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Graph Databases: A “Must-Have” Component of the 2022 Data Landscape

Predictions attributed to:  Jay Yu, VP of Product & Innovation at TigerGraph

According to Gartner, by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the enterprise.

As the volume of data created and replicated throughout the enterprise continues to increase, scalable graph technology has become the critical link between massive amounts of data and key business insights. Graph will become a major competitive differentiator among the companies in multiple industries — from financial services and healthcare to retail and manufacturing. Graph can quickly highlight, discover, and predict complex relationships within data —insights that uncover financial fraud or help solve logistics challenges within the supply chain.

Throughout 2022, more companies will apply the power of graph analytics to support advanced analytics and machine learning applications, including fraud detection, anti-money laundering (AML), entity resolution, customer 360, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. Graph will become even more linked with ML and AI. Gartner even reports that “as many as 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology.”

TigerGraph is working to advance graph databases, and has committed to delivering petabyte-scale graph databases within the next two years. The company is also working on a 100 TB version of the LDBC-SNB benchmark, which should be achieved within 2022. In terms of standardisation, the next year will see a move towards finalising standardisation on Graph Query Language (GQL). We also anticipate a deployment shift from on-prem to the cloud (both private and public.)

The Rise of Graph Data Science in 2022

Predictions attributed to: Richard Henderson, Technical Director at TigerGraph

In the coming year, we will see the rapid adoption of Graph Data Science, as data and learning are naturally merged. Relationships are fundamental, so having meaningful patterns is critical to enhance conventional machine learning and provide explainable results in industries such as healthcare (to provide real-time recommendations), the industrial supply chain (leveraging analytics for decisions), and financial services (to power real-time fraud detection). In fact, we will see the fusion of distributed graph databases, advanced analytics, and in-database machine learning.

Graph Data Science brings expert knowledge to machine learning via graph analytics — and more enterprises will shift from an exploration to production mindset during the next year. These enterprises will benefit from deep-link pattern discovery, uncovering patterns and insights within 10-20 hops (or connections) of data.

As companies within various industries continue to rapidly grow and scale, they will need to find solutions (such as TigerGraph) to unlock the huge potential of their connected data. In 2022, we will see a plethora of new graph analytics industry use cases and vertical solutions.

Specifically, “digital twins” will appear everywhere, and they will be based on real-time analytic graph databases.

After the COVID-related disruption of the past two years, all enterprises have been focused on becoming more operationally resilient. This has spurred interest in adopting models and techniques from the manufacturing industry such as a digital twin, or a real-time model of a business and its environment.

A digital twin gives a complete and current view of the physical business situation, using the business events and data that are probably already available in individual operational silos and data marts. This, combined with graph analytics, can deliver a detailed and immediate digital scenario, showing the impact and risk associated with any delays or failures within that network. Graph analytics will combine these individual events and the map of the network to produce a completely “zoomable” big picture view of the entire operation, so decisions can be prioritised based on both tactical and global concerns — with full knowledge of the impact of specific decisions.

This powerful and scalable technology allows an organisation to construct a what-if analysis for any changes or alternate scenarios, which can qualify investment decisions. A digital twin helps avoid unintended consequences by plotting the path that avoids such pitfalls as overlapping changes that individually seem safe, but in combination create unsustainable risk.

The beauty of “analytic graph databases” is that they are generic, adaptable, and powerful enough to express digital twins in a simple, direct, and rapid way. This means the technology can be quickly applied in whatever domain it may provide benefit. The result: the joined-up viewpoint that businesses have been asking for that will drive resilience in the form of real-time, data-driven operations.

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