January 13, 2026
Vibes vs Numbers!
Understanding the Types of Data in Data
Numbers vs Vibes: comments explode over what 'data' means
TLDR: A simple explainer breaks down numbers vs descriptions and tidy vs messy data, plus big data’s Three Vs. Comments erupt into a numbers‑versus‑vibes brawl, AI‑replaces‑basics hot takes, and meme demands for a fourth V—Vibes—showing why knowing your data matters for real decisions.
Data Types 101 dropped, splitting the crowd into two loud camps: the numbers or it didn’t happen crew and the vibes tell the story romantics. The article explains that data can be numbers (quantitative) or descriptions (qualitative), plus whether it’s tidy (structured), messy (unstructured), or somewhere in between. Big data’s “Three Vs” — Volume, Variety, Velocity — got everyone buzzing on the post.
Strongest take? Quant fans shouted that only counts and measurements matter; qual defenders fired back that labels and rankings capture real life nuance. Then came the chaos: someone claimed “LLMs make data types obsolete”, and a stats purist clapped back with a spreadsheet emoji and “learn the basics before your robot does.” Privacy hawks chimed in, calling social data just surveillance in yoga pants.
The memes rolled in hard. “Semi‑structured = my junk drawer,” “Unstructured = my inbox,” and a movement to upgrade the Three Vs to Four Vs: add Vibes. A rating‑scale skirmish erupted too: is a 5‑star twice a 2‑star? Cue popcorn. Love it or hate it, readers agreed on one thing: understanding what your data actually is changes the story you tell — and the decisions you make. That’s the rule of the internet.
Key Points
- •Data is defined as raw facts and figures that become meaningful through processing and analysis.
- •Two primary data types are quantitative (numeric) and qualitative (descriptive), each with distinct analysis methods.
- •Quantitative data splits into discrete (countable) and continuous (measurable within ranges) subtypes.
- •Qualitative data includes nominal (unordered categories) and ordinal (ordered categories with undefined intervals).
- •Data can be structured, unstructured, or semi-structured; key big data sources include transactional, machine, social, and text data, and are characterized by Volume, Variety, and Velocity.