3 ways graph data platforms help governments leverage data

By Nik Vora, Asia-Pacific vice president, Neo4j

Governments across the globe today are challenged with solving complex problems. Digitalisation in government has created a vast inflow of data which often contains the answers they need. But making sense of the growing volume and variety of information, often held in disparate repositories, as well as the interrelationships of data, is extremely difficult.  

The role of government is extensive and in today’s digital economy, governments need data to fight crime, prevent terrorism, improve fiscal responsibility, and provide transparency to citizens. This frequently involves connecting data from different agencies and departments. Doing this with traditional, two-dimensional databases is challenging. Columns and rows are useful for collecting and processing data, but lack the depth and capacity to correlate multiple sources or understand the relationships between them. 

In contrast, a graph data platform can model complex networks of entities and their interrelationships. This reveals patterns that may be impossible to detect using traditional representations such as tables. They store data in knowledge graphs of linked nodes that also include relational information. It’s a much more powerful and flexible solution for storing and analysing complex datasets that governments deal with.

Below are three examples of how governments are using graph data platforms to improve their operations:

1. Using connected data in criminal investigations

Connections in data can point to potential suspects in a case. A suspect often appears in several different databases. Connecting that data is key for investigators to find out all they can about a suspect through phone records, financial transactions, fingerprints, DNA, court records, associates and more.

Separate data silos of people, objects, locations, and events (POLE) aren’t useful for a criminal investigation or trying to stop a terrorist attack. Law enforcement and intelligence agencies need the relationships that span those data silos and contextualise the activities and associations among suspects.

One example of a vast data set that was analysed through a graph data platform is the Pandora Papers. These involved 11.9 million leaked documents that the International Consortium of Investigative Journalists (ICIJ) published from October 2021. It comprised over 2.94 terabytes of unstructured data in a range of different languages and formats (documents, pdfs, emails, spreadsheets, images, emails) from disparate sources.

Managing this data was a huge challenge. To trawl through all those documents and “join the dots” would have taken many years to conclude. With a graph data platform, it took just over 12 months, exposing offshore entities linked to hundreds of politicians, business heads, celebrities, royals, drug dealers and religious leaders.

2. Managing supply chain costs and efficiency

Government departments such as the defence force have big budgets when it comes to the acquisition and maintenance of equipment. For example, the US army manages equipment – from vehicles and aircraft to munitions and radios – used by over one million military personnel.

The maintenance, operation and support costs of this equipment represent as much as 80% of total lifecycle costs and involve procuring millions of spare parts every year. The US army formerly used an ageing mainframe-based system, but it was very limited and difficult to manage information with, resulting in unpredictable maintenance costs.

By using a graph data platform, the Army has a much more flexible and robust view of the parts requirements and costs of replacement parts across systems, components and subcomponents. Rapidly storing, exploring, and visualising a wealth of logistics and cost data is much easier.

3. Fighting and tracing cybersecurity threats

Preventing cyber attacks involves tracking large amounts of detailed information, such as network and endpoint vulnerabilities, firewall configurations and intrusion detection events. The solutions used to analyse this data typically track data points. But to be successful, analysts need to understand how those data points are related.

Mitre Corporation, a government-funded organisation that manages research and development centres, has used graph data platforms to develop a tool called CyGraph. It is used for cyberwarfare analytics, visualisation and knowledge management.

CyGraph brings together isolated data and events into an ongoing overall picture for decision support and situational awareness. It prioritises exposed vulnerabilities, mapped to potential threats, in the context of mission-critical assets. It also correlates intrusion alerts to known vulnerability paths and suggests the best course of action for responding to attacks. For post-attack forensics, CyGraph shows vulnerable paths that warrant deeper inspection.

Graph data platforms make storing and analysing huge datasets much easier. They are highly scalable, with increased level of connections not resulting in a corresponding increase in computing cost. For governments that must prioritise efficiency and transparency, they are the ideal way to traverse data and deliver services effectively.