Smarter.SEO… powered by Qlik
Smarter.SEO is one of Differentia Consulting’s family of Smarter.BI Dashboards that provides those responsible for digital transformation with quick and simple access to a powerful range of information about your businesses most vital assets.
Is MetaData Management necessary for SEO? #SmarterSEO
Delivering a consistent and distinct brand image requires agile tools and technology to aid the collaborative process. Smarter.SEO facilitates the proliferation of your brand identity via metadata and empowers Marketing and Advertising companies to manage digital knowledge graph on a single platform in the cloud. This means that your SEO team can be assured that their content is properly understood by machines and humans alike without dis-ambiguity.
What role does Google play in Smarter.SEO?
External agencies, vendors and partners are all vying to take a share of what has become a >$trillion dollar industry. Google, the leader in internet search and paid for online advertisement for many years, helps publishers to list their products, services and solutions using what are called Rich Text Snippets. Artifacts that are a consistent means to display information relevant to search.
Google search, Google’s rich snippets, Google’s E-E-A-T are all underpinned by Schema.org metadata, which Google helped to pioneer Bing and Yahoo, and then Yandex, in 2011, to help with the discovery and validation of content for humans and machines. Crawlers index content using schema metadata.
Nearly all search is based on the presence of schema, and since AI engines crawled the internet for LLM based learning the need for metadata has been questioned by many an SEO expert for ranking. What they are not seeing is that metadata is now more important, not less to provide context, augmenting content, to help avoid hallucinations by these engines, to enable listing.
For many AI tools they are relying on what is know as fan based crawling which simply makes multiple search requests and aggregates the results but actually have no deterministic principles to underpin the results. To avoid complete hallucination they may also use search results as the basis for aggregation. This is a nonsense as the RAG componentised search elements may be unrelated and thus have no actual results to compare to. The results are then unreliable and inconsistent.
What role does Qlik play in Smarter.SEO?
Smarter.SEO pulls information in relation to all metadata artifacts, providing management with quick and simple access to a powerful range of data about your internet digital knowledge graph.
When you need to control your digital narrative both on-page (SEO) and at the meta data level (to be discovered), core to your business, it is vital you have insight into which artifacts are being deployed and their accuracy. Smarter.SEO provides complete information on the performance of your company’s metadata assets and content; so you can audit and control them. Using Qlik as a visualisation tool for VISEON.IO our SEO control and audit tool we pull data from our proprietary APIs, FUSEON to create live data for analysis, validating both for completeness and accuracy across the domains that you own.
The Smarter.SEO VISEON Qlik analytics application provides an audit trail and analytics to document governance to metadata standards and audit for compliance to activities so you can protect and remain competitive environment in the most dynamic of marketplaces that is now more complex to navigate with the AI tools each having their own specific search methods.
The Critical Role of Metadata in AI-Driven Search and RAG Systems
Structured data, such as Schema.org markup, is fundamental to modern semantic search engine optimisation (SEO) and the effective functioning of AI-driven systems, including large language models (LLMs). Despite some SEO professionals questioning the relevance of metadata in the context of LLM-based web crawling, it remains essential for providing precise context, enhancing discoverability, and mitigating AI hallucinations—incorrect or fabricated outputs generated by models lacking sufficient semantic grounding.
Metadata, particularly structured schema, enables search engines like Google to deliver rich snippets, populate knowledge graphs, and support conversational search features. For LLMs, metadata serves as a critical anchor, ensuring responses are rooted in verifiable data. For instance, a webpage with “Article” schema markup, including authorship and publication date, signals expertise and trustworthiness, aligning with Google’s EEAT guidelines.
Many AI systems employ a retrieval technique known as fan-out crawling, where multiple search queries are issued, and results are aggregated to inform responses. This approach, common in Retrieval-Augmented Generation (RAG) frameworks, often lacks robust deterministic principles, relying heavily on the quality of results. When these results are unrelated or of low quality—due to SEO spam or outdated content—the aggregated data can lead to incoherent or inaccurate outputs, exacerbating hallucination risks.
Definition and Mechanism
- Fan-out Crawling: This technique involves issuing multiple search queries simultaneously to gather a wide range of results. The results are then aggregated to inform the AI’s responses. This method is indeed common in Retrieval-Augmented Generation (RAG) frameworks, where the goal is to enhance the model’s output with relevant external information.
Quality of Results
- The effectiveness of fan-out crawling heavily depends on the quality of the retrieved results. If the results are irrelevant, low-quality, or outdated, they can lead to incoherent or inaccurate outputs. This is a significant concern, as it can increase the risk of hallucinations—instances where the AI generates information that is not grounded in reality.
Deterministic Principles
- Lack of robust deterministic principles is the underlying issue. Fan-out crawling can be less predictable than other methods, as the aggregation of results from various queries may not follow a consistent logic or framework, making it challenging to ensure reliability in the outputs.
Conclusion
Structured metadata addresses these challenges, and risks by providing clear, machine-readable context that improves semantic retrieval relevance. For example, a webpage marked with “LocalBusiness” schema, specifying location and services, is more likely to be accurately matched to a relevant query, reducing the inclusion of irrelevant sources in RAG outputs. As AI-driven semantic search evolves, metadata-driven indexing and semantic context-aware retrieval will be critical for ensuring precision and reliability in both SEO and AI applications. With Smarter.SEO powered by Qlik you can be assured that the best digital- version of your organisation is represented the way you want.
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