Solving business' data challenges with graph

Estimated reading time: 5 minutes

According to TigerGraph’s VP in APJ, Joe Lee, “When we start thinking about building a region, we always think about the addressable market and how we can help clients overcome challenges they are dealing with specifically in the data space.

“That’s our big, big focus.”

Having set up their regional base in Singapore, Joe’s playbook is to continue growth by setting up presence in untapped markets like Korea and Japan, while also engaging talent the company needs to support its expansion. Engineers, sales professionals, consultants, and more are in demand. However, it is engineers that Joe finds are especially excited to work with graph technology.

Graph technology is growing rapidly

“Gartner says that 80-percent of analytics is going to be graph-related by 2024,” Joe pointed out. It is as though the engineering community knew this because they are well aware of the challenges that come with traditional analytics and traditional databases.

And while all of this is going on, there is huge demand for engineering resource which seem to be in limited supply at the moment.

(Engineers) are well aware of the challenges that come with traditional analytics and traditional databases.

And as the fight for this particular resource wages on, Joe has made some observations., not least of which is that engineers are problem solvers – the more different types of customers/industries they can contribute to, the better.

TigerGraph also offers the freedom to explore, do things, learn new tech, as well as work with technology that is leading edge. Seeing Singapore as the connection between two of the world’s most populated countries aka tech centres, he recognises thriving R&D hubs in both China and India that TigerGraph can leverage.

Pondering Malaysia and the region

“Dealing with several rows and columns and tabular scenarios is limited, and they understand that looking at looking at things from a relationship perspective is much more conducive to solving business problems,” Joe said.

No matter which sector one looks at, they all need to somehow collaborate with data.

Joe finds Malaysia similar to some other markets in Asian, and uses similar approaches to address this. “I think the way we always look at it is based on three factors. The first is core banking and everything you can do with financial services.

“Then there’s telco and everything they do these days. Then there is the government sector. These three sectors are the backbone of Asian countries and Malaysia is no different.”

He opined that no matter which sector one looks at, they all need to somehow collaborate with data.

“They all need to find the right ways to help consumers, and the right ways to reduce crimes and unwanted behaviour like fraud. And one of the things we do specifically well; and it’s prevailing in the financial services industry also; is to help stop financial crime.”

Targetting financial crime and more

Joe shared about the Australian Tax Office (ATO) press release about ATO’s foray into graph technology and their usage of it.

Graph allows you to look at all the relationships you can have, unlock a lot of insights, and have the best analytics for whatever the use case may be.

The VP observed that the ATO is going all the way into graph and making a move before a lot of other organisations have productionised graph technologies in their environments.

Notably, an organisation may have all their data in a data warehouse, or a data lake, on a system that is on-premise. “What’s (usually) missing is the way to connect all the data so you can find advanced insights.

“What graph does is allow you to look at all the relationships you can have, unlock a lot of insights, and have the best analytics for whatever the use case may be.

“In ATO’s case, they are literally spotting and detecting tax avoidance, which is a high value statement for their government,” Joe said.

Solving problems with deep link analysis

The valuable ability to look into relationships is helping organisations more easily spot financial crime activities like shell companies, synthetic accounts and more.

It’s not just about looking at relationships. TigerGraph’s uniqueness lies in being able to look deeper into ten to twenty connections. This is possible because of an enterprise-class and native architecture (that is also massively parallel and distributed) that enables the technology to traverse up to that many relationships.

In first generation graph technologies, this ability traverse data in depth, is still a challenge.

TigerGraph’s uniqueness lies in being able to look deeper into ten to twenty connections.

Joe opined, “They can’t do it because they are not distributed, it may be based on a single server and hence it can’t scale.

“So, you can get to maybe three hops (connections), or five hops and you stop. There is not a lot of value with that.”

There is a potential 11-percent uplift in catching fraud and money-laundering, when visualisation technology is added on top of graph.

Because of this ability, Joe shared that TigerGraph is able to increase fraud and money-laundering detection by as much as 5-percent. Five percent in this particular use case for a financial services company can amount to as much as millions of USD.

Fraud-specific solutions can take a year to get be deployed, but Tigergraph is able to drastically reduce this to 2 months. There is a potential 11-percent uplift in catching fraud and money-laundering, when visualisation technology is added on top of graph, he also said.