In today’s data-driven world, your organisation collects an impressive volume of data. From inventory specifics to customer service interactions, data forms an intricate network of untapped potential. Discovering the hidden connections within your data can lead to substantial organisational benefits that can bring about significant improvements in your decision-making and business operations.
By understanding these concealed interdependencies, you can recognise how different aspects are interlinked, enabling them to make holistic decisions that are beneficial across different departments, branches or divisions.
Consequently, decoding this complex data network isn’t just an interesting idea, it’s a business necessity.
Graph databases are a ground-breaking technology designed to manage complex data relationships. Unlike traditional relational databases, graphs consider the connections between your data elements as significant as the data itself. This makes them ideal for analysing intricate, interrelated datasets and exploiting them in a more flexible, scalable and efficient way.
There are two main categories of graph database models: Resource Description Framework (RDF) and Labelled Property Graph (LPG). Whilst both emphasise the importance of relationships, they diverge in terms of their underlying data models and the methods used to represent and store data. Ultimately, it will be your specific use case that will determine the right model for your requirements.
RDF was created by the World Wide Web Consortium (W3C) and this approach provides a standard model for representing data in the form of subject-predicate-object triples. These are essentially two nodes connected by a single edge. For example, James knows Rosie where James is the subject, knows is the predicate, and Rosie is the object. Each element in an RDF triple has a Uniform Resource Identifier (URI), which allows for the unique identification of every subject, predicate and object. These RDF triples can be stored in various ways, including triplestores, relational databases, or even as plain text files. The data is represented using standardised RDF serialisation formats like RDF/XML, Turtle, N-Triples, or JSON-LD. Standardisation and interoperability are two key benefits of RDFs. This facilitates easy integration and information exchange with other RDFs.
In contrast, an LPG depicts data as nodes, edges/relationships and properties. Nodes represent entities, relationships represent connections between nodes, and properties store additional information about nodes and relationships. The relationships are typically directional and, unlike relationships in an RDF, can have properties associated with them. Taking our previous example, James and Rosie are both nodes. James knows Rosie (and presumably Rosie knows James), and there is therefore a bidirectional relationship between them. We can go further with an LPG and add properties James, Rosie and their relationship. For example, we could add their job titles, key skills, the length of time they have known each other, and the type of their relationship.
Data in an LPG is typically stored in a graph database that uses a native graph storage format, optimised for efficient traversal and graph querying.
Graph databases have become a valuable tool in handling complex and highly interconnected data. These databases excel where intricate relationships and flexibility, such as social network analysis, recommendation systems, fraud detection, and knowledge graphs are required.
You can select the right data model for your organisation by carefully considering the nature of your specific use case.
The standardisation and interoperability offered by an RDF does come at the cost of flexibility. To support these features, RDF data conforms to pre-defined ontologies and controlled schema definitions. In contrast, LPGs are often schema-optional or flexible. This allows the data model to evolve over time. The schema can be defined on a per-node or per-relationship basis, providing flexibility in representing diverse data structures.
This makes LPGs better suited to use cases in your organisation that involve dynamic datasets and changing contexts.
Finding hidden connections within your data is an iterative process. It requires a combination of domain knowledge, data exploration and visualisation to uncover meaningful relationships. Following the process below is one way to help you get started:
By harnessing the power of graph databases to uncover the hidden interconnections within your organisation’s data, you’re laying the foundations for success in this increasingly data-centric world.
For further information on how we can support your organisation to leverage graph technology, contact us at [email protected]
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