
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
Sifting through data, hoping for something insightful.
A digital breadcrumb trail for when things inevitably go wrong.
A structured way to work with large datasets.
Copying data from one mistake to another.
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
Getting the most out of your budget before the CFO notices.
Slicing and dicing data until it fits your argument.
Hacking yourself before someone else does.
“We need better numbers, but we don’t want to change anything.”
“Yes, our data platform supports SQL. That’s not a selling point.”
A structured way to describe data relationships (or overcomplicate things).
Cutting back on data storage costs until everything runs painfully slow.
Because finding the right dataset shouldn’t feel like a scavenger hunt.
“Your data reports need to be better, but we won’t give you more resources.”
The law that keeps finance teams on their toes.
The Data Lake’s evil twin.
Because manually checking your code is for the weak.
Microsoft’s favorite way to make bar charts look really dramatic.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
Like moving houses, but with more downtime and crying.
A strategic delay tactic used to avoid commitment in meetings with more than three directors present.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.
Making sure your app doesn’t make users want to throw their devices.
Making data look important in executive meetings.
Checking if your security is solid—or just wishful thinking.
A fancy word for "number we use to see if our model sucks or not."
Making sure data doesn’t become a dumpster fire.
Human API who communicates in endpoints and considers UIs a moral weakness.
Europe’s way of reminding companies that data privacy matters.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Because winging it with data governance isn’t a long-term strategy.
Granting permissions based on job roles, not personal favorites.
Making sure data stays trustworthy—or at least looks like it.
Shows up after work's done to sink regulatory fangs into your launch plans.
The theoretical version of your data that reality refuses to match.
Because JSON wasn’t painful enough.
When your data is so bloated no one knows what to do with it, but it sounds impressive.
The fight over who actually controls the data mess.
Where your data goes to sleep.
A fancy term for “don’t let hackers steal our stuff.”
The stuff hackers (and marketers) dream about stealing.
Keeping data safe from hackers, leaks, and bad employees.
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
Making complex queries expensive since forever.
Like a Data Lake, but with regret control.
Your code, but only when someone remembers it exists.
The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.
Corporate deity whose random breakfast thoughts outrank your entire research department.
Guessing with data—because flipping a coin isn't "data-driven."
The reason healthcare companies fear data leaks.
A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.
The family tree of your data, assuming you can track it.
Poking around in your data to find trends, outliers, and problems before they ruin your model.
Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.
“We ran the same SQL query but indexed a column, so now it’s 2% faster.”
Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.
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