A knowledge graph excels at harmonizing complex data and flexibly modeling massive real-world structures and their business logic.
Organizations are leveraging graph technology to build large-scale digital twins capable of unifying data across disparate sources, delivering rich analytics, and supporting near real-time monitoring of critical assets.
In 2022, the digital twin market size was valued at $6.9 billion and is projected to reach $73.5 billion by 2027. Further, Gartner predicts that by 2025, 25 global enterprises will achieve $1 billion in revenue or cost savings from their digital twin initiatives, up from one in 2021. Source: Gartner, Emerging Technologies: Revenue Opportunity Projection of Digital Twins, Alfonso Velosa et al.,16 Feb 2022. While the value of digital twins is compelling, the scope of effort required to create them can be daunting. An effective digital twin is created from massive volumes of disparate data from many sources and in many formats, incorporating elements such as 3D models, accounting systems, and operational systems, as well as data from IoT devices.
“While the technology for digital twins is only just emerging, it is quickly becoming popular in corporate strategy, giving us the power to understand the present and predict the future.”
Maya Natarajan, Senior Director of Product Marketing at Neo4j
A knowledge graph excels at harmonizing complex data and flexibly modeling massive real-world structures and their business logic. With Neo4j’s graph database at the foundation, organizations can manifest a digital twin in any structure or process within any industry, leading to a wide variety of use cases. Neo4j’s graph data platform provides the flexibility, performance, and analytical capabilities needed to build, manage, and query digital twins on an enterprise scale economically, unifying data across myriad sources to provide the most business value. Graphs bring the most advanced analytics to digital twins and support powerful queries, as well as data science and machine learning techniques from algorithms to embeddings.
Emphasizing on the value digital twin technologies unlock and bring to a wide range of industries, Maya Natarajan, Senior Director of Product Marketing at Neo4j, said, “While the technology for digital twins is only just emerging, it is quickly becoming popular in corporate strategy, giving us the power to understand the present and predict the future.”
“Today, we can use digital twins to create digitized model simulations of all parts of our businesses, from supply chains to HR systems, auto manufacturing, and more. With digital twin deployed to its full potential with graph technology, organizations can enable a new, intelligent, resilient capability and ultimately gain the most business value,” she added.
