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.
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
The stuff hackers (and marketers) dream about stealing.
Cutting down the number of variables in your dataset—because sometimes, less is more (especially in Excel).
The magic that makes your slow queries slightly less slow.
When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.
Moving data from one mess to another.
Double-checking data before it makes a fool of you.
When your company trends on Twitter for all the wrong reasons.
Because reading rows one at a time is for chumps.
All the missing data that everyone pretends doesn’t exist.
The mess left behind when shortcuts meet data analytics.
When leadership changes the KPI goal after you’ve already built the report.
Translating raw data into real-world meaning so it’s actually useful.
The Data Lake’s evil twin.
Blueprints for security that companies try to follow.
The serial focus assassin. Everyone knows at least one.
Keeps the data stack humming so analysts can pretend it’s “just a quick query.”
The easiest SQL query that someone still wants to call a "data-driven insight."
“Will this dashboard break when more than 5 people refresh it at once?”
Hoping two systems eventually agree on reality.
Turning numbers into narratives people might actually remember.
The law that keeps finance teams on their toes.
The reason your database admin hates you.
Making sure standard data values stay standard—good luck with that.
A digital breadcrumb trail for when things inevitably go wrong.
Treats your dashboards like a digital coloring book.
The magic behind neural networks—basically, trial and error on steroids until the model gets it right.
A last-minute meeting because someone didn’t read the dashboard.
Fluent in stakeholder management, and can turn vague requests into scarily accurate dashboards. Built half the team's workflows on vibes and somehow made it work.
Data’s glow-up into something actually useful.
Getting machines to do the boring stuff for you.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
Spotting the weirdos in your data—because outliers can mean fraud, errors, or just bad luck.
The family tree of your data, assuming you can track it.
Like conducting a symphony, but with way more screaming.
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
Feeding your data pipeline a never-ending buffet.
Helping engineers understand how data flows, transforms, and actually works.
When a relational database is too much effort.
Getting the most out of your budget before the CFO notices.
The bare minimum dressed up like a competitive edge.
Someone else’s computer, but shinier.
The fantasy of having the same data everywhere at the same time.
A 57-slide PowerPoint where 3 slides actually contain useful charts.
Protecting user info while secretly monetizing it.
Because just because you can collect data doesn’t mean you should.
Handpicking quality data like it’s fine wine.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
Making sure data stays trustworthy—or at least looks like it.
XML’s cooler, slightly less annoying cousin.
A fancy word for "number we use to see if our model sucks or not."
Wants to monitor every client blink without a clue what to do with it.
Training models on decentralized data—because sharing is caring, but privacy lawsuits are expensive.
Because “I have no idea where this data came from” is not a great answer.
Cutting back on data storage costs until everything runs painfully slow.
A table that tells you how often your model gets things right (or, more realistically, how often it screws up).
Finding out where all the secrets are hiding before someone else does.
“We’ll consider all possible factors… except the ones that make us look bad.”
Keeping secrets… until someone forgets to lock the database.
Sifting through data, hoping for something insightful.
Because sometimes, you actually want long-winded responses.
This query better finish before the meeting, or I’m in trouble.
Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.
Data about your data—because keeping track of what your numbers mean is harder than it should be.
It’s not just a conference—it’s a group hug wrapped in YAML. No fluff, no gatekeeping—just real talk from data practitioners sharing their learnings and strategies.
Where your data goes to sleep.
The dashboard everyone ignores until an executive asks for it.
Because finding the right dataset shouldn’t feel like a scavenger hunt.
The one number we stare at while ignoring the iceberg.
The theoretical version of your data that reality refuses to match.
Checking if your security is solid—or just wishful thinking.
Making teams promise they won’t break each other’s data pipelines.
Because “whatever naming convention feels right” is not a strategy.
Turning monolithic problems into distributed chaos.
Ignoring that data quality issue until it causes real problems.
Collecting data the unethical-but-effective way.
Predicting all the ways data can ruin your day.
A fancy term for “don’t let hackers steal our stuff.”
The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.
The constant struggle to keep data clean, secure, and useful.
Poking around in your data to find trends, outliers, and problems before they ruin your model.
Like moving houses, but with more downtime and crying.
Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.
Stopping data leaks before they make headlines.
Because bad data leads to bad decisions and lots of excuses.
Goes to every conference and is part of every newsletter. Needs an intervention.
The endless cycle of finding new ways to blame bad data for bad decisions.
The key metrics leadership suddenly decided to care about this quarter.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
A central place for data that everyone fights over.
The one dashboard we all agreed on… until someone else made a new one with different numbers.
Making sense of numbers so businesses can pretend to be data-driven.
The reason your computer fan sounds like a jet engine.
Bridging the gap between development and IT operations.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
We built it for five people and are praying it doesn’t break at ten.
Running the same weekly report with slightly different date filters.
Fake data used for training models when real data is too sensitive, messy, or non-existent.
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
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