Schema Data: Shifting Beyond SEO in Digital Marketing

A recent article about Schema structured data sparked a thought-provoking discussion on Twitter, particularly about its role in adapting to emerging AI technologies and its evolving impact on alt-SEO strategies. The conversation, inspired by Jono Alderson’s article, focused on the increasing need to think of Schema.org structured data markup as a tool that can support the understanding of content by AI technologies.

Alderson’s article delves into how structured data markup, typically used to improve search engine visibility, could also serve as a crucial asset for AI systems that aim to better understand, categorise, and surface published content across the web. This marks a shift in how SEO might be perceived in the age of artificial intelligence, pushing beyond traditional methods towards a more sophisticated integration of AI and structured data.

The notion that AI technologies are beginning to rely more heavily on structured data to interpret the context of content is gaining traction. As AI systems evolve, the way they process and understand online content could significantly transform, especially with the introduction of natural language processing and machine learning algorithms. This suggests that Schema.org data might serve as a bridge between traditional SEO tactics and more advanced AI-driven content delivery.

As these AI tools become more powerful, it’s likely that structured data will play a key role not only in optimising websites for search engines but also in enabling AI systems to surface more relevant and contextually accurate content to users. This has important implications for digital marketers and content creators, who may need to reconsider their strategies for content structuring, especially in light of AI’s growing role in the digital ecosystem.

In this context, it becomes clear that the traditional SEO methods will need to adapt to the rise of AI. Digital marketers, web developers, and content creators must start thinking about Schema data in a broader sense—one that aligns with the needs of both search engines and AI technologies. By doing so, they will ensure their content remains discoverable and relevant as the digital landscape evolves.

Schema.org Structured Data Markup

The content on a website is often referred to as unstructured data because it lacks a formal, organised structure that labels each part of the content in a way that machines can easily interpret. Unstructured data may contain useful information, but it is not inherently arranged for machines to instantly understand.

On the other hand, structured data is essentially the same content but with added organisation. This type of data uses labels that identify images, authors, and content, allowing machines to immediately process and understand the information. By marking up content in this structured way, it becomes easier for search engines and AI technologies to extract meaningful insights.

Schema.org structured data markup is commonly used by publishers and SEO professionals to enhance a webpage’s chances of being featured in rich results on Google. This has been the traditional view in the SEO community, which is reflected in the many WordPress plugins designed specifically for generating structured data that Google can use to display rich results. These tools help optimise web pages for improved visibility in search engines.

However, new AI technologies are emerging that are capable of using structured data in more advanced ways. As a result, search marketers are now facing the challenge of adapting their approach to deploying structured data. Jono Alderson, in his article, encourages professionals to think of structured data as a “data-first foundation” that prepares websites for the future.

In light of the growing role of AI in digital marketing, this shift in thinking is essential. Structured data is no longer just a tool for SEO but is increasingly becoming a key component in building a robust foundation for AI-driven technologies. By adopting a data-first mindset, publishers and marketers can ensure their content is ready for the next wave of technological advancements.

The article suggests that Schema.org markup should be seen not just as a tool for improving SEO but as a method of communicating the purpose of a web page and how it fits within the broader context of the entire website. Jono Alderson emphasises the importance of thinking beyond immediate SEO benefits and focusing on creating a well-connected data structure. He writes:

“But don’t shy away from building a connected graph of broader, ‘descriptive’ schema just because Google’s not showing an immediate return. These ‘descriptive’ types and relationships might end up being the lifeline between your content and the AI models of the future.”

Jono shared his thoughts on X (formerly Twitter), where his article sparked further discussion on the evolving role of structured data. Martha van Berkel, founder of SchemaApp, voiced her agreement with Jono’s perspective, recognising that the function of Schema structured data markup is undergoing a shift. She acknowledged that the approach to structured data is no longer just about traditional SEO but about preparing websites for the future, where AI models will increasingly rely on rich, descriptive data to understand and connect content across the web.

This conversation highlights the growing recognition within the industry that Schema.org markup is becoming a fundamental element in building a sustainable, AI-ready web. The focus is now on creating data structures that are not only relevant for search engines but also future-proofed for emerging technologies. By prioritising this shift, content creators can position themselves for success in a rapidly changing digital landscape.

Martha van Berkel, founder of SchemaApp, also weighed in on the conversation, tweeting her agreement with Jono Alderson’s views. She wrote:

“I agree with you that the role of schema markup is changing. Building a knowledge graph to manage how your website/content is understood with schema, and then asking it questions, will be more important than optimising for Rich Results or for Google.”

This reflects a growing consensus in the industry that Schema.org structured data is evolving from a tool purely for SEO to a more foundational element in content management, preparing websites for the future, where AI technologies will rely heavily on structured data to interpret and connect content.

Ammon Johns also contributed to the discussion, pointing out a significant concern about Schema.org markup. He tweeted:

“The biggest issue with Schema is that it is largely just self-declaration, no different in essence to META content, and we know how reliable Google decided that stuff was. So Google will use it, but they are unlikely to fully trust it.”

Ammon raises a valid point: structured data, much like META content, can’t be entirely relied upon without scrutiny. Google may use it, but there’s no guarantee of trust. One potential solution, as suggested by Perplexity AI, is to use a smaller index of high-quality websites, improving the accuracy of data utilisation.

Gagan Ghotra also joined the conversation, expressing frustration with the limitations of current SEO and structured data tools. Gagan tweeted that they often feel restricted in expanding their use of structured data, as the available tools don’t always offer the functionality needed to maximise its potential. This highlights a gap in the market for more robust tools that allow for more comprehensive implementation of structured data across websites.

This ongoing dialogue underscores the rapidly shifting role of Schema.org markup, where the emphasis is moving from purely SEO optimisation to creating more meaningful, AI-friendly data structures.