Structured data: what it is, what it is used for and how to implement it

The search for online visibility certainly depends on the quality of the content, but also on the ability to make it understandable and accessible to search engines: if we don’t speak the same language as Google, in fact, we can’t expect to be found by users or build a continuous relationship with our audience. Structured data responds exactly to this need, because it provides Google and other search engines with a clear and immediate language to interpret the information on a page and present it more effectively in search results. The correct use of these markups is what allows advanced features such as rich snippets, thematic carousels and knowledge panels to be activated, transforming a simple entry in the SERP into a more visible and informative result: a recipe with structured data immediately shows the ingredients and cooking times, an eCommerce site can highlight the price, reviews and availability of products, while a local business can make its opening hours and address immediately visible. Recently, the evolution of technology has profoundly influenced the role of structured data: Google has introduced specific new types of markup, AI-driven platforms are increasingly exploiting this information to improve the quality of generative responses, while some once-common features, such as rich results for FAQs, have been removed, making a strategic approach to the use of structured data even more necessary. This guide explores in detail what structured data is, why it has become an essential element for those who want to emerge online and how to integrate it effectively to optimize visibility and interaction with one’s audience. We will analyze tools, best practices and mistakes to avoid, with particular attention to the new opportunities offered by artificial intelligence and advanced semantic research.

What is structured data

Structured data is information organized in a standardized format that allows search engines to accurately interpret the contents of a web page. Unlike simple text, which requires more complex processing to be understood by Google, structured data provides clear indications about what a page contains: products, reviews, articles, events, people, companies and much more.

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This method of representation is the direct language with which websites communicate with search engines such as Google and Bing and is therefore a fundamental element for SEO optimization and for improving online visibility: by using the correct format, a site can obtain advanced results in search engines, activating features such as rich snippets, knowledge panels and interactive results that attract more clicks and improve the user experience.

In particular, Google uses structured data to understand the context of the information on a page in greater depth, and in this way it is able to catalog content more effectively, enabling new ways of displaying it in search engine results pages (SERP) and facilitating quick access to information by the user. For this reason, integrating them into your website is becoming an essential practice for both companies and publishers, especially in an era in which generative AI and advanced semantic search models are redefining user behavior and the way search engines return results.

Definition of structured data: what it means in brief

In the broadest sense, we are talking about information (data) that is organized in a way that makes it understandable to search engines (structured): in more technical terms, structured data is a standardized format that allows Google and other search engines to better navigate a site, to understand the relationships between pages and to obtain information to better understand and evaluate it, enabling visualization as a multimedia result in the search results.

To use the words of Google’s help guide on this topic, “structured data is a standardized format for providing information about a page and classifying its content”, so that the search engine algorithms can analyze the page itself starting from what they interpret as explicit clues about its meaning.

These small portions of code are called structured data because the information is organized according to a defined scheme, namely the famous vocabulary schema. org, which since 2011 has provided the rules for organizing the information found on publishers’ websites, the markup (computer language) used to define the entities of each type and the relationships between them, transforming content into data and, more precisely, metadata.

That is to say, information that is not directly displayed to visitors to the site, but is intended for search engines which, thanks to this language, can more easily understand what the images and contents of the tagged page are about, without the need for algorithmic interpretation, and display these contents with precision in the search results.

In practice, by correctly inserting this metadata, respecting the syntactic rules and the common reference structure, Google can understand the meaning of the information and, after analyzing it, return the best and most relevant results for the users’ queries.

In short, it’s a win-win situation for both the search engine and the websites.

The difference between structured, semi-structured and unstructured data

Not all data on the web follows the same pattern. Data can be divided into three main categories – structured, semi-structured and unstructured – depending on their level of organization and the ease with which they can be interpreted by machines and algorithms, as well as the way in which the information they communicate is found, collected and scaled and the type of database in which they reside.

• Structured data

Structured data is completely organized and follows predefined patterns that make it immediately interpretable by software and search engines. It is organized in relational databases, tables with specific values, or in standardized formats such as JSON-LD, XML and RDFa . For example, an eCommerce database contains dedicated columns with well-defined information about each product: name, price, rating, brand, etc. This allows this information to be quickly processed and presented in search results, as is the case with product rich snippets that directly show the details of a product without the user having to open the website page.

• Unstructured data

Unstructured data has no fixed organizational structure and does not follow a defined pattern. It is therefore much more difficult for a search engine or algorithm to analyze, because it must be processed through advanced AI and natural language processing (NLP) models to be interpreted correctly. This category includes all content that does not have organized metadata, such as free text, images, videos, emails or social media posts. Every day, most of the information generated online is unstructured: a review written on a blog without appropriate markup is an example of unstructured data, because Google will have to deduce its meaning based solely on textual analysis, without clear indications of its nature.

• Semi-structured data

Semi-structured data is something in between completely organized data and unstructured data. It doesn’t have a rigid structure like databases, but it does contain identifiable elements and predefined fields that allow for easier analysis by an algorithm. Examples of semi-structured data include JSON, CSV, HTML documents with metadata, and some types of emails that contain interpretable structures (such as electronic receipts or booking tickets). This data requires some level of processing before it can be fully utilized by search engines, but it is still more easily interpretable than completely unstructured data.

Understanding the relevance between structured data and unstructured data

Integrating structured data into your web pages means transforming information from unstructured or semi-structured into content that is immediately readable by search engines, improving their ability to interpret the meaning of the pages and facilitating positioning in search results.

It’s not a question of reasoning in terms of human understanding, but of machine understanding: structured data refers to organized data, while unstructured data identifies unorganized data. For example, if we write “Gennaro is the author of this article and works at SEOZoom”, we are not providing search engines with organized data: if it is easy for readers to understand the sentence and the information it contains (Gennaro is a human being, a copywriter, he works at SEOZoom and SEOZoom is a brand), for search engines this information is not equally immediate. They can understand and organize it, but with a little more “effort” because the data provided is “ambiguous” – Gennaro could be the name of the brand and SEOZoom a person’s name, to exaggerate: thanks to structured data, we can instead better define the information and clarify the “concepts” and entities for search engines.

One last digression. According to a page by IBM, true structured data, also classified as quantitative data, is that which follows the structured query language (SQL) developed by the same company in 1974, and is highly organized and easily decipherable by machine learning algorithms. Using a relational database (SQL), business users can quickly enter, search and manipulate this structured data.

Unstructured data, on the other hand, is typically classified as qualitative data: it cannot be processed and analyzed using conventional data tools and methods because it does not have a predefined data model, and therefore it can be managed in non-relational databases (NoSQL) or through data lakes, to store it in unprocessed form.

In this sense, structured SEO data would actually be semi-structured data (the examples mentioned are JSON, which we will discuss shortly, but also CSV and XML) and represent a “bridge” between structured and unstructured data: They do not have a predefined data model and are more complex than structured data, but easier to archive than unstructured data. Semi-structured data uses “metadata” (such as tags and semantic markers) to identify specific characteristics of the data and to scale the data into preset records and fields, and ultimately allows for better cataloging, searching and analyzing of information compared to unstructured data.

