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.
Corporate for "I forgot what this is about but I need to make noise before someone notices".
“Yes, our data platform supports SQL. That’s not a selling point.”
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
Because spreadsheets just don’t scale.
Because winging it with data governance isn’t a long-term strategy.
The dashboard everyone ignores until an executive asks for it.
Splitting your database into smaller disasters.
Renting someone else’s servers but paying more.
Proof that a company probably takes security seriously.
Just because two things happen together doesn’t mean one caused the other. Like, eating more cheese doesn’t actually make you better at math.
Transforming categorical data into numerical form—because computers just don’t get words.
Collecting data the unethical-but-effective way.
Because “I think this field means…” shouldn’t be part of data analysis.
A marketing term for "we kinda fixed the Data Lake problem."
That thing you forgot to set up before the system crashed.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
A central place for data that everyone fights over.
Translating raw data into real-world meaning so it’s actually useful.
The endless cycle of finding new ways to blame bad data for bad decisions.
A fancy word for "number we use to see if our model sucks or not."
A structured way to describe data relationships (or overcomplicate things).
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
Hoping two systems eventually agree on reality.
Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.
“This data connector technically works, but barely.”
Corporate deity whose random breakfast thoughts outrank your entire research department.
Talking to inanimate objects because humans are worse.
The family tree of your data, assuming you can track it.
Running the same weekly report with slightly different date filters.
Fancy PowerPoint slides no one follows.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”
“This dashboard is broken, but let’s not discuss it in front of leadership.”
“Will this dashboard break when more than 5 people refresh it at once?”
The reason your database admin hates you.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
When everyone agrees on what to pretend to care about.
Guards their "secret metric" like it's launch codes when it's really just page views in a trench coat.
Blueprints for security that companies try to follow.
A gradient boosting algorithm that wins Kaggle competitions—because sometimes brute force just works.
A flowchart-like model that makes decisions—think "choose your own adventure" but with math.
When talking about talking becomes your main deliverable. Bonus points if you can turn it into a self-congratulatory Linkedin post.
Turning monolithic problems into distributed chaos.
The alarm system for when hackers come knocking.
Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.
Sharing resources and pretending everything is fine.
A free tool for tracking website traffic—until privacy laws step in.
Frankenstein’s monster made of expensive software.
A checklist of rules to follow… until regulations change again.
500 commits in 3 hours. No documentation and no survivors.
The badge that says “We take security seriously” (but still have breaches).
The dashboards and reports that will be outdated within a week.
Running a ton of random simulations to predict outcomes—because guessing with math sounds fancier.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
Keeping multiple copies of your data in sync.
Cutting back on data storage costs until everything runs painfully slow.
“We need better numbers, but we don’t want to change anything.”
Data about your data—because keeping track of what your numbers mean is harder than it should be.
Following data laws just enough to avoid fines.
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.
Because well-managed data is the difference between insights and chaos.
Stopping data leaks before they make headlines.
Because SQL SELECT wasn’t fancy enough.
Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.
“We need to filter this data in every way possible until it agrees with us.”
The reason your software updates faster than you can blink.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
When your company trends on Twitter for all the wrong reasons.
A group of overworked data engineers and analysts thrown together to fix a reporting disaster.
“Let’s keep slicing the data until we find something that supports our assumption.”
The one dashboard we all agreed on… until someone else made a new one with different numbers.
“We ran the same SQL query but indexed a column, so now it’s 2% faster.”
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
“I have 10 dashboards to fix and zero time for your ad-hoc request.”
Because raw data is just too ugly.
When your data is so bloated no one knows what to do with it, but it sounds impressive.
Sorting data into neat categories, only for users to ignore them.
Making data look important in executive meetings.
Keeping data within borders—because governments say so.
Checking your data before it embarrasses you.
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
Tracking data’s dramatic journey from birth to deletion
Microsoft’s latest “one tool to rule them all” attempt—until the next one.
The chaos of switching from Excel to an actual BI tool.
The buzzword architects love, but engineers fear.
Tweaking the settings of your machine learning model—kind of like adjusting the seasoning in a bad recipe.
Sifting through data, hoping for something insightful.
The "we'll fix it in production" person. They're just one misplaced comma away from getting fired.
Google’s way of making your SQL queries cost a small fortune.
Creates JIRA tickets to track their JIRA tickets while drowning in chaos.
Redefines success metrics faster than politicians backpedal after an election.
Hiding sensitive data so developers don’t see what they shouldn’t.
Workflow automation, so you don’t have to babysit data pipelines.
Where structured data goes to drown.
Stripping away identities because privacy lawsuits are expensive.
A job posting for a data analyst who can also engineer pipelines and train AI models.
Makes dashboards for people who will ignore them and then ask you for the same numbers in a spreadsheet.
Your code, but only when someone remembers it exists.
Teaching models with labeled data—kind of like school, but for algorithms.
Human API who communicates in endpoints and considers UIs a moral weakness.
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