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.
Corporate for "I forgot what this is about but I need to make noise before someone notices".
Stalking customers, but make it “data-driven.”
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
The awkward middle child of structured and unstructured data.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
The lazy friend of machine learning—it just looks at its closest neighbors and copies them.
“Here’s what you should do, but no one actually follows.”
When everyone agrees on what to pretend to care about.
Splitting your database into smaller disasters.
SQL’s rebellious younger sibling.
Transforming categorical data into numerical form—because computers just don’t get words.
Moving data to the cloud—hopefully without breaking everything.
The delicate art of begging people to care.
Corporate deity whose random breakfast thoughts outrank your entire research department.
Cutting back on data storage costs until everything runs painfully slow.
Doing more work with fewer complaints—on a good day.
A measure of how spread out your data is—basically, how weird or normal your numbers are.
A fancy word for "number we use to see if our model sucks or not."
Predicting all the ways data can ruin your day.
Making sense of numbers so businesses can pretend to be data-driven.
Getting machines to do the boring stuff for you.
The reason your computer fan sounds like a jet engine.
Trust no one, verify everything. Paranoia as a security strategy.
“This data connector technically works, but barely.”
Keeping unauthorized users out - until someone shares a password.
Because winging it with data governance isn’t a long-term strategy.
Granting permissions based on job roles, not personal favorites.
“This dashboard is broken, but let’s not discuss it in front of leadership.”
Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.
Because well-managed data is the difference between insights and chaos.
Grouping users to prove that trends aren’t just luck.
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
Because spreadsheets just don’t scale.
When your system crashes but pretends it never happened.
Stripping away identities because privacy lawsuits are expensive.
Teaching models with labeled data—kind of like school, but for algorithms.
Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.
Stopping data leaks before they make headlines.
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
A fancy term for “don’t let hackers steal our stuff.”
The key metrics leadership suddenly decided to care about this quarter.
The legal hoops companies jump through to keep your data kinda safe.
Where we test new models and hope no one deploys them to production by accident.
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
When you can’t commit to a single cloud provider.
Running the same weekly report with slightly different date filters.
Pay a monthly fee to lose your files in someone else’s basement.
The thing everyone blames but nobody fixes.
Because not every department deserves full database access.
Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.
Where your data has commitment issues.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
Shipping code faster than your team can fix bugs.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
That thing you forgot to set up before the system crashed.
Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.
Following data laws just enough to avoid fines.
“We made a pretty chart—please pretend it changed your decision-making.”
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
Organizing data at a scale where things will go wrong.
The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.
Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.
Like conducting a symphony, but with way more screaming.
The dashboards and reports that will be outdated within a week.
Because raw data is just too ugly.
The terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.
Saving progress so your system can crash at a later, more inconvenient time.
A structured way to describe data relationships (or overcomplicate things).
The serial focus assassin. Everyone knows at least one.
“We need to filter this data in every way possible until it agrees with us.”
The Costco of structured data.
“Yes, our data platform supports SQL. That’s not a selling point.”
Teaching computers to recognize patterns so they can pretend to be smart—until they overfit and fail.
Because reading rows one at a time is for chumps.
Because sometimes, you actually want long-winded responses.
Keeping data within borders—because governments say so.
No one understands the report, but we’re pretending we do.
Human API who communicates in endpoints and considers UIs a moral weakness.
Because “I have no idea where this data came from” is not a great answer.
When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.
Hoping two systems eventually agree on reality.
The IT version of “Ctrl+Z” for disasters.
The alarm system for when hackers come knocking.
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 JSON wasn’t painful enough.
The fantasy of having the same data everywhere at the same time.
Turning monolithic problems into distributed chaos.
Extract, transform, load—the classic data pipeline approach.
500 commits in 3 hours. No documentation and no survivors.
Turning raw data into fancy charts that people ignore.
A data point that’s way off from the rest—could be an error, or could be the next big discovery.
Keeping track of all the ways hackers can ruin your day.
Like moving houses, but with more downtime and crying.
“We ran the same SQL query but indexed a column, so now it’s 2% faster.”
Protecting user info while secretly monetizing it.
The Data Lake’s evil twin.
Double-checking data before it makes a fool of you.
Making data look important in executive meetings.
Deploying apps without touching infrastructure (until something breaks).
Deciding where to spend time, money, and energy—usually wrong.
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