Semantic data

Semantic data is a way of thinking about and organizing data that puts meanings and context first. It makes it easier for both people and machines to understand and use.

Instead of isolated data points, semantic data creates a knowledge graph which should reflect real-world concepts (usually ones relevant to your organization or domain). This allows for complex queries and insightful analysis. It's a shift from data storage to knowledge representation.

What’s in it for you?

• Semantic data can improve data-driven decision making. Many businesses struggle with fragmented data spread across different departments and systems (e.g., "customer" in sales might be "client" in logistics and "counterparty" in finance). With semantic layers across — a core feature of semantic data — you have an intermediary that can translate disparate definitions into a unified, consistent view.

• It can simplify data access for non-technical users. Semantic data abstracts the complexity of underlying data structures. This means non-technical users won’t need to write complex SQL queries or understand intricate data schemas — they can access and analyze data using more familiar business terms.

• It can improve search (internal and external tools) by better connecting fragmented information. Semantic data helps to connect scattered information, such as customer feedback across support tickets, social media and reviews, to form a comprehensive understanding of customer sentiment and identify patterns.

• Semantic data can also power AI, and is particularly important in an age of increasing unstructured data and generative AI. With additional contextual information, semantic data can support conversational interfaces and applications.

• It also has compliance and governance benefits too — explicitly defining data semantics can help ensure consistency and accuracy as well providing transparency on data lineage and provenance.

What are the trade-offs of semantic data?

• It can be expensive — implementing semantic data involves significant upfront investment and specialized skills. 

• Complexity increases as data grows, which makes maintenance challenging.

• Performance can sometimes be a concern if you’re dealing with large or complex knowledge graphs. 

• Businesses risk vendor lock-in with some technologies built for semantic data. 

• Defining a universally agreed-upon ontology can be difficult, hindering interoperability.

How is semantic data being used?

Semantic data is particularly valuable in contexts where you’re trying to leverage data from many different sources. This could be in deeply specialized research areas, like healthcare research, but it can also be powerful when trying to build information discovery tools, like search features.

Let’s take a look at a couple of specific examples.

• Healthcare is a prime beneficiary of semantic data due to the vast, complex and often siloed nature of medical information. It can, for instance, help improve patient care and diagnoses. Hospitals are using semantic interoperability to integrate electronic health records (EHRs) from different providers, so a patient's lab results from one clinic, medication history from another and specialist notes can be understood and combined. It’s also aiding medical research too, with pharmaceutical companies using semantic data and knowledge graphs to connect diverse information on everything from chemical structures to clinical trial data to uncover richer insights, faster.

• In the financial industry, meanwhile, the Financial Industry Business Ontology (FIBO) has been introduced as an initiative to standardize financial concepts and relationships, improving data quality for risk analysis and regulatory reporting. This is a good example of semantic data in action. Semantic data can also aid with more effective compliance, helping organizations better understand and follow complex and changing regulations, helping them comply with law and protect consumers.