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.
“We’ll consider all possible factors… except the ones that make us look bad.”
Making sure standard data values stay standard—good luck with that.
The family tree of your data, assuming you can track it.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.
Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.
Spotting the weirdos in your data—because outliers can mean fraud, errors, or just bad luck.
A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.
The law that keeps finance teams on their toes.
Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.
Fine-tunes LLMs like they’re sourdough starters. Has five GPU credits left and no intention of using them responsibly.
A data point that’s way off from the rest—could be an error, or could be the next big discovery.
Getting the most out of your budget before the CFO notices.
A passive-aggressive way to say “this will be your problem soon.”
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
The legal hoops companies jump through to keep your data kinda safe.
Demands data-driven decisions then overrides everything because their morning shower had "different vibes."
The magic that makes your slow queries slightly less slow.
Renting someone else’s servers but paying more.
Wants to monitor every client blink without a clue what to do with it.
Teaching computers to recognize patterns so they can pretend to be smart—until they overfit and fail.
500 commits in 3 hours. No documentation and no survivors.
“Your data reports need to be better, but we won’t give you more resources.”
Redefines success metrics faster than politicians backpedal after an election.
Collecting data the unethical-but-effective way.
A last-minute meeting because someone didn’t read the dashboard.
A structured way to describe data relationships (or overcomplicate things).
Preparing for disasters that will still somehow surprise you.
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
Splitting your database into smaller disasters.
Hoping two systems eventually agree on reality.
A marketing term for "we kinda fixed the Data Lake problem."
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
A chaotic attempt to explain why the numbers don’t match across reports.
A statistical way to check if two things are related or if your data is just messing with you.
A structured way to work with large datasets.
Where we test new models and hope no one deploys them to production by accident.
The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.
The awkward silence between launch and someone actually using it.
Turning monolithic problems into distributed chaos.
A job posting for a data analyst who can also engineer pipelines and train AI models.
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
Bridging the gap between development and IT operations.
The science of making sense of data—assuming it’s not lying to you.
The behind-the-scenes data that keeps everything (barely) organized.
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
Moving data to the cloud—hopefully without breaking everything.
Fluent in stakeholder management, and can turn vague requests into scarily accurate dashboards. Built half the team's workflows on vibes and somehow made it work.
Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?
Hiding sensitive data so developers don’t see what they shouldn’t.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
A minor data visualization tweak that gets presented as groundbreaking.
The one number we stare at while ignoring the iceberg.
This query better finish before the meeting, or I’m in trouble.
Stripping personal details so data looks anonymous (but isn’t always).
The universal answer to every data question, forever and always.
The reason your software updates faster than you can blink.
Like conducting a symphony, but with way more screaming.
The chaos of switching from Excel to an actual BI tool.
The reason your computer fan sounds like a jet engine.
Because finding the right dataset shouldn’t feel like a scavenger hunt.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
Getting machines to do the boring stuff for you.
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
Metadata management to keep track of your ever-growing data jungle.
Idea-vomiting buzzword dispenser.
When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.
Proof that "we'll fix it later" never actually means later.
Where your data goes to sleep.
Predicting all the ways data can ruin your day.
Frankenstein’s monster made of expensive software.
Because mistakes were made.
Deploying apps without touching infrastructure (until something breaks).
Guessing with data—because flipping a coin isn't "data-driven."
The difference between well-structured data and a digital black hole.
A/B testing’s overachieving cousin.
The one dashboard we all agreed on… until someone else made a new one with different numbers.
The secret sauce that makes data searchable, understandable, and actually useful.
Deciding where to spend time, money, and energy—usually wrong.
The underappreciated hero who turns messy data into charts and makes everyone else look good.
A digital breadcrumb trail for when things inevitably go wrong.
Slapping AI on the same old nonsense.
Metrics that executives obsess over (but don’t always understand).
The awkward middle child of structured and unstructured data.
The secret sauce behind databases that actually perform.
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
The unlucky souls tasked with keeping data under control.
Keeping data within borders—because governments say so.
Because SQL SELECT wasn’t fancy enough.
Data that refuses to fit into neat tables—think text, images, and the chaos of the internet.
“This data connector technically works, but barely.”
When a relational database is too much effort.
Keeping track of all the ways hackers can ruin your day.
The terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.
Goes to every conference and is part of every newsletter. Needs an intervention.
The alarm system for when hackers come knocking.
The fight over who actually controls the data mess.
Treats your dashboards like a digital coloring book.
The reason your reports make no sense.
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