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.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
The easiest SQL query that someone still wants to call a "data-driven insight."
Feeding your data pipeline a never-ending buffet.
Because SQL SELECT wasn’t fancy enough.
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
Organizing data at a scale where things will go wrong.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
Rules everyone agrees on but nobody follows.
Someone else’s computer, but shinier.
This query better finish before the meeting, or I’m in trouble.
Sifting through data, hoping for something insightful.
Tweaking a button color and calling it "strategy."
Handpicking quality data like it’s fine wine.
No one understands the report, but we’re pretending we do.
“Here’s what you should do, but no one actually follows.”
Holding onto data just long enough to avoid legal trouble.
“We made a pretty chart—please pretend it changed your decision-making.”
“This report is valid until next quarter, when everything changes.”
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
Metrics that executives obsess over (but don’t always understand).
The key metrics leadership suddenly decided to care about this quarter.
Trust no one, verify everything. Paranoia as a security strategy.
Bridging the gap between development and IT operations.
Because spreadsheets just don’t scale.
The law that keeps finance teams on their toes.
The awkward middle child of structured and unstructured data.
The art of making sure analysts don’t work with garbage.
The unlucky souls tasked with keeping data under control.
When your system crashes but pretends it never happened.
When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.
The go-to event for data professionals who want to rethink how governance is done. Join experts reimagining the future of what AI-readiness looks like
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
Like a Data Lake, but with regret control.
The badge that says “We take security seriously” (but still have breaches).
Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.
When a relational database is too much effort.
A marketing term for "we kinda fixed the Data Lake problem."
The delicate art of begging people to care.
All the missing data that everyone pretends doesn’t exist.
Finding insights in data—or just realizing what’s missing.
Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.
Saving progress so your system can crash at a later, more inconvenient time.
Data’s glow-up into something actually useful.
Doing more work with fewer complaints—on a good day.
When two teams argue over whose data is right until they both give up.
Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.
That thing developers ignore until the database breaks.
A strategic delay tactic used to avoid commitment in meetings with more than three directors present.
Blueprints for security that companies try to follow.
The secret sauce that makes data searchable, understandable, and actually useful.
Fake data used for training models when real data is too sensitive, messy, or non-existent.
Helping engineers understand how data flows, transforms, and actually works.
The awkward silence between launch and someone actually using it.
“We ran the same SQL query but indexed a column, so now it’s 2% faster.”
The dashboards and reports that will be outdated within a week.
The reason your software updates faster than you can blink.
The behind-the-scenes details of how data was collected.
Stalking customers, but make it “data-driven.”
“I forgot to check the dashboard before this meeting.”
Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"
The stuff hackers (and marketers) dream about stealing.
Workflow automation, so you don’t have to babysit data pipelines.
Making sense of numbers so businesses can pretend to be data-driven.
The behind-the-scenes data that keeps everything (barely) organized.
Ignoring that data quality issue until it causes real problems.
Idea-vomiting buzzword dispenser.
Just because two things happen together doesn’t mean one caused the other. Like, eating more cheese doesn’t actually make you better at math.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
Cutting down the number of variables in your dataset—because sometimes, less is more (especially in Excel).
The reason your computer fan sounds like a jet engine.
Where your data has commitment issues.
When you want fast answers and minimal thinking.
A statistical method that updates what you believe based on new data—just like changing your opinion after checking Yelp reviews.
The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
Shoving a half-baked feature into the project at the last minute.
Tracking data’s dramatic journey from birth to deletion
Invisible data hero who's seen SQL horrors that would make junior devs cry.
Treats your dashboards like a digital coloring book.
Redefines success metrics faster than politicians backpedal after an election.
Keeping unauthorized users out - until someone shares a password.
Following data laws just enough to avoid fines.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
The family tree of your data, assuming you can track it.
Because manually moving data is for people who hate themselves.
When processing big data was still cool.
Getting the most out of your budget before the CFO notices.
Splitting your database into smaller disasters.
Because “whatever naming convention feels right” is not a strategy.
Where structured data goes to drown.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
“Let’s keep slicing the data until we find something that supports our assumption.”
The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.
Because someone needs to process transactions in real-time.
When bad data leads to even worse decisions.
The one dashboard we all agreed on… until someone else made a new one with different numbers.
The alarm system for when hackers come knocking.
The frustrations of explaining, again, why two reports don’t match.
Guessing with data—because flipping a coin isn't "data-driven."
The difference between well-structured data and a digital black hole.
Because mistakes were made.
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