What is structured data used for?

Structured data allows search engines to process and interpret the content of a page more precisely, facilitating indexing and improving the presentation of search results. Without a clear and standardized architecture, a website risks being understood in an incomplete or inaccurate way, limiting the possibilities of obtaining visibility and interaction in the SERP.

Google, Bing and other search engines use structured data to identify key elements of a web page and organize them in a format that is readable by both users and automated classification systems. If a site provides clear information with precise markup, the search engine can recognize and differentiate content categories such as articles, products, reviews and events. This allows information to be presented in an advanced way in search results, increasing relevance to user intentions.

The strategic use of structured data is increasingly decisive in SEO today: as mentioned, Google values highly content enriched with correct markup, favoring the activation of advanced features in its SERPs. Furthermore, the growing integration of artificial intelligence in search processes amplifies the role of the semantic organization of data. With the rise of tools such as Google AI Overviews, SearchGPT or Bing Chat, providing structured data doesn’t just mean improving readability for the search engine, but becomes a method for adapting to the new mechanisms for generating and presenting results.

The impact is not limited to the search engine: integrating effective structured data also improves the user experience. Users get faster and more contextualized answers right in the search results, without having to navigate through multiple pages to find relevant information. The direct influence on visibility and interaction makes these tools indispensable for anyone trying to optimize online content, regardless of the sector.

Why search engines use structured data

The indexing of web content by Google and other search engines is based on an in-depth analysis of the pages, but the unstructured text does not always clearly identify the main elements of a document. Structured data provides precise information on the nature and attributes of a piece of content, reducing the need for complex algorithmic interpretations.

Thanks to this defined categorization, search engines are able to present results in an advanced form, using functions such as rich snippets, thematic carousels and knowledge panels. A clear example of this is the product pages in eCommerce: without specific markup, Google would analyze the text on the page to extract details such as price and availability, but the use of structured data allows communicating this information directly in a recognized format, increasing the precision and relevance of the response in SERP.

Google and Bing regularly implement new functions that leverage the semantic structuring of data. The evolution of SERPs has strengthened the importance of these tools, confirming their central role in making the web more organized and accessible, both for users and for advanced search technologies.

Structured data is a common system for providing information about a page and its content, which uses the schema.org vocabulary and generates different search features in Google.

In summary, search engines use structured data for three main purposes: to recognize the entities present on the page, to understand the relationships between these entities and, ultimately, to return the right answer to the user’s desired query.

Markups are useful because, as explained in detail, they help Google’s systems to understand the content on a site and on a page more accurately: this way, users get more relevant results for their queries and better understand how relevant these pages are for their searches. At the same time, if a site implements structured data it could be chosen by Google for a better visualization and enriched in the Search results (but obviously there is no guarantee of a direct link between the use of structured data and the actual presence in the search results, according to Google).

More generally, structured data is needed to ensure that machines can easily read, understand and classify this information, especially in light of the increasing evolution of technology and the expansion of the size and complexity of the Internet. Even if Google and other search engines continue to become more intelligent and more advanced, their resources are still limited in terms of time, processing power and energy to be distributed in all the activities necessary for their “routine”, and therefore providing them with more direct information is certainly a way to facilitate their tasks.

Many websites (even today) don’t use these tools and offer crawlers data retrieved only from database archives, formatted in HTML code that can be difficult to interpret; on the contrary, structured data simplifies life for crawlers, who use the information to better understand the core business or main topic of a website and thus improve the search results for that activity.

Automation in the interpretation of information

Artificial intelligence is transforming the way search engines understand digital content. In the past, the analysis of a page was mainly based on keywords and links, but today Google and Bing use advanced semantic models that interpret the connections between content and its context. In this scenario, structured data represents a bridge between raw information and algorithmic interpretation, making the classification of a page easier and more immediate.

The integration of this data into AI-powered search processes is now evident. The technology behind tools such as Google AI Overviews, for example, allows content from multiple sources to be aggregated and a summary generated that is adapted to the user’s query. In this process, pages with valid structured data have a higher probability of being selected as a reference to provide more precise answers in the results generated by artificial intelligence.

New search methods also require more efficient information management: AI models such as Bing Chat select authoritative sources based on the quality and clarity of the data provided. Organizing information correctly with valid markup allows you to compete more effectively in AI-based search results, making a site more visible in contexts that go beyond traditional text search.

The impact on SEO and online visibility

Optimization based on structured data offers concrete advantages in terms of visibility, although it does not directly affect ranking factors. Increased organic traffic and interaction rates are a direct consequence of the possibility of obtaining more detailed and relevant results. Google shows a clear preference for pages that provide readable data through Schema.org and similar standards, improving the chances of appearing among the visual elements of the SERP.

Increased CTR is one of the most obvious effects of using structured data correctly. Pages that activate rich snippets often receive more attention than standard results, with an increase in the number of clicks of up to 30%, according to case studies analyzed in the eCommerce sector. The reduction in bounce rate is another direct consequence: a user who sees clear details in the search results is more inclined to interact with the content, finding exactly what they were looking for without having to explore several pages blindly.

The effectiveness of structured data has also been demonstrated by large sites such as Rakuten, which implemented detailed markup to improve the readability of products in SERPs. Post-implementation analysis revealed a 35% increase in conversions and a 47% improvement in impressions on optimized products. This data highlights how structuring information has become a determining factor for website traffic and engagement.

Why use structured data: the main advantages

From everything we’ve written, we may have already realized the importance of structured data for a website, which is a useful tool for “talking” to search engines in a language they can understand.

If used well, structured data can support our SEO work because it makes it easier for Google to understand what the pages, products and website are about: Google’s job is always to understand the content of a page in order to give answers to users, and using structured data is like communicating directly with Google, providing the algorithms with explicit clues about the meaning of a page, which can potentially also help us in terms of visibility.

When appropriate, structured data changes the appearance of our snippets in Search, showing more – and more specific – information to users, who may then be more likely to click on our results. People appreciate rich snippets – which are detailed information about a web page that Google learns thanks to structured data – because they can immediately find out what ingredients are needed for a recipe, how difficult it is or how long it takes to prepare, and even how many calories the dish will contain; or they can find out the price of products and what people who have bought them think of them.

In short, if Google understands the markup of the pages, it can use this information to add rich snippets and other features to your search result. In other words, Google needs the structured data of a site and a page, and uses it to activate search results that can be more engaging for users, who may be encouraged to interact more with the website.

Google itself reveals the successful results of some case studies of websites that have implemented structured data for their site:

• Rotten Tomatoes added structured data to 100,000 unique pages and recorded a 25% higher click-through rate on pages with structured data compared to pages without structured data.
• The Food Network converted 80% of its pages to enable search results functionality and recorded a 35% increase in visits.
• Rakuten found that users spend 1.5 times more time on pages where structured data has been implemented compared to pages without structured data, and that the interaction rate is 3.6 times higher on AMP pages with search results functionality compared to AMP pages without this functionality.
• Nestlé found that pages shown as rich results in Search have an 82% higher click-through rate than pages not shown as rich results.

