Published:
by
Wayne Smith
At first glance, microdata is just JSON-LD flipped inside out — same schema concepts, only baked straight into the HTML. The goal’s the same: describe entities so search engines can read them.
On paper, JSON-LD keeps things neat: writers handle content, devs handle schema, SEO decides what goes in the table. That’s how you get your “gain of knowledge” into search — what used to just feed the knowledge panel, but now fuels AI overviews at the top of the SERP.
In practice? JSON-LD bloats fast. Every edit means digging back through code, remembering what you or someone else meant months ago. If the schema drifts from the visible content, best case it loses impact, worst case it kills trust.
Microdata changes the workflow. Since it’s inline, you can validate, check the table, and instantly spot where to add knowledge signals. Less back-and-forth, more alignment with the page itself — which matters now that every entity has a shot at surfacing in AI summaries above the results.
How Microdata Schema Compares to LD-JSON
Take this microdata for a WebPage with a headline and suggested TL;DR description snippet:
In JSON-LD, you’d see "@type": "WebPage". In microdata, that becomes the itemtype attribute. The headline hangs right off the <h1%gt;, the URL lives in a , and the description rides inline on the <p>. itemscope marks the start of the block — It doesn’t feel essential until things get nested, then it’s critical.
Here’s the same thing in JSON-LD:
Run both through validator.schema.org and you’ll get the same data table.
The real trick with microdata is scope and nesting. You open a block with itemscope + itemtype, and it closes when the tag closes. Inside that block, you can nest other schema items, which makes it possible to pack a lot of entity detail into short content.
For example:
… The JSON equivalent:
That nested block becomes part of the parent itemscope. Stack a few of these and suddenly you’ve got a dense, entity-rich dataset sitting invisibly inside your HTML.
Microdata solves LD-JSON Bloat and revision fatigue
One of the biggest frustrations with JSON-LD is how quickly it can become bloated. Even a simple WebPage or Article can balloon into dozens of lines of code, much of it repeating information that’s already present in the HTML. The gains from division of labor are lost when the LD-JSON needs to be manually compared to content that has been revised.
There’s another consideration as well: JSON-LD lives apart from the visible page content. Like meta tags, it isn’t displayed to users, which means search engines have to take it on trust. That creates a gap where some content producers are tempted to overstate, exaggerate, or even spam their structured data — a problem engines constantly guard against.
Even a well-intentioned updating of the byline date on the page can result in the trust of the date being removed ... the schema date needs to match the on-page content. For Example consider the Microdata for the published date:
Microdata Structure and Gain of Knowledge
While using itemscope and itemtype can mirror creating a LD-JSON data block a JSON object block it is not always required ... consider this microdata
The about property’s data type defaults to the type of the referenced thing, allowing multiple values and inline context without extra JSON objects.
The equivalent LD-JSON schema would be:
Both create the same data table at https://validator.schema.org/
Looking at the data table, you can see a chance to improve the info—and a small mistake too: Dave Grohl isn’t just a ‘thing,’ he’s a person. Here is a correct Mircodata example
Even though the about property is nested in the otherwise text description field, the itemscope="" pulls the schema data back to the previous itemscope under the itemtype="https://schema.org/WebPage" reviwing the data table shows the gain of knowledge.
Solution Smith Testing Protocols
Solution Smith tests SEO and guided AI search the same way it tests software -- methodically and with evidence. If a feature is claimed, it gets tested. Observations begin as anecdotal data points, which are then verified through repeated experiments.
Solution Smith does not rely on Google to confirm or deny findings -- in fact, it’s expected that Google and other search engines won’t publicly disclose the inner workings of their algorithms.