Today’s digital economy has seen the immense growth of Big Data surpassing the point where traditional databases can manage it. Information isn’t only growing in volume but in complexity. Vast repositories of data are being built by not only on their operations but also their customers, with each entity having numerous points and levels of information. The issue now is how to store, process and analyse this data in a meaningful and timely way.

As a result, many organisations across Southeast Asia are now turning to graph data science to store data and generate insights. Accelerated demand is coming from a wide range of sectors, including financial services, travel, retail, public sector and healthcare organisations wanting to solve complex problems.

Database platforms vs graph data science platforms

Representing today’s customer databases in a two-dimensional table or spreadsheet is a very limited approach. Data can be stored and queried but finding patterns among thousands of rows and cells isn’t an easy or immediate process. It’s extremely difficult to connect different areas of data: for example, not just who a customer is, but what they bought, how they bought, where they bought and why they bought.

Graph data science leverages the connections and relationships between billions and even trillions of data points. It lets the connected data “speak for itself”, such as running and unsupervised method of graph algorithm to find the signal in the noise. With a customer database it could show how the community of customers interacts, which could be useful information for segmentation.

For example, Singapore’s telecommunications operator Starhub has used graphs to bundle products and services to maximise value, building a unified database of every product and service with its rules and relationships to ensure that appropriate services are bundled. Having a graph database has made it much easier for StarHub to manage its commercial product line hierarchy.

DBS Bank in Singapore has also developed an Automated Financial Analysis (AFA) tool designed and developed by the financial institution to enhance customers’ digital journey by leveraging knowledge graphs and machine learning technologies. The solution automates the financial analysis process of credit applications within minutes; and ADA (Advancing DBS with AI) to provide data ingestion, data security, data storage, data governance, data visualisation, and analytics model management capabilities.

Why a predictive, and not reactive approach is needed

In Southeast Asia’s highly competitive landscape, organisations need to stay one step ahead of their competitors. For example, financial institutions typically address fraud when it has already happened. With graph data science, the suspicious connections between individuals and entities become visible and allow for much earlier intervention. A knowledge graph can identify chains and rings of linked individuals, scoring the quality, quantity and distance of one party’s relationships with suspicious entities.

When one fraud ring is identified, a similarity algorithm can also be used to identify other potential fraud rings across data. Once the types of patterns that predict a certain outcome are known, they can be used to generate much more accurate predictions in future.

In Singapore, Standard Chartered Bank’s SCypher application leverages knowledge graph and machine learning models to identify cyber threat events and protect clients’ data and assets. Cyber defense analysts and risk owners across the bank can quickly assess cyber threats and their potential impact on bank critical information assets quicker than ever before. 

Solving the APAC supply chain issue

Supply chains have been one of the biggest disruptions in the past couple of years – an issue keenly felt across many Asian ports such as Singapore, Shanghai and Hong Kong that act as major global hubs and transit points for goods travelling around the globe.

Unravelling the extremely complex web of routes and participants to try to re-route tens of thousands of container ships crossing the oceans every day has been an extremely challenging task.

Supply chain management is dynamic with many moving parts, with bottlenecks potentially occurring at any given point. But the volume and detail of data generated by traditional databases lacks real-time, accurate information processing capabilities. ExxonMobil for example has been trialling quantum computing to solve routing formulations, since the scale of the equation is too large for classical computers to handle.

But knowledge graphs which are adept at mapping complex, inter-connected supply chains, and maintaining high performance even with vast volumes of data are a viable solution. Having an inherently relationship-centric approach makes them able to better manage, read and visualise their data. A graph database typically demonstrates 100 times faster query response speeds in contrast to a traditional SQL database.

Graph data science holds enormous possibilities for organisations across Southeast Asia, with the region as one of the fastest-growing areas in the world. Organisations that have harnessed the capability of big data through technologies such as graph data science will be well positioned to become world leaders in their respective industries.

This article was contributed by Nik Vora, Vice President APJ at Neo4j

About the author

Nik Vora is the Vice President - APAC at graph data platform leader Neo4j

Nik Vora is the Vice President – APAC at graph data platform leader Neo4j. Prior to this, he established Qubole in APAC and had the opportunity to lead and be a part of three hyper-growth GTM plays for Capillary Technology, a Cloud-SaaS platform. Over the last 10 years, his focus has been about building markets in Singapore, Australia, India and China.