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.
An interactive report that executives will ignore until they ask for the same data… in an Excel sheet.
The unlucky souls tasked with keeping data under control.
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
Cutting down the number of variables in your dataset—because sometimes, less is more (especially in Excel).
Making sure your servers aren’t crying for no reason.
Shows up after work's done to sink regulatory fangs into your launch plans.
The lazy friend of machine learning—it just looks at its closest neighbors and copies them.
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
Data’s glow-up into something actually useful.
Because “I have no idea where this data came from” is not a great answer.
The programming language everyone pretends to know.
The key metrics leadership suddenly decided to care about this quarter.
Because SQL SELECT wasn’t fancy enough.
Because someone needs to process transactions in real-time.
Guessing with data—because flipping a coin isn't "data-driven."
When real-time isn’t worth the hassle.
Trying to convince non-technical people that data matters.
Predicting all the ways data can ruin your day.
Moving data to the cloud—hopefully without breaking everything.
Digging through massive datasets, hoping to strike gold.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
For when the cloud is just too far away.
Like a Data Lake, but with regret control.
The alarm system for when hackers come knocking.
Because “I think this field means…” shouldn’t be part of data analysis.
The difference between well-structured data and a digital black hole.
The reason your database admin hates you.
Turning data into a fixed-size mess—useful for passwords, not so great if you ever need to reverse it.
The dashboards and reports that will be outdated within a week.
The thing everyone builds but nobody documents.
Data about your data—because keeping track of what your numbers mean is harder than it should be.
The never-ending battle between hackers and IT teams running on coffee.
“Let’s keep slicing the data until we find something that supports our assumption.”
A checklist of rules to follow… until regulations change again.
That thing you forgot to set up before the system crashed.
A minor data visualization tweak that gets presented as groundbreaking.
Corporate deity whose random breakfast thoughts outrank your entire research department.
Fixing data mistakes before they embarrass you.
Tracking data’s dramatic journey from birth to deletion
Keeping data safe from hackers, leaks, and bad employees.
When bad data leads to even worse decisions.
Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.
When search meets machine learning and everyone gets confused.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
The Costco of structured data.
When you can’t commit to a single cloud provider.
Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.
The buzzword architects love, but engineers fear.
A last-minute meeting because someone didn’t read the dashboard.
Making sense of numbers so businesses can pretend to be data-driven.
A/B testing’s overachieving cousin.
Invisible data hero who's seen SQL horrors that would make junior devs cry.
Poking around in your data to find trends, outliers, and problems before they ruin your model.
The bare minimum dressed up like a competitive edge.
Making sure data doesn’t become a dumpster fire.
The serial focus assassin. Everyone knows at least one.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
A central place for data that everyone fights over.
Because mistakes were made.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
Guards their "secret metric" like it's launch codes when it's really just page views in a trench coat.
Making teams promise they won’t break each other’s data pipelines.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
When two teams argue over whose data is right until they both give up.
“This data connector technically works, but barely.”
When processing big data was still cool.
Rules about data that everyone agrees on but nobody follows.
The law that keeps finance teams on their toes.
500 commits in 3 hours. No documentation and no survivors.
Goes to every conference and is part of every newsletter. Needs an intervention.
How much pain your system can handle before collapsing.
Stopping data leaks before they make headlines.
The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.
Granting permissions based on job roles, not personal favorites.
A passive-aggressive way to say “this will be your problem soon.”
The one dashboard we all agreed on… until someone else made a new one with different numbers.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
A 57-slide PowerPoint where 3 slides actually contain useful charts.
When your data is so bloated no one knows what to do with it, but it sounds impressive.
DIY data anarchist whose unholy Excel concoctions somehow hypnotize executives despite breaking every statistical law.
Data that refuses to fit into neat tables—think text, images, and the chaos of the internet.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Wants to monitor every client blink without a clue what to do with it.
Bridging the gap between development and IT operations.
The stuff hackers (and marketers) dream about stealing.
A fancy way of saying, “Re-use that old SQL query, but make it look fresh.”
Shoving a half-baked feature into the project at the last minute.
Running the same weekly report with slightly different date filters.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
Because raw data is just too ugly.
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.
When your system crashes but pretends it never happened.
The reason healthcare companies fear data leaks.
A measure of how spread out your data is—basically, how weird or normal your numbers are.
Checking your data before it embarrasses you.
Grouping users to prove that trends aren’t just luck.
“I forgot to check the dashboard before this meeting.”
A job posting for a data analyst who can also engineer pipelines and train AI models.
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
Ignoring that data quality issue until it causes real problems.
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