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.
Corporate for "I forgot what this is about but I need to make noise before someone notices".
A checklist of rules to follow… until regulations change again.
A data point that’s way off from the rest—could be an error, or could be the next big discovery.
Copying data from one mistake to another.
The dashboard everyone ignores until an executive asks for it.
The key metrics leadership suddenly decided to care about this quarter.
Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?
Proof that a company probably takes security seriously.
Making your inefficient queries slightly less embarrassing.
Making sure data doesn’t become a dumpster fire.
When your system crashes but pretends it never happened.
The art of making sure analysts don’t work with garbage.
The magic behind neural networks—basically, trial and error on steroids until the model gets it right.
“Your data reports need to be better, but we won’t give you more resources.”
Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.
Feeding your data pipeline a never-ending buffet.
The mess left behind when shortcuts meet data analytics.
Getting machines to do the boring stuff for you.
The awkward silence between launch and someone actually using it.
When everyone agrees on what to pretend to care about.
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
Where your data goes to sleep.
Granting permissions based on job roles, not personal favorites.
Fancy PowerPoint slides no one follows.
Making teams promise they won’t break each other’s data pipelines.
The buzzword architects love, but engineers fear.
“Throw some data models at the wall and see what sticks.”
The never-ending battle between hackers and IT teams running on coffee.
Turning monolithic problems into distributed chaos.
A chaotic attempt to explain why the numbers don’t match across reports.
Following data laws just enough to avoid fines.
The universal answer to every data question, forever and always.
The bare minimum dressed up like a competitive edge.
A statistical way to check if two things are related or if your data is just messing with you.
A digital breadcrumb trail for when things inevitably go wrong.
Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.
Deciding where to spend time, money, and energy—usually wrong.
A free tool for tracking website traffic—until privacy laws step in.
Keeping data within borders—because governments say so.
The badge that says “We take security seriously” (but still have breaches).
Saving progress so your system can crash at a later, more inconvenient time.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
Your code, but only when someone remembers it exists.
Trust no one, verify everything. Paranoia as a security strategy.
Deploying apps without touching infrastructure (until something breaks).
Guessing with data—because flipping a coin isn't "data-driven."
Transforming categorical data into numerical form—because computers just don’t get words.
Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.
“We’ll consider all possible factors… except the ones that make us look bad.”
“This report is valid until next quarter, when everything changes.”
Guards their "secret metric" like it's launch codes when it's really just page views in a trench coat.
Like conducting a symphony, but with way more screaming.
The awkward middle child of structured and unstructured data.
Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.
Because just because you can collect data doesn’t mean you should.
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
The chaos of switching from Excel to an actual BI tool.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.
The thing everyone blames but nobody fixes.
The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.
The law that keeps finance teams on their toes.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
Human API who communicates in endpoints and considers UIs a moral weakness.
Brings structure to chaos with dbt and a folder hierarchy that could win awards.
The reason your reports make no sense.
Because spreadsheets just don’t scale.
Keeps the data stack humming so analysts can pretend it’s “just a quick query.”
Making database queries run faster—because no one likes waiting 10 minutes for an SQL query to finish.
Running the same weekly report with slightly different date filters.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
“We made a pretty chart—please pretend it changed your decision-making.”
This query better finish before the meeting, or I’m in trouble.
Stopping data leaks before they make headlines.
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
A 57-slide PowerPoint where 3 slides actually contain useful charts.
When you want fast answers and minimal thinking.
Making sure your app doesn’t make users want to throw their devices.
Frankenstein’s monster made of expensive software.
The serial focus assassin. Everyone knows at least one.
A measure of how spread out your data is—basically, how weird or normal your numbers are.
A/B testing’s overachieving cousin.
The legal hoops companies jump through to keep your data kinda safe.
Invisible data hero who's seen SQL horrors that would make junior devs cry.
Tweaking the settings of your machine learning model—kind of like adjusting the seasoning in a bad recipe.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
“Here’s what you should do, but no one actually follows.”
Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.
When processing big data was still cool.
The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.
Keeping unauthorized users out - until someone shares a password.
When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.
“Yes, our data platform supports SQL. That’s not a selling point.”
The IT version of “Ctrl+Z” for disasters.
When two teams argue over whose data is right until they both give up.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
Making data look important in executive meetings.
“We need better numbers, but we don’t want to change anything.”
Where structured data goes to drown.
Teaching machines to "think" so they can replace humans (but mostly just generate weird chatbot responses).
A last-minute meeting because someone didn’t read the dashboard.
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