The benefits of markup for organic visibility

Some time ago, Search Advocate Daniel Waisberg published an article to explore the topic in depth, highlighting that using structured data on a site guarantees a richer search experience and can make the difference between positive and negative performance – also in light of new trends in user engagement on SERPs.

To explain the tangible benefits of using structured data, the Mountain View blog analyzes three concrete examples of sites that have benefited in terms of performance and ranking, namely Eventbrite, Jobrapido and Rakuten. In the first case, the event management and ticketing site

Eventbrite used structured data for event coverage and saw a 100% increase in traffic growth year on year from search.

The other case study mentioned by Google concerns the search engine for job searches, Jobrapido, which integrated with the job experience on Google Search and saw a 115% increase in organic traffic, a 270% increase in new user registrations from organic traffic and a 15% reduction in bounce rate for Google visitors of job pages. Finally, the Japanese giant Rakuten, as already mentioned, used the recipe search experience and generated a 2.7-fold increase in traffic from search engines and a 1.5-fold increase in session duration.

The three main direct benefits for a site

Even more interesting is the advice from Google on how to use structured data to obtain benefits for your online site, because the article lists some possible benefits that can be generated, summarized as an increase in brand awareness, content highlighting and product information highlighting.

In relation to brand awareness, the use of structured data allows you to take advantage of features such as the search box with logo, local business information and links to the site; in addition to adding the markup, you need to verify the site for the Knowledge Panel and claim the business on the Google Business Profile (formerly Google My Business).

Those who publish online content can take advantage of the many features that can promote articles and attract more users, depending on their sector; the list of rich results includes informative articles, breadcrumb trails, events, job offers, recipes, reviews and more.

The benefits can also affect eCommerce businesses that sell products, because the structured data on the page allows Google to immediately show information such as price, availability and review scores.

Benefits for users and improved search experience

Changing perspective, there are also positive effects for end users. Google’s goal is not only to classify web pages, but also to improve the browsing experience of people who use its service. Structured data makes this possible by providing contextualized and easily accessible information, reducing the time needed to get relevant answers.

The adoption of these technologies improves the quality of the interaction between the person seeking information and the search engine: a complete product sheet eliminates the need to click on several pages to obtain details on price and availability, while a well-structured article markup makes it easier to find specific content within a news portal. With the introduction of advanced generative search tools, the availability of clear and formalized data will increasingly influence the way information is processed.

Who should use structured data and why

The adoption of structured data is not limited to large portals or platforms with advanced resources: any website that wants to improve its visibility in search results and offer clearer and more immediate experiences to users can benefit from implementing schema markup. From small businesses to eCommerce, from blogs to news sites, the ability to provide structured information to search engines represents a real opportunity to increase online relevance and competitiveness.

Google’s accurate interpretation of content depends on the clarity of the information provided. A website that presents well-implemented structured data is more likely to be understood and valued in search results, with direct effects on the presentation of pages in the SERP. The possibility of appearing in advanced formats, such as rich snippets or thematic carousels, enriches the way information is displayed to users who perform a search, increasing the interaction rate and trust in the content shown.

In addition to affecting visibility in Google searches, the adoption of structured data has even broader applications. In the eCommerce sector, it facilitates integration with sales platforms and marketplaces, while in the management of company data it contributes to adaptation to the GS1 Digital Link and Digital Product Passport standards, which are fundamental for transparency and traceability in digital commerce. Its impact is also reflected in media and journalistic optimization processes, where well-structured information allows verified sources to be linked and improves the thematic classification of editorial content.

Which are the most beneficial businesses and sectors

The implementation of structured data translates into concrete advantages for various sectors, optimizing the way Google and users perceive content and improving its direct use from the SERP. Its usefulness is not only measured in terms of technical optimization, but also as a strategic lever to increase visibility and guarantee greater reliability of the data provided.

• eCommerce

On online sales sites, structured data is a key element for the promotion of products in Google searches. Through specific markup for product data sheets, information such as availability, price, brand, variants and reviews can be made immediately visible, improving the experience of those who explore the catalog directly from the search results. The adoption of data structures for product data sheets is not limited to individual pages, but extends to collections of articles, offering Google more effective tools to understand the classification of information and propose more coherent results. Companies that correctly integrate advanced markups see a positive impact on search performance, with increased user engagement and a potential increase in the conversion rate.

• Blogs and publishers

In the digital publishing sector, structured data is now an integral part of optimization strategies for Google News. The semantic organization of editorial content allows search engines to distinguish not only the nature of a page, but also its relationship with other articles on the same topic. The inclusion of pages in thematic carousels and in sections dedicated to informative content depends on the correct implementation of structured data. Google uses “Article” markup to quickly identify the publication and classify it among the relevant sources on a given topic, favoring a more effective distribution of articles based on user queries.

• Local business

Companies with a physical presence can take advantage of structured data to optimize their visibility in local SEO. Through the markup dedicated to local businesses, it is possible to provide updated details on location, opening hours, service categories and contacts, making it easier to find the business directly in the SERP. Integration with Google Maps and Google Business Profile listings becomes more effective when the company website adopts data structures consistent with the information already present in Google’s databases. In addition to improving local search rankings, this practice helps strengthen the consistency of the online brand, avoiding inconsistencies between different platforms.

• Industry and supply chain

In sectors that operate in supply chain management and large-scale retail, the adoption of data structuring standards meets traceability and transparency needs. The GS1 Digital Link and Digital Product Passport standards allow each product to be associated with a digital identity, simplifying access to key information related to characteristics, certifications, origin or disposal methods. These structured data systems are applied in eCommerce, logistics platforms and catalog management systems for distributors and retailers. Native integration with search engines and digital marketplaces guarantees competitive advantages in the presentation of certified products, offering greater accessibility to technical data by consumers and industry operators.

SEO and marketers: why work with structured data

Optimizing structured data is not only a technical necessity, but also a strategic lever for marketing professionals and SEO specialists who aim to improve the positioning of a website and increase its ability to attract qualified traffic. The evolution of SERPs and the increasing use of artificial intelligence in searches make it essential to adopt effective tools to ensure that information is interpreted as clearly and completely as possible.

Google is increasingly rewarding structured content, making it easier to classify and enhancing the value of content that offers detailed and easy-to-read information right from the search results. This has a direct impact on competitiveness, allowing a site to differentiate itself visually in the SERP compared to competitors and to obtain enriched snippets that capture the attention of users more effectively.

The role of structured data also extends to digital PR and social SEO strategies, facilitating the connection between information published on different platforms and ensuring greater semantic consistency between websites, news media and online company profiles. The possibility of integrating specific markups for events, press releases or information sheets helps to make media campaigns more effective, improving their distribution on digital channels and increasing the chances of appearing in knowledge panels or advanced results.

The effectiveness of these techniques is not only measured in terms of immediate visibility, but also in the opportunities for advanced analysis they offer. Google Search Console and SEO monitoring tools allow you to evaluate the performance of pages with markup and better understand how structured data affects search performance. This allows you to optimize your strategy over time, intervening on the most relevant markups and adapting implementations to the specific characteristics of the reference sector.

