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.
Making your inefficient queries slightly less embarrassing.
Brings structure to chaos with dbt and a folder hierarchy that could win awards.
When you want fast answers and minimal thinking.
Trying to convince non-technical people that data matters.
Making complex queries expensive since forever.
Checking if your security is solid—or just wishful thinking.
Because mistakes were made.
Deciding where to spend time, money, and energy—usually wrong.
“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”
The one number we stare at while ignoring the iceberg.
Letting a neural network go crazy with layers upon layers of computation—basically AI's version of overthinking.
Scrambling data so only the right people (hopefully) can read it.
Shoving a half-baked feature into the project at the last minute.
A bunch of decision trees working together to make better predictions—because one tree alone isn’t enough.
Following data laws just enough to avoid fines.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
The magic that makes your slow queries slightly less slow.
The legal hoops companies jump through to keep your data kinda safe.
“We made a pretty chart—please pretend it changed your decision-making.”
Because “whatever naming convention feels right” is not a strategy.
“We need better numbers, but we don’t want to change anything.”
Digging through massive datasets, hoping to strike gold.
Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.
Convincing everyone that my version of the dashboard is the truth.
SQL’s rebellious younger sibling.
A free tool for tracking website traffic—until privacy laws step in.
When your company trends on Twitter for all the wrong reasons.
Trying to guess the future based on past data—like a digital crystal ball, but with spreadsheets.
The delicate art of begging people to care.
Keeping secrets… until someone forgets to lock the database.
The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.
Because finding the right dataset shouldn’t feel like a scavenger hunt.
The difference between well-structured data and a digital black hole.
Keeping multiple copies of your data in sync.
Bridging the gap between development and IT operations.
Hiding sensitive data so developers don’t see what they shouldn’t.
Making sure your app doesn’t make users want to throw their devices.
The art of torturing data until it confesses something useful—or at least makes a nice chart.
The awkward silence between launch and someone actually using it.
Europe’s way of reminding companies that data privacy matters.
A fancy term for “don’t let hackers steal our stuff.”
Rules everyone agrees on but nobody follows.
Human API who communicates in endpoints and considers UIs a moral weakness.
Moving data to the cloud—hopefully without breaking everything.
Because not every department deserves full database access.
Vanishes at deadlines but demands immediate responses to vague emails (read: your boss)
Fake data used for training models when real data is too sensitive, messy, or non-existent.
We built it for five people and are praying it doesn’t break at ten.
That thing you forgot to set up before the system crashed.
Finding insights in data—or just realizing what’s missing.
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
A 57-slide PowerPoint where 3 slides actually contain useful charts.
Tweaking a button color and calling it "strategy."
Stripping away identities because privacy lawsuits are expensive.
Rules about data that everyone agrees on but nobody follows.
Making teams promise they won’t break each other’s data pipelines.
A marketing term for "we kinda fixed the Data Lake problem."
The frustrations of explaining, again, why two reports don’t match.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
All the missing data that everyone pretends doesn’t exist.
Artificially inflating your dataset so your model learns better—kind of like stretching the truth on a résumé.
Telling you whether your results matter or if they’re just a fluke—like winning the lottery.
When your data is so bloated no one knows what to do with it, but it sounds impressive.
The behind-the-scenes details of how data was collected.
Because raw data is just too ugly.
Making sure data stays trustworthy—or at least looks like it.
A chaotic attempt to explain why the numbers don’t match across reports.
Proof that a company probably takes security seriously.
Because well-managed data is the difference between insights and chaos.
The reason your computer fan sounds like a jet engine.
“We need to filter this data in every way possible until it agrees with us.”
Turns their poor planning into your emergency with Slack messages that induce cardiac events.
When you can’t commit to a single cloud provider.
The dashboards and reports that will be outdated within a week.
The chaos of switching from Excel to an actual BI tool.
Google’s way of making your SQL queries cost a small fortune.
Because manually checking your code is for the weak.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
Code for “this could’ve been a Slack message.”
When a relational database is too much effort.
The buzzword architects love, but engineers fear.
The mess left behind when shortcuts meet data analytics.
Talking to inanimate objects because humans are worse.
Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.
Transforms your bullet point into 40 slides featuring at least two mountain-climbing metaphors.
Getting the most out of your budget before the CFO notices.
The dream every company sells but never actually delivers.
Finding out where all the secrets are hiding before someone else does.
Data about your data—because keeping track of what your numbers mean is harder than it should be.
Trust no one, verify everything. Paranoia as a security strategy.
Metadata management to keep track of your ever-growing data jungle.
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.
Where your data has commitment issues.
The numbers that make up your analysis—sometimes useful, sometimes just noise.
The bare minimum dressed up like a competitive edge.
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.
The universal answer to every data question, forever and always.
Renting someone else’s servers but paying more.
When leadership changes the KPI goal after you’ve already built the report.
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