Metadata rabbithole for beginners 🐇🕳️

#metadata #automation #programming

If you'd asked me about metadata a week ago, I would have confidently said "oh yeah, those HTML tags for SEO" and moved on with my life. 🤷‍♂️
I was perfectly content in my web development bubble. Building React frontends, teaching Python to teenagers, and occasionally wrestling with CSS grid layouts.

Then I made a mistake that changed everything.

We were pivoting at my startup when we got a meeting with the National Archives of *******.
I walked in ready to talk about how AI could revolutionize their "document processing workflows" and "automate their daily tasks", but they hit me with something that left me with my mouth open:

"Scanning works fine. The main problem is metadata."

I nodded along like I totally understood, but internally I was like... wait, what? 😅

What even is that?

Metadata is data about data. But that definition is about as helpful as saying "programming is writing code."

Metadata is like the invisible Post-it notes stuck to everything digital, telling systems what something is, when it was created, and how to handle it 📝.

Metadata isn't just background noise, it is what makes things findable, sortable, and actually useful.

Think about it:

Without metadata, Google would just be expensive text matching
Without metadata, your database queries would crawl through every record
Without metadata, Netflix would show you random movies instead of recommendations

The crazy part? We use it constantly without realizing.

git commit?

That's creating metadata.

Database indexes?

Metadata.

Alt tags?

Also metadata 💡.

First thing to understand is like any technology, metadata has multiple frameworks with their pros and cons. Here are the four that matter most:

Dublin Core 📚
The universal standard for describing any digital resource with 15 basic elements. Maintained by the Dublin Core Metadata Initiative. Used everywhere - local libraries to government archives because it's simple enough for anyone to implement but comprehensive enough to describe virtually anything.

Think of it as the minimum metadata. Use it when you need basic discoverability without complexity.

<dc:title>Introduction to Machine Learning</dc:title> <dc:creator>Jane Smith</dc:creator> <dc:date>2024-07-15</dc:date>

Schema.org 🔍
A structured vocabulary that tells search engines what your web content actually means. Maintained by Google, Microsoft, Yahoo, and Yandex. This is how Google can show recipe cards and event details directly in search results—it understands the content structure, not just keywords.

It works by embedding structured data directly in your HTML. Use it when you want search engines to display rich snippets.

<div itemscope itemtype="https://schema.org/Recipe"> <h1 itemprop="name">Best Pizza Recipe</h1> <span itemprop="cookTime">PT30M</span> </div>

PREMIS 🏛️
Digital preservation standard that tracks every detail of a file's lifecycle - creation, migration and access events. Maintained by the Library of Congress. Museums and archives use this as digital files degrade and become unreadable over time without proper tracking and migration.

<premis:object> <premis:objectIdentifier> <premis:objectIdentifierType>local</premis:objectIdentifierType> <premis:objectIdentifierValue>document_001.pdf</premis:objectIdentifierValue> </premis:objectIdentifier> </premis:object>

EXIF 📸
Standard that automatically embeds camera settings, location, and timestamps into every photo you take. Maintained by the Camera & Imaging Products Association (CIPA). This exists as photos without context are nearly useless, you need to know when, where, and how they were captured.

<exif:DateTime>2024:07:28 14:30:22</exif:DateTime> <exif:GPSLatitude>60.1699</exif:GPSLatitude> <exif:Make>Apple</exif:Make>

Practical example: Reading PDF Metadata 🛠️
Now that we know the standards exist, let's see them in action. Here's how to extract Dublin Core metadata from any PDF: