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".

An interactive report that executives will ignore until they ask for the same data… in an Excel sheet.

Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.

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

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

When leadership changes the KPI goal after you’ve already built the report.

Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.

Treats your dashboards like a digital coloring book.

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

Making sure data stays trustworthy—or at least looks like it.

Because JSON wasn’t painful enough.

A chaotic attempt to explain why the numbers don’t match across reports.

Wants to monitor every client blink without a clue what to do with it.

Idea-vomiting buzzword dispenser.

Human API who communicates in endpoints and considers UIs a moral weakness.

Teaching machines to "think" so they can replace humans (but mostly just generate weird chatbot responses).

Sharing resources and pretending everything is fine.

Brings structure to chaos with dbt and a folder hierarchy that could win awards.

Sifting through data, hoping for something insightful.

The art of torturing data until it confesses something useful—or at least makes a nice chart.

Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.

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

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

Digging through massive datasets, hoping to strike gold.

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

Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.

Workflow automation, so you don’t have to babysit data pipelines.

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

Slapping AI on the same old nonsense.

The secret sauce behind databases that actually perform.

What you just got assigned because you asked a question in the meeting.

Hoping two systems eventually agree on reality.

When real-time isn’t worth the hassle.

Double-checking data before it makes a fool of you.

Preparing for disasters that will still somehow surprise you.

A/B testing’s overachieving cousin.

Because winging it with data governance isn’t a long-term strategy.

Translating raw data into real-world meaning so it’s actually useful.

When processing big data was still cool.

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

Holding onto data just long enough to avoid legal trouble.

Because “I have no idea where this data came from” is not a great answer.

Collecting data the unethical-but-effective way.

The magic that makes your slow queries slightly less slow.

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

Because someone needs to process transactions in real-time.

A structured way to work with large datasets.

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

Because manually moving data is for people who hate themselves.

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

Code for “this could’ve been a Slack message.”

Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.

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

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

Stripping personal details so data looks anonymous (but isn’t always).

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

Making your inefficient queries slightly less embarrassing.

That thing developers ignore until the database breaks.

Trust no one, verify everything. Paranoia as a security strategy.

Stripping away identities because privacy lawsuits are expensive.

The easiest SQL query that someone still wants to call a "data-driven insight."

A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.

Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)

Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.

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

The frustrations of explaining, again, why two reports don’t match.

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

When you pivot data just to confirm what you already knew.

When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.

The secret sauce that makes data searchable, understandable, and actually useful.

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

Corporate deity whose random breakfast thoughts outrank your entire research department.

Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?

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

This query better finish before the meeting, or I’m in trouble.

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

A digital breadcrumb trail for when things inevitably go wrong.

Granting permissions based on job roles, not personal favorites.

Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.

SQL’s rebellious younger sibling.

Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.

Microsoft’s latest “one tool to rule them all” attempt—until the next one.

Turning numbers into narratives people might actually remember.

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

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

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

Your code, but only when someone remembers it exists.

Turning monolithic problems into distributed chaos.

Following data laws just enough to avoid fines.

The reason your computer fan sounds like a jet engine.

Tracking data’s dramatic journey from birth to deletion

Stopping data leaks before they make headlines.

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

When your data is so bloated no one knows what to do with it, but it sounds impressive.

Frankenstein’s monster made of expensive software.

Making sense of numbers so businesses can pretend to be data-driven.

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

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

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

The unlucky souls tasked with keeping data under control.

Turning raw data into fancy charts that people ignore.

Teaching computers to recognize patterns so they can pretend to be smart—until they overfit and fail.