Integrating structured data correctly means not only improving the way search engines interpret web pages, but also favoring more effective content marketing strategies, with a measurable impact on growth and user interaction metrics.

What are structured data: types and use

Structured data comes in different forms and types, adapting to the specific needs of websites, eCommerce, blogs and information platforms. The choice of format and type of markup depends on the objectives of the site and the information that you want to make accessible to search engines.

Google has progressively refined its use of structured data, introducing new features and removing markup that is no longer relevant. The removal of FAQ Rich Results and How-To Results from SERPs, for example, marked a turning point in the way Google handles structured information, while the addition of schemas such as Vehicle listing, Discount offers and 3D Model demonstrated an evolution towards greater support for e-commerce and transactional searches.

Understanding the categories of structured data available allows you to adopt the most suitable solutions to improve the readability of the site, activate advanced results in the SERP and guarantee greater relevance for the target audience.

Formats supported by Google

Structured data can be implemented in different formats, each with characteristics that influence its effectiveness and ease of management. Google has officially stated that it prefers one format over the others, but it also continues to support alternatives used by specific CMSs and platforms.

• JSON-LD

The JSON-LD (JavaScript Object Notation for Linked Data) format is the solution suggested by Google for implementing structured data. This standard allows you to insert structured data within the page code without directly modifying the HTML, simplifying the management and debugging of the markup.

From a technical point of view, it is a JavaScript notation embedded in a <script> tag in the <head> and <body> elements of an HTML page; this markup is not interlaced with the text visible to the user, which makes it easier to express nested data elements, such as the Country element of PostalAddress of MusicVenue of Event.

One of the strengths of JSON-LD is its flexibility: structured content can be easily managed through dynamic scripts, allowing automatic update systems to modify data without having to intervene on the page’s source code. Furthermore, Google can read JSON-LD data when it is dynamically inserted into the page’s content, for example through JavaScript code or widgets incorporated in the content management system. These characteristics make it particularly suitable for eCommerce, publishing platforms and sites that frequently publish updated content.

• Microdata

Microdata is an alternative that directly incorporates structured data elements into the HTML of the page. This method associates each piece of structured information with specific visible elements of the site, nesting the markup within the tags already present in the code.

From a technical point of view, it is an open-community HTML specification used to nest structured data within HTML content, using the attributes of HTML tags to name the properties that we intend to expose as structured data. It is generally used in the <body> element, but can also be used in the <head> element.

Although this approach guarantees a close connection between content and data, it is more complex to manage in dynamic contexts. Each change requires direct intervention in the page code, increasing the risk of errors and misalignments between the data shown to the user and that interpreted by the search engine.

• RDFa

RDFa (Resource Description Framework in Attributes) is a solution that extends HTML5 to enrich the information on a page with structured data. It uses attributes of HTML tags that correspond to the content visible to the user that we intend to describe for search engines, commonly used in both the <head> and <body> sections of the HTML page. This approach allows for expanding the semantic link between the elements of the site, favoring a clearer interpretation of the contents by search engines.

Despite its scalability and potential effectiveness in knowledge graph contexts, the adoption of RDFa is less widespread than JSON-LD, mainly due to the greater complexity of integration and markup management. Google continues to support it, but encourages the use of simpler and more flexible alternatives.

The most used markups and activated features

The use of structured data is not limited to its technical implementation, but above all concerns the features that can be activated in search results. Google analyzes the content of the page and, in the presence of valid markup, can generate SERP enriched with visual and informational elements that improve the user experience and increase the visibility of the site.

The updated standards have led to the removal of some previously widespread features, such as FAQ and How-To rich results, while expanding support for e-commerce and digital identity management schemes.

• Article

Article markup is designed to improve the classification and presentation of blog articles and editorial publications. It helps to clarify the content structure, making it easier to recognize the title, author, publication date and other metadata.

Used correctly, it allows you to activate advanced results in Google News Carousels, increasing the coverage of content in thematic searches and in areas dedicated to news.

• Product and Review

For eCommerce, structured data associated with products allows search engines to understand in more detail the characteristics of the offers available on the site. The Product markup allows you to specify the name, price, availability and description of a product, while Review allows you to add ratings, scores and user reviews, highlighting this information in the SERP.

The combined use of these elements improves the effectiveness of transactional search and allows Google to generate more relevant listings for buyers, encouraging qualified traffic to sales pages.

• Breadcrumbs and LocalBusiness

Structured data dedicated to site navigation and local businesses improves the structure of the pages and optimizes their presentation in search results related to geolocated searches and navigation paths.

The BreadcrumbList markup helps define the hierarchy of a site’s pages, allowing intuitive paths to be displayed directly in organic results. The LocalBusiness markup, on the other hand, allows businesses to provide Google with verified information on location, opening hours, contacts and services offered, improving the management of their local online presence.

• Recipe

The Recipe markup is dedicated to sites that publish content related to cooking and recipes. Google uses this data to build interactive results that highlight ingredients, preparation times, recipe difficulty and nutritional information, improving the usability of the pages for users looking for detailed cooking instructions.

New markups and features made available by Google

Recently, Google has introduced new schemes that respond to the emerging needs of the eCommerce and online sales sector, allowing retailers to optimize the presentation of product listings and manage promotions with greater visibility in search results.

• Vehicle listing

The Vehicle listing markup allows companies operating in the automotive sector to present vehicles for sale with detailed listings directly in the search results. The data can include make, model, year of production, mileage and any special offers, making it easier for users interested in buying a car to compare multiple options.

• Discount offers

The inclusion of the Discount offers markup is a significant step towards optimizing promotional campaigns in search engines. This scheme allows you to associate discounts and offers with specific products and services, with details updated in real time that improve the visibility of promotions directly in the SERP without the need for paid campaigns.

• 3D Model for products

The introduction of support for 3D models in search results offers a more immersive experience for users who want to examine products before purchasing. This markup is essential for companies operating in sectors such as technology, furniture and fashion, where three-dimensional visualization can influence the purchase decision.

How to implement structured data on the site

Inserting structured data on a web page requires a precise approach, which can vary depending on the level of control, technical skills and the need to update the site.

In general, structured data on the eeb uses Schema.org as its reference vocabulary; implementation on pages can be done in different ways, such as directly modifying the HTML code, using plugins and automated tools, or relying on artificial intelligence to generate the markup.

To obtain the best results, it is necessary to ensure that the markup complies with the standards defined by Google and Schema.org, avoiding syntactic errors or unsupported attributes. In this sense, we can rely on tools such as Google’s Structured Data Markup Helper. Even a formally correct configuration may not bring SEO advantages if the data provided is not consistent with the content of the page or if Google does not consider it relevant for the activation of advanced results in the SERP.

We can choose to add the code to the pages of the site manually if we know how to work with the code and are familiar with the process of creating and adding structured data to the site; alternatively, we can use a dedicated plugin, which does not require technical knowledge of the code, but still requires an understanding of SEO and how semantic markup works, as well as a general review of the content and management of mandatory fields. Many of these plugins automatically add the most important data for the type of site and we can select and determine what type of content is present per page or article, and then describe the page in the most appropriate way for search engines with valid structured data.

