At the heart of any data management discipline is the need to find meaning in the data around us. Semantic modelling is a method of structuring data, applying meaning to it and identifying the significant relationships within them – making this type of data more valuable.
Knowledge graph technology is gaining traction in the data management sector, offering a way to contextualise data without human intervention. When a knowledge graph is crafted using semantic data modelling, you are not only storing data, but harvesting the ability to interpret data for different information needs.
Semantic data modelling allows your data model to evolve alongside your company demands, moving your business forward and helping your organisation to move from data-centric to knowledge-centric decision making.
What is the semantic data model?
An effective data analytics strategy requires the ability to obtain relevant insights from large amounts of data originating from diverse data sources – introducing the semantic data model.
The semantic data model makes it easier for organisations to extract meaning, relationships, and truths behind all kinds of data; translating various fragments of data into information that can be consumed. The data itself can come from numerous sources including enterprise data sources and data lakes.
Semantic data can be anything from financial records to invoice histories that have been sitting in your inbox all along. The semantic data model ensures that a company has full command of that data, pushing each department closer and enabling transparent decision making.
When knowledge graphs are built using semantic data modelling, organisations are offered opportunities to revolutionise and interlink data into coherent knowledge. Semantic technology generates the transformation of data into useful information, which can then be used in decision making.
Achieving Knowledge-Centric Decision Making
While many applications can translate data into information, the semantic model produces practical information at the first stage of data analysis. From this process, decision performance is enhanced for all consumers of semantic data.
A knowledge-centric organisation is defined by TechTarget as “an organisation whose knowledge focus is to provide mechanisms for the firm to better apply, share, and manage knowledge resources across various components in the company.”
Considering that semantic data models are different from one organisation to the next, the model demonstrates the necessity for data relationships in achieving better management of resources. By integrating unstructured and semi-structured data sources, semantic data allows for multiple interpretations; helping business owners to make decisions based on data relationships.
The application of the semantic data model is growing rapidly with the emergence of innovative technologies such as artificial intelligence (AI) and machine learning (ML). Semantic data is what “connects the dots” for artificial intelligence bots with minimal human supervision – enabling knowledge-centric decision making with automation.
Can semantic data modelling help your business optimise data for decision making? Contact the expert team at Slingshot Solutions today to find out.