In the realm of technology, new terminology frequently emerges – it’s inevitable to keep apace of the swiftly evolving landscape. Back in 2016, articulating an accurate description of our platform proved to be a challenge, given the absence of appropriate vocabulary. Although we were aware that it was next generation, the terms available fell short of capturing its essence. Over time, as industry language developed, we experimented with descriptors such as “data analytics platform” and “unified data analytics platform”, yet these were still not comprehensive. In recent years, prominent industry players embraced the phrase “graph intelligence”, providing us with a more fitting classification: a graph intelligence platform.
However, the question arises: What does “graph intelligence” truly mean? As with many facets of technology, terminology evolves from a singular concept into an array of options. The outcome often hinges on your approach and familiarity with the relevant jargon. For those unacquainted with its nuances, attaining the requisite understanding can be elusive.
What’s in a name?
A graph is a data modelling structure that connects data points (nodes) to each other through their relationships (edges). So, in its simplest form, graph intelligence refers to the use of graphed data and graph algorithms to derive insights, identify patterns and predict outcomes. It creates a deeper understanding of complex data networks by leveraging the structural relationships to support more effective analysis, decision-making and problem solving.
Why choose graph?
Graph databases address several technical constraints commonly found in relational databases, particularly when dealing with highly interconnected and complex data. Key constraints and how graph databases resolve them are:
|Difficulty in handling relationships||Relationships are typically represented using foreign keys, resulting in complex join operations for querying related data. Navigating relationships can be inefficient, especially in deeply nested or many-to-many relationships.||Relationships are first-class citizens and are directly represented, making traversing and querying relationships efficient. This is especially advantageous for applications where understanding and analysing relationships between entities are essential.|
|Performance degradation with joins||Join operations become increasingly expensive as the data and relationships grow in complexity. Recursive joins or those involving large tables can significantly degrade performance.||Performance remains stable even as the dataset grows because traversing relationships in a graph is a constant-time operation. This produces efficient retrieval of related information, even in highly connected data.|
|Rigid schema design||A predefined, rigid schema that might not accommodate changes well, especially in evolving or semi-structured data environments.||Flexible and dynamic data modelling makes it easier to adapt to changing requirements and accommodate various data structures without the need for a predefined schema.|
|Inefficiency with hierarchical data||Inability to represent hierarchical data structures efficiently leads to complex queries and performance issues with tree-like or nested data.||Natural modelling of hierarchical relationships results in efficient handling of data, such as organisational structures or category hierarchies.|
|Query complexity for patterns and paths||Expressing complex queries involving patterns or paths can be convoluted and performance-intensive due to the need for multiple join operations.||Intuitive query languages enable straightforward representation of patterns and paths, making it easier to traverse and analyse relationships within the data.|
|Limited scalability for relationships||Scaling horizontally can be challenging, especially when dealing with highly connected data, as it may require complex sharding or replication strategies.||Easily scalable by distributing and partitioning the graph, maintaining efficient traversal and querying of relationships, regardless of scale or complexity.|
|Complexity of analysing networks and graphs||Analysing networks, social graphs or interconnected data can involve intricate SQL queries often leading to suboptimal performance.||Specialised in analysing and visualising complex networks and graphs, providing efficient algorithms for operations like centrality analysis, community detection and pathfinding.|
Therefore, graph databases offer a more efficient and intuitive solution for handling highly connected and relationship driven data. This overcomes the limitations imposed by the rigid structure and query mechanisms of traditional relational databases.
Graph intelligence platforms
A graph intelligence platform offers several significant benefits in handling and analysing complex, interconnected data structures:
- Efficient representation of relationships: Excel in representing and navigating relationships between entities in the data allows for efficient querying and retrieval of related information. This makes it ideal for use cases like social networks, recommendation systems, fraud detection and knowledge graphs.
- Flexible data modelling: Allow for flexible and dynamic data modelling. Entities and relationships can be easily added, modified or removed without requiring a rigid predefined schema. This is valuable in scenarios where data models evolve or are not well-defined in advance.
- Performance and scalability: Can deliver high performance for queries involving complex relationships, especially when compared to traditional relational databases. And as the volume of data grows, the performance remains relatively stable due to the efficient traversal of relationships.
- Real-time insights and analysis: Facilitate real-time analysis of relationships and patterns within the data, which is crucial for applications requiring immediate insights, such as fraud detection, network monitoring or recommendation systems.
- Holistic view of data: Connect disparate pieces of information and enable the discovery of hidden patterns and insights. This interconnected perspective is important for applications like understanding customer behaviour, impact analysis and supply chain optimisation.
- Optimised for graph algorithms: Come with built-in or optimised graph algorithms that can efficiently compute various metrics and patterns within the data. These algorithms aid in tasks like community detection, centrality analysis, shortest path calculations and influence propagation.
- Natural language processing (NLP) integration: Seamless integration with NLP techniques allows for text analysis, sentiment analysis, entity extraction and topic modelling. This integration enhances the understanding of textual data and enriches the graph with meaningful insights.
- Machine learning (ML) integration: Enable predictive analytics, anomaly detection and recommendation enhancements. The graph structure can serve as a powerful feature representation for ML algorithms.
- Data privacy and security: Often come with robust security features to ensure that sensitive relationships and data are adequately protected. Access controls and encryption mechanisms are typically implemented to safeguard the integrity and confidentiality of the data.
- Collaborative knowledge sharing: Allow multiple users or teams to contribute and access knowledge in a centralized or distributed manner. This is particularly beneficial for building knowledge graphs and collaborative intelligence systems.
A graph intelligence platform built around a graph database provides the capability to model and analyse data that aligns seamlessly with the interconnected nature of modern digital landscapes. It is a potent tool with diverse applications across various domains.
However, a graph database alone is not a universal panacea. Realising its immense potential and effectively leveraging it are distinct endeavours. In practice, we often encounter clients expressing frustration after adopting a prevalent graph database. They commonly cite challenges related to usability, data onboarding, driving analytical insights and seamless integration within their digital environment. Fortunately, we offer an exceptional solution: Locstat LightWeaver®—a groundbreaking graph intelligence platform that adeptly addresses these persistent challenges.
During the developmental phase of our graph intelligence platform, we meticulously delineated the necessary functionalities crucial for providing effective situational awareness and the generation of high-quality insights. Acknowledging both the potential and constraints inherent in graph databases, we built a comprehensive ecosystem of capabilities to fully unlock the potential of graph. The result materialised as Locstat LightWeaver®, a pioneering achievement that seamlessly amalgamates the power of graph, complex event processing (CEP) and a robust rules engine. The fusion of CEP with graph raises sensemaking to a heightened level, enabling real-time decision-making grounded in data-driven insights. Essentially, this augmentation vastly magnifies the capabilities of graph intelligence.
The result is a robust graph intelligence platform that delivers in-depth analysis of intricate data structures and the extraction of valuable insights in real-time. Our offerings encompass tailored industry and point solutions, finely calibrated to align with your specific business requirements. In addition, our streamlined deployment approach ensures a rapid and unobtrusive integration, promptly yielding a significant return on investment. Consequently, this empowers you to optimise operations, enhance customer engagement, drive revenue growth, identify potential risks, mitigate fraudulent activities, streamline processes or innovate new use cases, all according to your discretion.
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