A fundamental step is to verify the code before publication: tools such as the Google Advanced Results Test and the Schema.org Validator allow you to identify any problems and ensure that the data is structured in the most effective way possible.

Furthermore, to simplify operations even more, Google provides lists of structured data to copy, customize and paste on your website, but also on schema.org you can find examples complete with documentation. There are scripts to insert to implement structured data for various types of information or property types, such as breadcrumbs, bullets, site name, site links, contacts, social profiles, logos, courses, reviews, videos and scientific information (among the most recent items).

Adding structured data: manual insertion in HTML code

Directly inserting structured data into the page code is a method that allows maximum freedom and control, but requires advanced knowledge of Schema.org syntax and best practices. Among the various formats available, Google has stated that JSON-LD is its preferred format, as it is separate from visible content and easier to modify than alternatives such as Microdata and RDFa.

Manual implementation of JSON-LD markup typically takes place in the <head> of the page or before the closing of the <body>. For example, for a product sheet in an eCommerce site, the code could be structured in the following way:

<script type=”application/ld+json”>

{

  “@context”: “https://schema.org/”,

  “@type”: “Product”,

  “name”: “Smartphone XYZ”,

  “image”: “https://www.site.com/images/smartphone.jpg”,

  “description”: “Latest generation smartphone with triple camera and long-lasting battery.”,

  “brand”: {

    “@type”: “Brand”,

    “name”: “XYZ”

  },

  “offers”: {

    “@type”: “Offer”,

    “price”: “399.99”,

    “priceCurrency”: “EUR”,

    “availability”: “https://schema.org/InStock”

  }

}

</script>

By manually entering this JSON-LD code, Google can accurately interpret crucial product details such as name, price, availability and image.

In addition to directly writing the markup, it is possible to validate the syntax before publication using dedicated tools. The Google Advanced Results Test allows you to verify if the code is readable and compatible with the search functions, while the Schema.org Validator allows you to check the correct application of the JSON-LD format.

How automation works with AI and dedicated tools

Manually adding structured data is an effective solution for those who need direct control over the markup, but it can be challenging for sites with many pages or frequent updates. In these cases, using AI-based tools or automated scripts simplifies code implementation and management.

Artificial intelligence has made it possible to automatically extract and generate structured data, improving the accuracy and scalability of its implementation. AI-based tools, such as WordLift, automatically analyze the page content and generate the most appropriate JSON-LD markup.

These systems work through machine learning models that recognize key entities within the text, such as product names, authors, dates and reviews. After identifying these elements, the AI is able to structure the data and integrate it directly into the code of the page, without the need for manual intervention.

One advantage of this approach is the continuous optimization of the markup, thanks to the AI’s ability to dynamically adapt the data based on changes to the site and updates to Google’s algorithms. This makes AI-based tools particularly useful for news organizations, large content portals and fast-growing sites.

Among the tools available for adding structured data, we should also mention Data Highlighter, accessible via Google Search Console, a function that allows you to mark relevant information directly in the tool’s interface, without the need to modify the site’s HTML code.

Its use is based on visual interaction: the webmaster selects the elements of a page (for example, the title of an article, the name of a product or the date of an event) and assigns them a label corresponding to the type of data supported. Google interprets these annotations and uses the information to improve the display of the site in search results.

However, Data Highlighter has some limitations that make it less effective than directly inserting JSON-LD markup. Google can only interpret pages that have already been scanned, which means that any changes or newly published content may not be processed immediately. Furthermore, using this solution is only beneficial to Google, while other search engines such as Bing or Yahoo will not be able to use the same information.

Python for advanced implementation

For those who manage sites with many dynamic pages, automation can be taken to a more advanced level using Python and specific libraries for the dynamic generation of structured data.

Libraries such as BeautifulSoup allow you to extract textual information from existing HTML pages, while with jsonschema you can structure JSON-LD markup in a way that conforms to the standards required by search engines. This type of implementation is particularly useful for eCommerce portals, review sites and platforms that need to create custom schemas without having to write them manually for each individual page.

A practical example of automated JSON-LD generation with Python:

import json

structured_data = {

    “@context”: “https://schema.org”,

    “@type”: “BlogPosting”,

    “headline”: “How to generate structured data with Python”,

    “author”: {

        “@type”: “Person”,

        “name”: “Mario Rossi”

    },

    “datePublished”: “2025-02-15”,

    “mainEntityOfPage”: {

        “@type”: “WebPage”,

        “@id”: “https://www.site.com/article”

    }

}

json_ld = json.dumps(structured_data, indent=4)

print(json_ld)

This script allows you to dynamically generate the markup for blog articles, automatically customizing the title, author and other details. Once integrated with a CMS system, the JSON-LD code can be populated dynamically for each new piece of published content.

How to verify structured data and debug the code

As mentioned, implementing structured data doesn’t end with its integration into the site, but requires constant verification and optimization in order to respond to the evolution of search algorithms and make the most of the opportunities offered by Google and other search engines.

In short: after adding the structured data it is essential to test the validity of the code before publication to avoid errors that could compromise its interpretation by Google. Failure to comply with standards can in fact make the markup ineffective and prevent the activation of advanced functions in the search results.

Google provides specific tools that allow you to verify if the JSON-LD, Microdata or RDFa markup has been applied correctly and if all the required attributes are present.

• Testing Google’s advanced results

This tool allows you to test a page or a code fragment to verify which structured data Google is able to read and interpret. The system reports any errors, highlighting missing attributes or properties that could make the markup unsuitable for activating certain features in the SERP.

Using it before publication is a fundamental step to avoid implementation problems that could nullify the benefits of structured data.

Structured data can be added to a page using the schema.org vocabulary or by tagging the data on the page using the Google Data Highlighter tool, and currently nine data categories are supported, to define articles, events, local businesses, films, products, restaurants, software applications, TV episodes and books.

In addition to the official testing tools, remember that Google’s Data Highlighter can also be an alternative option for signaling structured information without having to modify the page code. Although it can be useful in some cases, this solution is not a substitute for direct implementation in JSON-LD and may not guarantee constant recognition of the information by the search engine.

• Schema.org Validator

The official Schema.org tool allows you to analyze your JSON-LD code in detail, showing possible syntax problems and suggesting corrections to improve the markup’s compatibility. Unlike Google’s Rich Results Test, which only evaluates the schemas supported by the search engine, the Schema.org Validator verifies compliance with the entire vocabulary that can be used by search engines and semantic applications.

Integrating these checks into the normal procedure for publishing structured data helps to ensure greater accuracy and reliability of the markup, avoiding penalties or the failure of advanced results to be displayed in the SERP. In the presence of errors, quickly correcting the code prevents unnecessary loss of opportunities in terms of visibility and user interaction.

The evolution of structured data and the role of AI

The adoption of structured data is no longer limited to simple SEO optimization and its use is evolving rapidly thanks to integration with artificial intelligence technologies, knowledge graphs and search systems based on generative AI.

