Graph Analytics, Graph Data Science and Knowledge Graphs 

We apply Neo4j-based graph analytics and data science for use cases such as financial crime detection, supply chain risk, customer 360 and recommendation engines across Singapore and the wider Asia Pacific region 

Graph technology connects data as networks of people, entities, transactions, assets and events instead of isolated tables. This makes it possible to see relationships, patterns and paths that are hard to capture with traditional BI and relational databases.

At BioQuest Advisory, we design and implement graph analytics, graph data science solutions and enterprise knowledge graphs for organisations across Asia Pacific, including Singapore, Malaysia, Hong Kong and Sydney. We focus on practical use cases in financial services, supply chain and logistics, manufacturing, biotech and public sector.

What are graph analytics

A graph is a way of representing data as nodes and relationships. Nodes can be customers, accounts, suppliers, shipments, products, locations or any other entity. Relationships capture how they are connected.

• Knowledge graphs organise your business concepts and data into a connected model. They create a single view of entities such as customers, suppliers or assets across systems, policies and documents.

• Graph analytics and graph data science apply algorithms on top of this graph to detect communities, rank importance, find shortest paths, measure similarity and identify unusual patterns.

Together they allow you to answer questions like "Who is connected to this customer", "Which supplier failure will have the largest impact" or "Which transactions form a suspicious network" in a direct way.

Why use graph technology instead of only tables 

• Relationships are first class
  Graphs store relationships directly. This makes queries about paths and connections much faster and more natural than complex joins.
  
  

• Flexible schema
  Graph models can evolve as your understanding of the domain changes. New node or relationship types can be added without redesigning large numbers of tables.
  
  

• Cross system view
  A graph can link data from core systems, data warehouses, spreadsheets and external feeds into one connected view without forcing everything into a single relational schema.
  
  

• Better fit for many questions
  Fraud networks, supply chain paths, influence maps, dependency graphs and lineage are easier and more efficient on graph structures than on flat tables.

Example graph analytics use cases 

Below are typical ways our clients use graph technology.

Financial services and insurance

• Customer and party 360 across core banking, CRM, transactional systems and external data.

• Financial crime and fraud networks that link customers, accounts, devices, merchants and locations.

• Risk propagation across portfolios, exposures and counterparties.
  
  

Supply chain and logistics

• End to end supply chain network that connects suppliers, manufacturers, warehouses, routes and customers.

• Supply chain risk analytics to find single points of failure and alternative routes or suppliers.

• Logistics network optimisation such as lane performance and multi hop routes.
  
  

Corporate and shared services

• IT and data lineage graphs that show how data moves between applications, databases and reports to support governance and migration.

• Contract and policy knowledge graphs that link obligations, clauses, parties and processes for faster review and compliance checks.

Biotech, pharma and healthcare

• R&D, trial and manufacturing graphs that connect studies, samples, facilities, equipment and results.

• Safety, quality and deviation analysis by linking incidents, batches, suppliers and processes.

Read more Graph Use Cases

Our graph analytics services 

• Use case and value discovery
  We identify graph driven opportunities in your business, define specific questions to answer and estimate the value from better detection, faster decisions or improved visibility.
  
  

• Domain and graph model design
  We work with business and data teams to define entities, relationships and properties, then design a graph model that reflects how your business really works.
  
  

• Platform and architecture selection
  We help you choose suitable graph databases, graph data science tools and integration patterns that fit your cloud strategy, data platforms and security requirements.
  
  

• Data sourcing, ingestion and integration
  We profile and source data from core systems, warehouses and external feeds, then design pipelines to load and maintain the graph on an ongoing basis.
  
  

• Graph analytics and graph data science
  We apply algorithms such as community detection, centrality, link prediction and similarity to reveal patterns, risks and opportunities, and convert them into business friendly metrics.
  
  

• Dashboards and applications
  We build visualisations and simple applications so business users can explore the graph, run scenarios and act on insights without needing to learn graph query languages.
  
  

• Training and capability building
  We train your data and analytics teams on graph thinking, graph modelling and the practical use of graph tools so you can continue to extend the solution.

FAQ about graph analytics 

Question 1: How is graph analytics different from our existing BI and dashboards?

Answer: Traditional BI and dashboards are very good at aggregations by customer, product, region and time. Graph analytics focuses on the connections between entities. It answers questions about paths, networks, clusters and influence that are difficult or inefficient in tables. In practice, graphs complement your existing BI stack rather than replace it. Many clients use graphs to generate new insights that are then surfaced through familiar BI tools.

Question 2: Can graph technology handle the size and complexity of our data?

Answer: Yes, modern graph platforms are built for highly connected data and can handle large and complex graphs. We pay close attention to graph modelling, indexing and query patterns so that performance remains acceptable as data grows. Often, queries that are complex and slow in relational databases become simpler and faster when implemented as graph queries. Leading graph databases can provide real-time complex query results. 

Question 3: Can we use graphs with both structured and semi-structured data?

Answer: Yes. Graphs are a natural way to link structured data from core systems with semi-structured data such as events, logs or external feeds. For unstructured content like documents and text, we usually extract key entities and relationships first, then store those in the graph so they can be analysed together with structured data.

Question 4: Where should we start with graph analytics in our organisation?

Answer: The best starting point is a focused use case where relationships clearly matter, such as fraud networks, supply chain risk, customer 360, hyper-personalised recommendation or data lineage. We typically run a short discovery to confirm feasibility, data sources and value, design a graph model for that domain and build a pilot that answers specific business questions. Once impact is demonstrated, we agree a roadmap to extend the graph and connect additional domains and markets.