Corporate for "I forgot what this is about but I need to make noise before someone notices".
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.
A passive-aggressive way to say “this will be your problem soon.”
The secret sauce that makes data searchable, understandable, and actually useful.
Hoping two systems eventually agree on reality.
Cutting down the number of variables in your dataset—because sometimes, less is more (especially in Excel).
The constant struggle to keep data clean, secure, and useful.
The awkward silence between launch and someone actually using it.
All the missing data that everyone pretends doesn’t exist.
“Will this dashboard break when more than 5 people refresh it at once?”
Because winging it with data governance isn’t a long-term strategy.
Deciding where to spend time, money, and energy—usually wrong.
When leadership changes the KPI goal after you’ve already built the report.
Because just because you can collect data doesn’t mean you should.
Rules everyone agrees on but nobody follows.
Because bad data leads to bad decisions and lots of excuses.
Finding out where all the secrets are hiding before someone else does.
Organizing data at a scale where things will go wrong.
500 commits in 3 hours. No documentation and no survivors.
Europe’s way of reminding companies that data privacy matters.
The universal answer to every data question, forever and always.
Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.
Renting someone else’s servers but paying more.
The reason your database admin hates you.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
Doing more work with fewer complaints—on a good day.
The key metrics leadership suddenly decided to care about this quarter.
A fancy way of saying, “Re-use that old SQL query, but make it look fresh.”
“We’ll consider all possible factors… except the ones that make us look bad.”
Where structured data goes to drown.
Stripping away identities because privacy lawsuits are expensive.
A central place for data that everyone fights over.
Where your data goes to sleep.
Blueprints for security that companies try to follow.
Making database queries run faster—because no one likes waiting 10 minutes for an SQL query to finish.
Trying to convince non-technical people that data matters.
Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.
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
Because “I think this field means…” shouldn’t be part of data analysis.
Transforming categorical data into numerical form—because computers just don’t get words.
When you can’t commit to a single cloud provider.
Deploying apps without touching infrastructure (until something breaks).
The thing everyone builds but nobody documents.
The reason your reports make no sense.
The awkward middle child of structured and unstructured data.
When you pivot data just to confirm what you already knew.
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.
Keeping data within borders—because governments say so.
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
A fancy word for "number we use to see if our model sucks or not."
The magic that makes your slow queries slightly less slow.
Brings structure to chaos with dbt and a folder hierarchy that could win awards.
Because spreadsheets just don’t scale.
When you want fast answers and minimal thinking.
When two teams argue over whose data is right until they both give up.
The fight over who actually controls the data mess.
The art of making sure analysts don’t work with garbage.
Teaching models with labeled data—kind of like school, but for algorithms.
A structured way to work with large datasets.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
The bare minimum dressed up like a competitive edge.
Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.
The behind-the-scenes data that keeps everything (barely) organized.
“Yes, our data platform supports SQL. That’s not a selling point.”
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
Cutting back on data storage costs until everything runs painfully slow.
Turning monolithic problems into distributed chaos.
The theoretical version of your data that reality refuses to match.
Fake data used for training models when real data is too sensitive, messy, or non-existent.
The illusion of structure in your chaotic data world.
“This data connector technically works, but barely.”
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
A chaotic attempt to explain why the numbers don’t match across reports.
The IT version of “Ctrl+Z” for disasters.
Because finding the right dataset shouldn’t feel like a scavenger hunt.
Sifting through data, hoping for something insightful.
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
Guessing with data—because flipping a coin isn't "data-driven."
Keeping secrets… until someone forgets to lock the database.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
The Costco of structured data.
Helping engineers understand how data flows, transforms, and actually works.
“We made a pretty chart—please pretend it changed your decision-making.”
The law that keeps finance teams on their toes.
Slapping AI on the same old nonsense.
The mess left behind when shortcuts meet data analytics.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
Translating raw data into real-world meaning so it’s actually useful.
When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.
A job posting for a data analyst who can also engineer pipelines and train AI models.
Tracking data’s dramatic journey from birth to deletion
Making your inefficient queries slightly less embarrassing.
Checking if your security is solid—or just wishful thinking.
Extract, transform, load—the classic data pipeline approach.
Where we test new models and hope no one deploys them to production by accident.
Slicing and dicing data until it fits your argument.
The dream every company sells but never actually delivers.
A 57-slide PowerPoint where 3 slides actually contain useful charts.
Corporate deity whose random breakfast thoughts outrank your entire research department.
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