In recent years, Google and other search engines have accelerated the development of algorithms capable of understanding the deeper meaning of online information, going beyond simple text indexing. In this scenario, structured data is a fundamental pillar because it provides a clear semantic framework that can be interpreted by AI, improving the quality and reliability of search results.

New retrieval-based search and generative AI technologies have changed the way information is processed. Tools such as Google AI Overview and Bing Copilot don’t just show text results, they generate semantic answers based on structured data sets and logical connections between content. The construction of intelligent models capable of connecting different information requires the adoption of clear, verifiable and classifiable data patterns, which only a solid structuring of the contents can offer.

The evolution underway demonstrates how structured data is no longer simply a way to enrich a web page with metadata, but an essential tool for making information machine-readable and inserting it into a larger ecosystem, powered by AI. Integration with knowledge graphs and advanced search will lead to an increasingly refined use of semantic markup in the coming years, with a significant impact on the visibility and reliability of digital sources.

The intersection of AI, knowledge graphs and structured data

The application of artificial intelligence in the field of structured data is based on a model of connection between entities, content and semantic relationships. Search engines no longer operate as simple aggregators of textual results, but use knowledge graphs, semantic ontologies and advanced data retrieval models to generate more articulated and contextualized responses.

The integration of structured data and knowledge graphs allows us to build an information infrastructure in which the relationships between different concepts are processed with greater precision. A significant example is Graph-based Retrieval Augmented Generation (Graph RAG), a model that combines knowledge graphs with retrieval-based search systems, improving the AI’s ability to generate answers based on structured and interconnected sources.

The use of Graph RAG and knowledge graphs overcomes the limits of classic textual research, offering contextualized and verifiable information. AIs that adopt this model construct more detailed answers because they can access semantically organized content, identifying connections that wouldn’t emerge in traditional research.

The role of semantic ontology in Large Language Models (LLM) is increasingly central. Current AI works on a probabilistic and predictive basis, but the absence of well-defined structured data can lead to inaccurate answers or information lacking in contextualization. Integration with a well-constructed ontology reduces the risk of ambiguity, ensuring that information is interpreted and selected correctly before being included in search results.

The future with artificial intelligence

The adoption of AI in search engines is not only redefining the criteria for classifying content, but is also increasing dependence on structured information to generate more reliable and transparent answers. AI-powered search systems are no longer limited to keyword-based searches, but process interconnected data sets to offer more articulated information solutions.

The most significant evolution can be seen in generative response systems such as Google AI Overview and Bing Copilot, which no longer rely exclusively on simple document searches, but analyze structured data to compose complete and detailed answers.

Google AI Overview uses structured data to extract entities, connect concepts and generate informative content based on reliable sources. The difference compared to traditional search results is clear: while SERPs used to be limited to showing a list of relevant links, today the information can be directly synthesized and presented in the form of AI-driven answers.

Bing Copilot follows a similar approach, integrating knowledge graphs and structured data to build complex answers, reducing the need for the user to navigate through multiple pages to find relevant information. This trend suggests that content that does not adopt structured data runs an increasing risk of remaining invisible in the new AI-based search interfaces, which favor sources optimized for semantic understanding.

The future of digital optimization is increasingly shifting towards a synergy between AI and structured data. In the past, structured data was considered as simple additional metadata to enrich SERPs, but today it is clear that its function is becoming a key element in making content interpretable by artificial intelligence and inserting it into a completely new search environment. The way Google and Bing exploit this data shows that future searches will not only be based on keywords and positioning, but on a broader concept of semantic relevance and relationships between digital entities.

How to use structured data well: strategies and best practices

Implementing structured data is a powerful tool for improving the understanding of and indexing of content, but its effectiveness depends on the quality of the markup and its consistency with the standards required by Google. It is not enough to add a schema without criteria: it is necessary to adopt a precise strategy that takes into account the specific characteristics of the site, the relevance of the information and the opportunities offered by web semantics.

The correct use of structured data is not limited to the activation of advanced SERP functionalities, but can also favor better integration with content marketing strategies, improve the connection between entities and enhance the distribution of information through knowledge graphs. To maximize the impact of markup, it is essential to follow precise guidelines, optimize the semantic structure of the site and ensure that each implemented schema is consistent with the actual content of the page.

Advanced optimization to improve SERP performance

Simply implementing structured data is not enough to guarantee an improvement in performance in the SERP. It is essential to refine the markup strategy to optimize the click-through rate (CTR) and improve the quality of the information provided to search engines.

A decisive aspect is the choice of structured data based on the site’s objectives. An eCommerce site will benefit more from integrating Product and Offer markups, while a blog or editorial site should focus on Article and Author elements to improve readability by Google News. In addition to the type of data used, it is essential to optimize its semantic layout within the page to ensure that the entire structure is consistent with the textual context.

One of the most effective tools for consolidating brand identity and improving search reputation is the correct use of the sameAs property. This attribute allows an entity or brand to be linked to its official profiles, avoiding ambiguity and favoring its appearance in Google information sheets. If used correctly, it can help consolidate the site’s digital identity and improve its connection with verified knowledge panels.

Integration with corporate knowledge graphs represents a further advanced level of optimization. Structuring data so that it can be interpreted and semantically linked to existing information networks allows you to expand the validity of the site as a reliable source, increasing its potential exposure in search engines. Effective management of this information involves targeted planning of the markup and the use of verified and recognized datasets within existing semantic networks.

Quality standards for structured data

Obviously, then, there are more general rules regarding the correct approach to implementing structured data on our pages, which for example must not violate the classic guidelines of Google Search for content, including those relating to spam, as listed in the quality standards.

1. Content.

• Provide up-to-date information, because Google will not show rich results for outdated content that is no longer relevant.
• Provide original content generated by us or our users.
• Do not mark up content that is not visible to page readers: for example, if JSON-LD markup describes an artist, the HTML body must describe the same artist.
• Do not mark up irrelevant or misleading content, such as fictitious reviews or content unrelated to the topic of a page.
• Do not use structured data to deceive or mislead users; do not steal the identity of people or organizations, and do not misrepresent our ownership, affiliation, or primary purpose.
• Structured data content must also comply with additional content guidelines or standards, as documented in the specific feature guide. For example, content in JobPosting structured data must comply with the standards for job posting content. Content in Tutorial structured data must comply with the guidelines for Tutorial content.

2. Relevance. Structured data must be a faithful representation of the page’s content, and Google lists the following as examples of irrelevant data:

• A live sports streaming site that labels broadcasts as local events.
• A woodworking site that labels instructions as recipes.

3. Completeness.

• Specify all mandatory properties listed in the documentation for your specific type of rich result; items without specified mandatory properties are not eligible for rich results.
• The more recommended properties we provide, the higher quality results users receive. For example, users prefer job postings with explicit salary information to those without, and recipes with reviews from real users and authentic star ratings (reviews or ratings from non-real users may result in manual action). The ranking of the advanced results takes additional information into account.

