Understanding a Semantic data model

Within the data management discipline has always been the need to find meaning in the data around us. Especially when we are intent on trying to make the best decisions that we can within an organization for the services and products that we deliver and support for our customers. Semantic as a practice itself is the study of extracting the truth and meaning of the data around us. Everything is based on the foundation of how we use and interpret data, information, and knowledge for decisions within information technology. Enterprise data management depends on the use and interpretation of data for its needs, people, and customers.

What is Semantic data

Semantic data is data that has been structured to add meaning to the data. This is done by creating data relationships between the data entities to give truth to the data and the needed importance for data consumption. Semantic data helps with the maintenance of the data consistency relationship between the data.

A semantic data hub enables organizations to extract meaning, relationships, and truths between all kinds of data. The data can be structured or unstructured and exist in any source. Creating data collaborations using a semantic approach allows the transformation of data into information and information into knowledge for agile decision support. Although many applications can do this, now it can be done at the data layer to support improved data management and faster performance for decisions for all consumers of the semantic data.

Especially emerging technologies such as machine learning and artificial intelligence can benefit from semantic data. These technologies can be consumers of the data and providers of the data to improve performance, intelligence, and overall services and products.

How does semantic data work?

A Semantic data model works basically by creating relationships between data when the data is organized. This allows the data to have meaning without human intervention or additional processing.

The data is organized into three essential parts—the first data element or object, the relationship, and then the second data element or object. Database management systems that follow a semantic data model can easily be integrated and compared to each other for further intelligence related to the data itself.

Building a semantic data model begins with understanding the outcome of the questions that need to be answered for the required decisions. Then gather the data and ensure data integrity. Then create the data model by defining relationships between the data. The language used is called Gellish, which is used to express facts about data and its relationships to other data for knowledge and decisions.

What is the Semantic data model?

A Semantic data hub helps supply intelligent data for data-driven applications and other consumers of the data. The data itself is already in a state of truth relative to other data elements, so this does not need to be discovered within the application itself.

The data itself has meaning and represents knowledge needed by the consumers of the data. The data itself can come from various sources such as data lakes and other enterprise data sources to support the Semantic data hub.

The Semantic data model can be different from data hub to data hub. The model represents the need for data relationships that the consumers of the data need to make decisions. The model itself needs to be customer service-oriented instead of just a data model of all data. This helps with the overall service performance and the reduction of data management complexities. Especially for data that does not need to be made available in the Semantic data hub, since that data would have no value to the consumers of the data.

Semantic data model vs. relational data model

The most significant difference between a semantic data model and a relational data model is how they are built. The relational data model is built using relationships between tables, columns, and rows in the database. Although associations are made in a relational data model, it requires queries to discover the relationship of one data element to another.

In a semantic data model, the meanings of data are described as related to a real-world interpretation of how the data is used. The semantic model is derived fact and truth-oriented than a relational model without having to query the truth. Although similar to a relationship model, the semantic model is more intelligent and faster with deriving the truth between data elements in the module.

Applications of Semantic data model

The advantages of a data semantics model include the following:

• Easy to understand data relationships.
• Data relationships are, on the surface, truthful without deriving more data.
• Easier development of application programs using semantic data.
• Better visualization and reporting of data.

The disadvantages

• Relatively new, not many standards for semantic identity and design.
• Need more work and best practices to be done to model for an organization’s business.

Semantic data models are gaining a lot of traction in the industry. The application of the semantic data model is growing significantly with the introduction and improvement of emerging technologies such as Machine Learning (ML) and Artificial Intelligence (AI). The data models are just another evolution of how we manage enterprise data for performance and decision support.

If your company does not yet have a data hub solution or you are still struggling with legacy point-to-point integrations, then Actian can help. Actian DataConnect provides you with a hybrid integration-platform-as-a-service (IPaaS), so you can implement your data hub solution quickly and accelerate value back to your organization.