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 terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.
A corporate delusion tactic to feign control, optimism, or progress in the face of complete chaos.
The unlucky souls tasked with keeping data under control.
When your company trends on Twitter for all the wrong reasons.
Handpicking quality data like it’s fine wine.
Proof that "we'll fix it later" never actually means later.
Because not every department deserves full database access.
The Data Lake’s evil twin.
Because mistakes were made.
A statistical way to check if two things are related or if your data is just messing with you.
Shipping code faster than your team can fix bugs.
A job posting for a data analyst who can also engineer pipelines and train AI models.
A structured way to describe data relationships (or overcomplicate things).
Because “I think this field means…” shouldn’t be part of data analysis.
Because manually checking your code is for the weak.
Moving data from one mess to another.
“We ran the same SQL query but indexed a column, so now it’s 2% faster.”
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
For when the cloud is just too far away.
Keeping data safe from hackers, leaks, and bad employees.
Sifting through data, hoping for something insightful.
Finding out where all the secrets are hiding before someone else does.
Ignoring that data quality issue until it causes real problems.
Making sure data stays trustworthy—or at least looks like it.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
No one understands the report, but we’re pretending we do.
Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.
The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.
Translating raw data into real-world meaning so it’s actually useful.
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.
A marketing term for "we kinda fixed the Data Lake problem."
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
Spotting the weirdos in your data—because outliers can mean fraud, errors, or just bad luck.
The easiest SQL query that someone still wants to call a "data-driven insight."
“Let’s keep slicing the data until we find something that supports our assumption.”
Slicing and dicing data until it fits your argument.
Corporate deity whose random breakfast thoughts outrank your entire research department.
When bad data leads to even worse decisions.
This query better finish before the meeting, or I’m in trouble.
Predicting all the ways data can ruin your day.
The universal answer to every data question, forever and always.
A fancy word for "number we use to see if our model sucks or not."
The family tree of your data, assuming you can track it.
Organizing data at a scale where things will go wrong.
A structured way to work with large datasets.
The behind-the-scenes data that keeps everything (barely) organized.
When you pivot data just to confirm what you already knew.
Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?
A fragile house of cards filled with hidden errors, broken formulas, and misplaced decimal points.
Like a Data Lake, but with regret control.
Treats your dashboards like a digital coloring book.
Checking if your security is solid—or just wishful thinking.
Because finding the right dataset shouldn’t feel like a scavenger hunt.
Because raw data is just too ugly.
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
“This data connector technically works, but barely.”
The buzzword architects love, but engineers fear.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
The programming language everyone pretends to know.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
Transforming categorical data into numerical form—because computers just don’t get words.
Redefines success metrics faster than politicians backpedal after an election.
Schedules pre-meetings for the pre-meeting's pre-brief because they couldn't read an email to save their life.
Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.
“We made a pretty chart—please pretend it changed your decision-making.”
The thing everyone builds but nobody documents.
The fantasy of having the same data everywhere at the same time.
The reason your reports make no sense.
The endless cycle of finding new ways to blame bad data for bad decisions.
Like conducting a symphony, but with way more screaming.
“Throw some data models at the wall and see what sticks.”
Helping engineers understand how data flows, transforms, and actually works.
Frankenstein’s monster made of expensive software.
Because SQL SELECT wasn’t fancy enough.
When your model suddenly starts making terrible predictions because the real world refused to stay the same.
Making pretty charts so people think the data makes sense.
Keeping secrets… until someone forgets to lock the database.
Keeping unauthorized users out - until someone shares a password.
Feeding your data pipeline a never-ending buffet.
Deciding where to spend time, money, and energy—usually wrong.
The legal hoops companies jump through to keep your data kinda safe.
A table that tells you how often your model gets things right (or, more realistically, how often it screws up).
The fight over who actually controls the data mess.
A chaotic attempt to explain why the numbers don’t match across reports.
Saving progress so your system can crash at a later, more inconvenient time.
When leadership changes the KPI goal after you’ve already built the report.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
When search meets machine learning and everyone gets confused.
The fine art of deciding who gets in and who gets a "403 Forbidden."
Guessing with data—because flipping a coin isn't "data-driven."
The underappreciated hero who turns messy data into charts and makes everyone else look good.
The reason your software updates faster than you can blink.
A/B testing’s overachieving cousin.
Tweaking the settings of your machine learning model—kind of like adjusting the seasoning in a bad recipe.
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
“Will this dashboard break when more than 5 people refresh it at once?”
Because someone needs to process transactions in real-time.
A 57-slide PowerPoint where 3 slides actually contain useful charts.
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