4. Location.

• Enter the structured data on the page that describes it, unless otherwise specified in the documentation.
• If you have duplicate pages for the same content, we recommend that you enter the same structured data on all of the duplicate pages, not just on the canonical one.

5. Specificity.

• Use the most specific type and names of applicable properties defined by schema.org for markup as much as possible.
• Follow any additional guidelines provided in the documentation for the specific type of advanced result.

6. Images.

• When specifying an image as a property of structured data, it is important that the image is relevant to the page it is on; for example, if we define the image property of NewsArticle, the image must be relevant to the news article in question.
• All image URLs specified in structured data must be crawlable and indexable; otherwise, Google Search cannot find and display them on the search results page.

7. Multiple elements on a page.

Multiple elements on a page means that there are multiple types of elements on the page: for example, a page could contain a recipe, a video showing how to make it, and breadcrumb information about how users can find the recipe. All this information visible to users can also be marked up using structured data, thus allowing search engines such as Google Search to more easily understand the information on a page. If we add more elements to a page, Google Search can get a more complete picture of the topic it covers and display it in different search features.

Google Search understands multiple elements on a page, both when nested and when each element is specified individually, and in particular:

• Nesting. When there is only one main element and additional elements are grouped under that element – a particularly useful solution for grouping related elements, such as a recipe with a video and reviews.
• Individual elements. When each element is a separate block on the same page.

Guidelines to avoid common mistakes

For structured data to be effective, it must be interpreted correctly by search engines and must comply with Google’s official guidelines. Each markup has required and recommended properties, and it is essential to respect all technical specifications to prevent Google from ignoring or penalizing incorrect implementations.

A common mistake is using generic markup or markup that is not relevant to the actual content of the page. Applying structured data that is inconsistent with the information actually visible to users can lead to the removal of content from the advanced SERPs or, in more serious cases, to manual penalties. The syntax must always be valid and conform to recognized Schema.org schemas: invalid codes or incorrect formatting can be useless and frustrate implementation.

It is also necessary to pay attention to the constant updating of the markup. Google regularly modifies its support for certain types of structured data, as demonstrated by the recent removal of FAQ rich results. To avoid using deprecated or no longer supported schemas, it is advisable to frequently monitor the official guidelines and update the code according to the latest practices.

Mistakes to avoid when using structured data

Although it is a key element of search engine optimization, the incorrect implementation of structured data can generate technical problems or, in the most serious cases, compromise the credibility of a page in search results. For a markup to be effective, it must not only be valid from a syntax point of view, but also respect the standards of quality and transparency established by Google.

Common mistakes can be divided into two main categories: technical issues related to code formatting and bad practices from an SEO point of view, which can lead to ranking loss or manual penalties.

What are the most common technical issues?

One of the most common mistakes is incorrect code formatting, which can prevent Google from reading and interpreting the data correctly. JSON-LD, being the recommended format, is less prone to nesting errors than Microdata and RDFa, but it is still essential to validate the code before publication using tools such as Google’s Rich Results Test.

Even formally correct syntax may not be functional if the site structure prevents Googlebot from accessing the data. The use of structured data on pages blocked by robots.txt or with the noindex attribute makes it impossible for search engines to interpret them, nullifying any possible SEO benefit. A site crawl verification, carried out periodically using tools such as Google Search Console, allows you to identify these kinds of problems and correct them promptly.

Another common technical error is the use of structured data without complete information, with essential properties omitted or entered ambiguously. Some of Google’s advanced results require mandatory fields for the rich snippet to be displayed in the SERP: incomplete compilation of the markup could limit its effectiveness or prevent the features from being activated.

Improper SEO practices that can penalize the site

Improper use of structured data can be a counterproductive strategy, to the point of causing Google to remove certain pages from the SERP or apply manual actions.

One of the most serious mistakes is the insertion of misleading structured data, i.e. the practice of marking content with information that does not correspond to the reality of the page. If a site applies the Review markup to products and services that do not actually have reviews, or uses the Event template for non-existent events, it risks violating Google’s guidelines and losing the possibility of appearing in the advanced results.

Similarly, the abuse of irrelevant or forced markups can generate negative effects: using structured data that is irrelevant to the content offers no SEO benefit and could even cause the search engine to ignore the markup throughout the site.

Violations of the guidelines can lead to more severe penalties if Google considers the structured data to be part of an opaque strategy to alter search results. Google’s official documentation recommends implementing only schemas that accurately reflect the actual content of the page, avoiding any attempt at algorithmic manipulation. In case of manual penalties, Google Search Console notifies of the error and requests corrections immediately, but restoring visibility can take time and in-depth technical interventions.

The adoption of structured data, therefore, must follow criteria of quality and correctness, guaranteeing that every piece of information transmitted is verifiable and actually present on the page. The best approach is to maintain a balance between optimization and authenticity, providing Google with clear metadata that is consistent with the content actually offered to the user.

The main errors to avoid for on-page markup

To sum up, we have said that structured data is becoming increasingly important for Google, which pays specific and rather constant attention to these markups: the Search system is based on trying to understand the content of a web page as best as possible, and structured data is a means of providing Google with explicit indications on the meaning of a webpage.

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However, as it is a rather technical subject, it is possible to make some errors that can damage the strategy or in any case make the work carried out useless. Below we will try to list the main problems with structured data, focusing in particular on some technical issues and one of a more theoretical nature, so to speak.

Basically, Google makes it clear that if our page contains a problem related to structured data, this may require manual action: unlike the “general” ones, in this case there are no effects on the ranking of the page in Google Search, but the page loses its eligibility for display as a multimedia result.

1. Not understanding the value of structured data.

The first error is the one we mentioned when talking about theory: it could be easy to misinterpret the value and meaning of the structured data implemented in a web page, and consequently to evaluate its use incorrectly.

Google itself specifies a central point: “The use of structured data enables its appearance, but does not guarantee it”, and furthermore “there is no guarantee that your page will appear in search results with the specified function”. In short, it is always Google that decides if and when to show structured data.

This means that search results will always be shown based on Big G’s interpretation of its algorithm and its attempt to offer the user the “best search experience”, taking into account “many variables, including search history, location and device type”. Therefore, the algorithm can “in some cases determine that one feature is more appropriate than another, or even that a simple blue link is more appropriate”, or it can favor the page of a competitor site instead of ours.

2. Making syntax errors.

The syntax for correctly marking structured data is rather rigid and complex, and therefore it is not uncommon to run into some compilation difficulties or to forget to add a required or recommended property. One of the most frequent errors is to skip a necessary comma, or to ignore case sensitivity, the distinction between upper and lower case letters to which the JSON-LD language is subject.

To avoid this error, you can use Google’s tools for testing structured data, which allow you to verify the correctness of the information during development, or the reports on the status of multimedia results after deployment, which allow you to monitor the status of the pages.

3. Misuse of structured data.

As we have tried to explain, structured data should be a “faithful representation of the page’s content”, of the markup that helps Google show more useful and accurate results to users. It follows that inserting structured data that “is not representative of the main content of the page or is potentially misleading” is a serious mistake, because it goes against the very principle of the tool, just as the official guide of the search engine warns us against creating empty pages just to include structured data and against adding structured data about information that is not visible to the user, even if accurate.

