Corporate for "I forgot what this is about but I need to make noise before someone notices".
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.
The dream every company sells but never actually delivers.
The frustrations of explaining, again, why two reports don’t match.
Demands data-driven decisions then overrides everything because their morning shower had "different vibes."
A central place for data that everyone fights over.
Data’s glow-up into something actually useful.
Renting someone else’s servers but paying more.
Because raw data is just too ugly.
Brings structure to chaos with dbt and a folder hierarchy that could win awards.
A chaotic attempt to explain why the numbers don’t match across reports.
That thing you forgot to set up before the system crashed.
The reason healthcare companies fear data leaks.
Making sure data doesn’t become a dumpster fire.
When processing big data was still cool.
Turning monolithic problems into distributed chaos.
The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
Rules everyone agrees on but nobody follows.
Shipping code faster than your team can fix bugs.
Following data laws just enough to avoid fines.
When economics meets statistics and things get extra nerdy.
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
When your system crashes but pretends it never happened.
Code for “this could’ve been a Slack message.”
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.
Idea-vomiting buzzword dispenser.
Making sure your servers aren’t crying for no reason.
“We’ll consider all possible factors… except the ones that make us look bad.”
Making complex queries expensive since forever.
“Will this dashboard break when more than 5 people refresh it at once?”
“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”
The constant struggle to keep data clean, secure, and useful.
When everyone agrees on what to pretend to care about.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
Sharing resources and pretending everything is fine.
Deploying apps without touching infrastructure (until something breaks).
Grouping users to prove that trends aren’t just luck.
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
Load first, transform later—modern data integration in action.
The one dashboard we all agreed on… until someone else made a new one with different numbers.
Making sure standard data values stay standard—good luck with that.
Shows up after work's done to sink regulatory fangs into your launch plans.
Trying to convince non-technical people that data matters.
When leadership changes the KPI goal after you’ve already built the report.
Someone else’s computer, but shinier.
The key metrics leadership suddenly decided to care about this quarter.
A fancy word for "number we use to see if our model sucks or not."
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
Creates JIRA tickets to track their JIRA tickets while drowning in chaos.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
The alarm system for when hackers come knocking.
“This dashboard is broken, but let’s not discuss it in front of leadership.”
Because sometimes, you actually want long-winded responses.
Keeping secrets… until someone forgets to lock the database.
Fixing data mistakes before they embarrass you.
The unlucky souls tasked with keeping data under control.
The thing everyone blames but nobody fixes.
Removing errors, duplicates, and someone else’s bad decisions.
A free tool for tracking website traffic—until privacy laws step in.
A data point that’s way off from the rest—could be an error, or could be the next big discovery.
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
Ignoring that data quality issue until it causes real problems.
Keeping multiple copies of your data in sync.
The badge that says “We take security seriously” (but still have breaches).
SQL’s rebellious younger sibling.
A structured way to describe data relationships (or overcomplicate things).
The buzzword architects love, but engineers fear.
“I have 10 dashboards to fix and zero time for your ad-hoc request.”
Turning data into a fixed-size mess—useful for passwords, not so great if you ever need to reverse it.
Double-checking data before it makes a fool of you.
Running the same weekly report with slightly different date filters.
Fine-tunes LLMs like they’re sourdough starters. Has five GPU credits left and no intention of using them responsibly.
The legal hoops companies jump through to keep your data kinda safe.
Machine learning for people who don’t want to do machine learning. Push a button, get a model—hopefully, a good one.
Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.
When your company trends on Twitter for all the wrong reasons.
The underappreciated hero who turns messy data into charts and makes everyone else look good.
Feeding your data pipeline a never-ending buffet.
Making sense of numbers so businesses can pretend to be data-driven.
The easiest SQL query that someone still wants to call a "data-driven insight."
Metadata management to keep track of your ever-growing data jungle.
The universal answer to every data question, forever and always.
When your AI learns from biased data and makes unfair decisions—because garbage in = garbage out.
Like moving houses, but with more downtime and crying.
Sifting through data, hoping for something insightful.
Collecting data the unethical-but-effective way.
Talking to inanimate objects because humans are worse.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
When you can’t commit to a single cloud provider.
Holding onto data just long enough to avoid legal trouble.
Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
Teaching machines to "think" so they can replace humans (but mostly just generate weird chatbot responses).
The programming language everyone pretends to know.
The secret sauce behind databases that actually perform.
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
How much pain your system can handle before collapsing.
When a relational database is too much effort.
A strategic delay tactic used to avoid commitment in meetings with more than three directors present.
The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.
Tracking data’s dramatic journey from birth to deletion
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