Turbocharge your data operations through graph analytics - Photo by Marius Masalar on UnsplashBe more ambitious in your digital transformation projects and gain tangible business insights by embracing analytical graph databases.

In business, there are broadly two types of leaders: those who scrutinise the competition, trying to match them pace for pace, and those who scan the road ahead to anticipate the twists and turns, looking for their chance to break through to the head of the pack.

Like a successful Formula One driver, you don’t want to be reckless. But neither do you want to hang back when an opportunity presents itself.

Businesses are seeking to make themselves more efficient through digital transformation. Yet, many are simply looking at throwing a greater number of more powerful servers at the same old big data problems? Are they not guilty of failing to challenge the traditional, legacy relational database, with its data ranked neatly in rows and columns, tables and databases?

Let’s face it, the world is not organised in neat rank and file like toy soldiers on parade. The real world is a network of data points joined to each other by relationships. She is friends with him. This company makes this product. This account sent money to that account. The real world is a web, and the chain of relationships is endless. Making this conform to the traditional relational database model is difficult, if not impossible, in some cases.

Graph databases are the natural way to represent this information. And while the technology has been around for a while, it has only relatively recently become mature enough to function at scale in an enterprise environment.

Consequently, it’s now being embraced by organisations that don’t want to play catchup. Instead, they are determined to leapfrog the competition by moving to the next big thing.

Extracting business insights

According to the 2019 Gartner CEO and Senior Business Executive Survey, 82% of business leaders seek to make their businesses more digital. But what do businesses mean by digital transformation? In many cases, it can be boiled down to making information more accessible.

If you are using traditional relational database tools, the quest to extract more business intelligence from your data can involve reorganising your data, linking previously separate databases or, if you are particularly ambitious, completely restructuring all of your information assets into a single, unified system, with all the risks that this entails.

But successful or not, this approach still means you are only fighting yesterday’s battles. You are condemning yourself to writing ever more sophisticated and complex SQL queries. It is the only way to get around the fundamental limits of relational databases.

And even if you do succeed, you are still only playing catch up with other companies that got there before you.

Some companies have broken the mould on data analysis with graph analytics. According to Gartner, the trend to graph is accelerating. It predicts that by 2025, graph technologies will power 80% of innovations in data and analytics, up from 10% in 2021.

Innovators will extract meaningful business intelligence from existing data more quickly and efficiently while their competitors struggle to keep pace.

The beauty of graph analytics is that it can be added as an additional layer to your data estate. There is no need to re-architect the entire system to enjoy the benefits.

Connect datasets and pipelines

A distributed graph database enables you to connect internal and external datasets and pipelines to extract invaluable business intelligence in real-time. Xandr, the advertising and analytics division of AT&T’s WarnerMedia, uses this across 15 WarnerMedia channels. It includes Cinemax, CNN, HBO, and TNT, each holding data on millions of customers. The challenge for Xandr was creating a unified picture of all of these customer databases to deliver a seamless experience across the portfolio for both viewers and advertisers.

Xandr created Community. It is an advertising platform able to disambiguate user data from multiple platforms. The goal was to create unified entities across the multiplicity of data sources and deliver a joined-up advertising experience. Importantly, it delivered that unified experience even as viewers jumped between their personal devices and across channels.

To achieve this, Xandr built the first and largest identity graph of its kind in the advertising industry. It used a graph database that scales horizontally to accommodate more than five billion vertices (business entities such as users, devices and identifiers) and seven billion edges (relationships among entities) and ingests a billion updates per day. Graph analytic capabilities, such as entity resolution and centrality algorithms, stitch together identities across the separate databases to create a view of households and devices that is both unified and granular.

Xandr now knows how many times an ad has been seen on a particular device. It can also tell how many times that ad has been seen across all the viewer’s devices – and target advertising accordingly. And the result is clear: Xandr wins advertisers from the competition by offering higher quality data and more precise targeting.

Analyse connected data

Advanced analytics can yield insights on a scale and depth never before seen in legacy databases. Vehicle manufacturer Jaguar Land Rover (JLR) uses graph analytics. It was able to accelerate supply chain planning from three weeks to just 45 minutes. It was also able to solve problems that were previously considered to be computationally intractable.

The challenge for JLR is that the automotive supply chain is vast and complex. A typical car comprises 4,500 parts drawn from a supply base that numbers in excess of 30,000. Any disruption from manufacturing processes and supply routes to changes in consumer tastes and local regulatory standards can cause expensive and time-consuming knock-on effects throughout the supply chain.

With graph analytics, the company can quickly run supply chain scenarios. It allows it to maintain real-time oversight of its entire supply chain to anticipate and manage disruptions more quickly and efficiently. It has yielded a 35% reduction in supplier risk and has added £100 million annually as incremental profits. As JLR’s head of data and analytics said, the company now gets three times the value from the same data with graph analytics. It can also ask questions that, for the past 20 years, it thought were impossible to answer.

In-database machine learning

Connected data contains a wealth of business intelligence that can be accessed through in-database machine learning. Financial services company NewDay has used it to develop sophisticated anti-fraud applications. Graph analytics is enabling it to turn the tables on criminals by identifying fraud at all stages in the credit card lifecycle. It also detects application fraud (trying to obtain credit cards with stolen personal information), transactional fraud and first-party fraud (fraud by existing customers).

NewDay has revenues of nearly £1 billion per year and five million consumers. It has an operation that spans the largest online retailers and best-known credit cards. NewDay had no shortage of data on which to draw with access to third-party fraud prevention and identity checking databases. However, bringing all of this information together in a meaningful way was a time-consuming manual process that required fraud investigators to jump in and out of internal and external databases.

Using graph analytics, NewDay created a system that brought together myriad databases, both internal and external, empowering fraud investigators to speed up complex fraud analysis. In addition, sophisticated algorithms enable it to analyse more than 10 million transactions per month and identify suspicious cases while minimising false positives. Consequently, anti-fraud at NewDay is more intelligence-driven, allowing it to block cards and issue new ones more quickly and refer a greater number of cases to the police.

NewDay says that their graph-based system is still in its infancy but has already yielded a 10-15% uplift in the number of fraud cases being detected. The head of fraud prevention says this only the beginning.

In conclusion

As we have seen, graph databases are a powerful tool for connecting datasets across the organisation. They enable the application of powerful graph analytics which can help in revealing hidden relationships between disparate databases. It allows organisations to use databases as they should be used – to connect, analyse and learn.

Companies like Xandr, Jaguar Land Rover and NewDay have recognised that the old data paradigms were not working for them. When the opportunity presented itself, the data professionals at these companies charged ahead, embracing the potential of graph databases and graph analytics and, by so doing, empowering professionals in other departments across their organisation to gain new business insights through a deeper understanding of their data assets.


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TigerGraph is a platform for advanced analytics and machine learning on connected data. Based on the industry’s first and only distributed native graph database, TigerGraph’s proven technology supports advanced analytics and machine learning applications such as fraud detection, anti-money laundering (AML), entity resolution, customer 360, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. The company is headquartered in Redwood City, California, USA. Start free with tigergraph.com/cloud.

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