Among the cases of irrelevant data, Google cites two off-limits examples, namely “a live sports streaming site that labels broadcasts as local events” or “a woodworking site that labels instructions as recipes”. Other incorrect uses of markup are to create empty pages just to contain structured data or to add it “on information that is not visible to the user, even if it is accurate”. However, it seems clear that acting in this way gives rise to distortions that are not useful for on-page optimization.

4. Blocking access by Googlebot.

The fourth point is also very technical: a common mistake, however naive, is to block access by Googlebot to pages implemented by structured data using one of the control methods such as robots.txt, noindex or other systems. Clearly, this prevents the correct scanning of the content and, in practice, makes structured data useless, as it can neither be used by Google for indexing nor shown in SERP.

5. Common mistakes by structured data category.

In the guidelines on the subject, Google also presents a useful list of common errors, problems and malfunctions of structured data, divided by markup category.

• For example, in the events category there are two types of errors: using markup on the page but not actually having content relating to visible events, or using text that appears to be “aimed more at promoting or selling the event than describing it”. The problem of presenting markup that has no relevance to the content also occurs with the recipes category
• Content on the page does not correspond to the markup. In the field of job offers, on the other hand, there are more errors: in addition to the generic one of inconsistency between the markup and the content on the page, Google also mentions the impossibility for the user to apply for the job and the mismatch between the markup and the job description visible to the user. Also considered serious are the presence on the page of misleading job documentation and the poor quality of the offer (i.e. if “payment is required to submit the application or the job seems to be fake”).
• Problems with structured data for lists and products. Among list items, it is incorrect to treat the various items as a single element when assigning object properties: in particular, “marking up a single entity of the category among those listed on the page goes against our guidelines”, Google emphasizes. Therefore, you must avoid assigning a single review rating or position to a list of items, just as you should not consider lists as single items. The indications on products and their reviews are also very specific. First of all, there are a series of precise rules for indicating the name of the product, which must not be identified simply by the brand of the manufacturing or sales company or by a description of it: the indication Nexus 5X is valid, says Google, but not “Android Phones” or “Best Selling Nexus Phones”.
• Regarding the markup of reviews there are other stringent recommendations: a review written by the site or by the person providing the product or service is considered wrong, while those made by a customer or by an independent and unpaid reviewer are accepted. Furthermore, if a page shows reviews, it must also offer users the opportunity to submit their own opinion, with the sole exception of a single review by a recognized author.

Structured data: FAQs and frequently asked questions

The use of structured data has become an essential practice for improving visibility in search engines and optimizing content ranking. However, many webmasters and SEO professionals still have doubts about its implementation, the real SEO implications and the best strategies to adopt.

In this section we answer the most common questions about structured data, clarifying the fundamental concepts and providing useful advice for its correct and strategic use. This concluding overview helps to consolidate the knowledge acquired and to avoid common mistakes that could compromise the effectiveness of these tools in improving online visibility.

• What is meant by structured data?

Structured data is information organized according to a standardized format, designed to be easily interpreted by search engines. Thanks to it, Google and other search engines better understand the meaning of a page and the context of the information.

• What is the difference between structured and unstructured data?

Structured data follows precise and easily processable schemes (such as tables and JSON-LD markup), while unstructured data lacks clear formatting and requires advanced AI and Natural Language Processing to be interpreted.

• What is the difference between structured, semi-structured and unstructured data?

Structured data has a rigorous and standardized structure, semi-structured data contains partially organized elements (such as JSON and XML), while unstructured data has no fixed schema (such as images, videos and unmarked texts).

• What is structured data in computer science?

In programming and database management, structured data refers to information organized in tables or records that can be easily queried using languages such as SQL. On the web, on the other hand, it is represented using HTML markup or JSON-LD scripts.

• What type of data is a web page?

A web page can contain structured, semi-structured and unstructured data at the same time. The information in linked databases is generally structured, but the texts, images and videos on the pages are often unstructured, unless they are accompanied by appropriate markup.

• What is structured data used for?

They make the content of a web page more understandable for search engines, improving the classification of information and allowing advanced formats to be activated in search results.

• What is unstructured data?

It is information without specific formatting recognizable by search engines, such as images, videos, free texts or non-standardized data records.

• What is the difference between simple data and structured data?

Simple data is a single value (e.g. a number or a string), while structured data is organized within a system that defines relationships and categories, making it more easily interpreted by software and algorithms.

• How do you define a set of data that is processed or structured in a way that provides meaning and usefulness?

A structured dataset is organized in a way that allows for clear and logical processing, facilitating analysis and use. It is the principle on which any database or advanced information categorization system is based.

• Why is structured data important?

They help create more informative and visually appealing results in search engines, such as rich snippets, carousels and information panels, making content more accessible and understandable.

• What is the main advantage of structured data?

They increase the likelihood that a web page will obtain an advanced presentation in the search results, improving the click-through rate (CTR) and facilitating access to key information already in the SERP.

• Does structured data directly influence ranking?

No, Google has stated that they are not a direct ranking factor, but they can increase CTR and improve user experience, elements that can indirectly influence the performance of a page in search engines.

• What are the most important types of structured data for SEO?

It depends on the sector and the type of content. Among the most used are Article, Product, Review, FAQ, LocalBusiness, each designed for specific categories of information.

• What are the types of structured data?

Schema.org has defined numerous types of structured data that can be classified into different categories, such as information on products, events, places, reviews and much more. Their function is to allow search engines to better interpret and display relevant information.

• How do you implement structured data?

You can add it manually by directly writing the JSON-LD code, using plugins for CMS such as WordPress or using dedicated online tools to automatically generate the markup.

• What is the difference between JSON-LD, Microdata and RDFa?

JSON-LD is an external JavaScript script that is easier to manage, whereas Microdata and RDFa are markups that must be integrated directly into the HTML code of the page, making them less practical to implement and update.

• Is JSON-LD better than Microdata or RDFa?

Yes, Google recommends JSON-LD because it is easier to implement, update and manage, as well as being more flexible in terms of integration with CMS and dynamic systems. Microdata and RDFa are still supported, but they are more complex to modify and less performing.

• How can structured data be inserted?

It can be inserted directly into the HTML of the page, in the <head> or in the <body>, or it can be dynamically generated via server-side scripts that automate the integration of JSON-LD markup.

• What are rich snippets?

Rich snippets are advanced search results that contain additional structured information, such as star ratings, prices, images and other specific details extracted from the structured data of the page.

• Why did Google remove FAQ Rich Results?

Google has reduced the visibility of FAQ Rich Results to limit the presence of redundant markup and repetitive results in the SERP. Now they are only shown in very specific cases and for highly authoritative sources.

The evolution of search strategies has led to greater selectivity in Google’s use of structured data. The most recent changes suggest optimization aimed at the effectiveness of the content rather than the accumulation of generic markups. Adapting to these changes means focusing on strategic and relevant implementations, ensuring consistency with the new search logic.