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".
Granting permissions based on job roles, not personal favorites.
“We made a pretty chart—please pretend it changed your decision-making.”
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Grouping users to prove that trends aren’t just luck.
Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.
Proof that a company probably takes security seriously.
Finding insights in data—or just realizing what’s missing.
Following data laws just enough to avoid fines.
The secret sauce behind databases that actually perform.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
The easiest SQL query that someone still wants to call a "data-driven insight."
Corporate deity whose random breakfast thoughts outrank your entire research department.
When your company trends on Twitter for all the wrong reasons.
The alarm system for when hackers come knocking.
Rules about data that everyone agrees on but nobody follows.
The reason healthcare companies fear data leaks.
Data that refuses to fit into neat tables—think text, images, and the chaos of the internet.
Helping engineers understand how data flows, transforms, and actually works.
Sifting through data, hoping for something insightful.
Holding onto data just long enough to avoid legal trouble.
Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.
Running the same weekly report with slightly different date filters.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
Hoping two systems eventually agree on reality.
A last-minute meeting because someone didn’t read the dashboard.
Guessing with data—because flipping a coin isn't "data-driven."
When processing big data was still cool.
The dashboard everyone ignores until an executive asks for it.
The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.
Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.
“I haven’t looked at the data yet, but I will… eventually.”
Where your data goes to sleep.
Bridging the gap between development and IT operations.
Because sometimes, you actually want long-winded responses.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
The science of making sense of data—assuming it’s not lying to you.
What you just got assigned because you asked a question in the meeting.
Feeding your data pipeline a never-ending buffet.
Like a Data Lake, but with regret control.
When leadership changes the KPI goal after you’ve already built the report.
Doing more work with fewer complaints—on a good day.
Stripping away identities because privacy lawsuits are expensive.
When you pivot data just to confirm what you already knew.
“Yes, our data platform supports SQL. That’s not a selling point.”
When real-time isn’t worth the hassle.
A flowchart-like model that makes decisions—think "choose your own adventure" but with math.
Translating raw data into real-world meaning so it’s actually useful.
Checking if your security is solid—or just wishful thinking.
Because JSON wasn’t painful enough.
Where your data has commitment issues.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
When a relational database is too much effort.
Making database queries run faster—because no one likes waiting 10 minutes for an SQL query to finish.
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
When your system crashes but pretends it never happened.
Automating code merges so your team doesn’t go crazy.
Deploying apps without touching infrastructure (until something breaks).
The serial focus assassin. Everyone knows at least one.
Teaching machines to "think" so they can replace humans (but mostly just generate weird chatbot responses).
When you can’t commit to a single cloud provider.
Where structured data goes to drown.
Protecting user info while secretly monetizing it.
Google’s way of making your SQL queries cost a small fortune.
“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”
The art of making sure analysts don’t work with garbage.
Sharing resources and pretending everything is fine.
Shoving a half-baked feature into the project at the last minute.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
The dream every company sells but never actually delivers.
Trust no one, verify everything. Paranoia as a security strategy.
Checking your data before it embarrasses you.
Making pretty charts so people think the data makes sense.
A measure of how spread out your data is—basically, how weird or normal your numbers are.
The IT version of “Ctrl+Z” for disasters.
Sorting data into neat categories, only for users to ignore them.
Extract, transform, load—the classic data pipeline approach.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
Moving data to the cloud—hopefully without breaking everything.
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
Scrambling data so only the right people (hopefully) can read it.
Trying to convince non-technical people that data matters.
The family tree of your data, assuming you can track it.
Proof that "we'll fix it later" never actually means later.
Pay a monthly fee to lose your files in someone else’s basement.
Demands data-driven decisions then overrides everything because their morning shower had "different vibes."
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
Someone else’s computer, but shinier.
Because SQL SELECT wasn’t fancy enough.
Because bad data leads to bad decisions and lots of excuses.
Keeping data safe from hackers, leaks, and bad employees.
Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"
The reason your software updates faster than you can blink.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
Organizing data at a scale where things will go wrong.
Goes to every conference and is part of every newsletter. Needs an intervention.
Turning numbers into narratives people might actually remember.
Metadata management to keep track of your ever-growing data jungle.
Making sure your servers aren’t crying for no reason.
Europe’s way of reminding companies that data privacy matters.
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