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.

Corporate for "I forgot what this is about but I need to make noise before someone notices".

The thing everyone builds but nobody documents.

Like moving houses, but with more downtime and crying.

Doing more work with fewer complaints—on a good day.

Checking if your security is solid—or just wishful thinking.

Cutting back on data storage costs until everything runs painfully slow.

Granting permissions based on job roles, not personal favorites.

Because bad data leads to bad decisions and lots of excuses.

A gradient boosting algorithm that wins Kaggle competitions—because sometimes brute force just works.

The science of making sense of data—assuming it’s not lying to you.

The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.

Fine-tunes LLMs like they’re sourdough starters. Has five GPU credits left and no intention of using them responsibly.

A fancy way of saying, “Re-use that old SQL query, but make it look fresh.”

When everyone agrees on what to pretend to care about.

Convincing everyone that my version of the dashboard is the truth.

Goes to every conference and is part of every newsletter. Needs an intervention.

“Will this dashboard break when more than 5 people refresh it at once?”

Keeping data safe from hackers, leaks, and bad employees.

Slicing and dicing data until it fits your argument.

Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.

“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”

The thing everyone blames but nobody fixes.

Sharing resources and pretending everything is fine.

Guessing with data—because flipping a coin isn't "data-driven."

Grouping users to prove that trends aren’t just luck.

The legal hoops companies jump through to keep your data kinda safe.

The secret sauce behind databases that actually perform.

The alarm system for when hackers come knocking.

Like conducting a symphony, but with way more screaming.

A data point that’s way off from the rest—could be an error, or could be the next big discovery.

The never-ending battle between hackers and IT teams running on coffee.

The bare minimum dressed up like a competitive edge.

Keeps every dataset like it’s a family heirloom but can’t explain where it came from or what it’s for.

Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.

Keeping data within borders—because governments say so.

Splitting your database into smaller disasters.

The fine art of deciding who gets in and who gets a "403 Forbidden."

Tracking data’s dramatic journey from birth to deletion

Retiring an old dashboard but keeping the dataset running ‘just in case.’

The one number we stare at while ignoring the iceberg.

Stopping data leaks before they make headlines.

Deciding where to spend time, money, and energy—usually wrong.

The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.

Because just because you can collect data doesn’t mean you should.

“I have 10 dashboards to fix and zero time for your ad-hoc request.”

Running the same weekly report with slightly different date filters.

Saving progress so your system can crash at a later, more inconvenient time.

Fake data used for training models when real data is too sensitive, messy, or non-existent.

The delicate art of begging people to care.

The theoretical version of your data that reality refuses to match.

The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).

Keeping unauthorized users out - until someone shares a password.

Because “I think this field means…” shouldn’t be part of data analysis.

The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.

Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.

A structured way to work with large datasets.

Helping engineers understand how data flows, transforms, and actually works.

Spotting the weirdos in your data—because outliers can mean fraud, errors, or just bad luck.

When you can’t commit to a single cloud provider.

The behind-the-scenes details of how data was collected.

Proof that a company probably takes security seriously.

Poking around in your data to find trends, outliers, and problems before they ruin your model.

The chaos of switching from Excel to an actual BI tool.

The lazy friend of machine learning—it just looks at its closest neighbors and copies them.

Where structured data goes to drown.

The endless cycle of finding new ways to blame bad data for bad decisions.

Frankenstein’s monster made of expensive software.

A group of overworked data engineers and analysts thrown together to fix a reporting disaster.

When a relational database is too much effort.

The fantasy of having the same data everywhere at the same time.

The serial focus assassin. Everyone knows at least one.

Turning numbers into narratives people might actually remember.

Checking your data before it embarrasses you.

The unlucky souls tasked with keeping data under control.

The family tree of your data, assuming you can track it.

Handpicking quality data like it’s fine wine.

Getting machines to do the boring stuff for you.

Treats every email address like nuclear launch codes and speed-dials Legal when someone shares a first name.

Because someone needs to process transactions in real-time.

The programming language everyone pretends to know.

When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.

Making sure your servers aren’t crying for no reason.

A flowchart-like model that makes decisions—think "choose your own adventure" but with math.

Demands data-driven decisions then overrides everything because their morning shower had "different vibes."

Talking to inanimate objects because humans are worse.

Keeping secrets… until someone forgets to lock the database.

Making sure your app doesn’t make users want to throw their devices.

Microsoft’s favorite way to make bar charts look really dramatic.

“Here’s what you should do, but no one actually follows.”

That thing you forgot to set up before the system crashed.

The dashboard everyone ignores until an executive asks for it.

The magic behind neural networks—basically, trial and error on steroids until the model gets it right.

Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.

Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.

“Throw some data models at the wall and see what sticks.”

Because mistakes were made.

“We ran the same SQL query but indexed a column, so now it’s 2% faster.”

Shows up after work's done to sink regulatory fangs into your launch plans.

Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.

The stuff hackers (and marketers) dream about stealing.

Finding insights in data—or just realizing what’s missing.