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".
A measure of how spread out your data is—basically, how weird or normal your numbers are.
The secret sauce behind databases that actually perform.
Stalking customers, but make it “data-driven.”
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.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
“Your data reports need to be better, but we won’t give you more resources.”
When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.
Where your data has commitment issues.
The dream every company sells but never actually delivers.
Bridging the gap between development and IT operations.
“We’ll consider all possible factors… except the ones that make us look bad.”
Digging through massive datasets, hoping to strike gold.
Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.
A flowchart-like model that makes decisions—think "choose your own adventure" but with math.
A gradient boosting algorithm that wins Kaggle competitions—because sometimes brute force just works.
Making complex queries expensive since forever.
Because raw data is just too ugly.
Like conducting a symphony, but with way more screaming.
Shoving a half-baked feature into the project at the last minute.
Teaching computers to recognize patterns so they can pretend to be smart—until they overfit and fail.
Trust no one, verify everything. Paranoia as a security strategy.
Proof that "we'll fix it later" never actually means later.
Data’s glow-up into something actually useful.
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
The IT version of “Ctrl+Z” for disasters.
A last-minute meeting because someone didn’t read the dashboard.
Making teams promise they won’t break each other’s data pipelines.
The behind-the-scenes data that keeps everything (barely) organized.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
Making your inefficient queries slightly less embarrassing.
Because mistakes were made.
A chaotic attempt to explain why the numbers don’t match across reports.
The buzzword architects love, but engineers fear.
Transforming categorical data into numerical form—because computers just don’t get words.
Checking if your security is solid—or just wishful thinking.
Wants to monitor every client blink without a clue what to do with it.
The difference between well-structured data and a digital black hole.
Where we test new models and hope no one deploys them to production by accident.
Blueprints for security that companies try to follow.
Retiring an old dashboard but keeping the dataset running ‘just in case.’
A structured way to describe data relationships (or overcomplicate things).
Keeps the data stack humming so analysts can pretend it’s “just a quick query.”
The science of making sense of data—assuming it’s not lying to you.
“I haven’t looked at the data yet, but I will… eventually.”
Following data laws just enough to avoid fines.
Tweaking the settings of your machine learning model—kind of like adjusting the seasoning in a bad recipe.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
“We need to filter this data in every way possible until it agrees with us.”
Because not every department deserves full database access.
A statistical way to check if two things are related or if your data is just messing with you.
Helping engineers understand how data flows, transforms, and actually works.
Protecting user info while secretly monetizing it.
The Data Lake’s evil twin.
A central place for data that everyone fights over.
The constant struggle to keep data clean, secure, and useful.
Sorting data into neat categories, only for users to ignore them.
Fake data used for training models when real data is too sensitive, messy, or non-existent.
Poking around in your data to find trends, outliers, and problems before they ruin your model.
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
“We need better numbers, but we don’t want to change anything.”
The secret sauce that makes data searchable, understandable, and actually useful.
When bad data leads to even worse decisions.
Shows up after work's done to sink regulatory fangs into your launch plans.
Getting the most out of your budget before the CFO notices.
Workflow automation, so you don’t have to babysit data pipelines.
When your company trends on Twitter for all the wrong reasons.
“Throw some data models at the wall and see what sticks.”
The endless cycle of finding new ways to blame bad data for bad decisions.
The terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.
The bare minimum dressed up like a competitive edge.
Making sure your servers aren’t crying for no reason.
Splitting your database into smaller disasters.
Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.
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
A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.
The chaos of switching from Excel to an actual BI tool.
Teaching machines to "think" so they can replace humans (but mostly just generate weird chatbot responses).
When economics meets statistics and things get extra nerdy.
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
When search meets machine learning and everyone gets confused.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
Guards their "secret metric" like it's launch codes when it's really just page views in a trench coat.
The key metrics leadership suddenly decided to care about this quarter.
Builds the data highways, then spends half the week fixing potholes caused by everyone else driving like maniacs.
The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
Because spreadsheets just don’t scale.
Organizing data at a scale where things will go wrong.
Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.
Rules everyone agrees on but nobody follows.
Slicing and dicing data until it fits your argument.
Because JSON wasn’t painful enough.
Making sure standard data values stay standard—good luck with that.
When your data is so bloated no one knows what to do with it, but it sounds impressive.
The thing everyone blames but nobody fixes.
Deciding where to spend time, money, and energy—usually wrong.
“I forgot to check the dashboard before this meeting.”
The dashboards and reports that will be outdated within a week.
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