The global Knowledge Graph market is anticipated to expand from USD 1,068.4 million in 2024 to USD 6,938.4 million by 2030, registering a Compound Annual Growth Rate (CAGR) of 36.6% during the forecast period. Stringent data regulations, such as GDPR and CCPA, place significant pressure on businesses to manage data transparently and compliantly. Knowledge graphs, which represent structured and interconnected data, offer a solution by enhancing traceability and accountability. They enable companies to track data flows, manage consent, and handle personal information appropriately by linking data points and illustrating relationships across various systems. This enhanced visibility allows organizations to respond swiftly to data access requests, audits, and compliance checks, ensuring regulatory compliance and fostering customer trust.
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“By vertical, the BFSI segment to hold the largest market size during the forecast period.”
The knowledge graphs serve as a strong foundation for relating customer data, transactions history, credit scores, and risk profiles within the BFSI (Banking, Financial Services, and Insurance) sector, allowing the exact relationship mapping and insights. These are also employed in fraud detection through real-time identification of hidden patterns and for regulatory compliance with standards such as AML (Anti Money Laundering) and KYC (know Your Customer), where data can be traced and is transparent. In banking, knowledge graphs facilitate credit risk analysis which makes the process of loan approval more efficient, in insurance by linking policies, claims data, and fraud indicators thus optimizing claims processing. All these will, when combined with other data points, produce AI-powered applications: personalized advice-based solutions on finances and intelligent virtual assistants, which will create operational efficiency and improved customer experience in BFSI.
“Virtual assistants, self-service data, and digital asset discovery segment to have the highest growth during the forecast period.”
Knowledge graphs are essential for building virtual assistants, self-service data platforms, and even digital asset discovery, for they build interconnected data networks that help in enhancing the searchability and insights. Virtual assistants use knowledge graphs to provide context-sensitive responses that improve user interactions and provide tailored recommendations. Self-service data platforms use knowledge graphs to allow business users to access and analyze complex datasets without technical help, which helps them to make better decisions. They make the identification and classification of digital resources, such as documents or media, easier through linking metadata and content relationships for the discovery of digital assets. This capability enables effective resource management, innovation, and improvement in user experience in areas such as content creation, research, and enterprise workflows.
“Asia Pacific is expected to witness the highest market growth rate during the forecast period.”
The knowledge graph landscape is rapidly evolving in Asia Pacific, with initiatives across various domains. In December 2022, the National Library Board (NLB), Singapore, launched a Linked Data-based Semantic Knowledge Graph to merge resources from libraries and archives using BIBFRAME and Schema.org vocabularies for seamless updating and improved data quality. HydroKG in Australia merges hydrologic data from resources such as GeoFabric and HydroATLAS that allow for pinpoint queries on water bodies and river networks, enabling better environmental management. Japan uses knowledge graphs in manufacturing for supply chain optimization and South Korea uses it in telecommunications to enhance the customer experience through personalized AI.
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Unique Features in the Knowledge Graph Market
Knowledge graphs excel in integrating data from diverse sources, breaking down data silos, and establishing relationships between disparate data points. This interconnected structure enables organizations to derive insights by visualizing relationships and dependencies that traditional data systems might overlook.
Unlike traditional databases, knowledge graphs incorporate semantic metadata, allowing them to understand the meaning and context of data. This feature facilitates more accurate query results, intelligent recommendations, and advanced analytics by interpreting the relationships between entities.
Knowledge graphs are designed to scale seamlessly as data grows. Their flexibility enables the addition of new data and relationships without significant reengineering, making them ideal for dynamic industries where information is constantly evolving.
By linking data points and maintaining a transparent structure, knowledge graphs enable organizations to manage consent, ensure proper data handling, and demonstrate accountability. This is particularly valuable in meeting regulatory requirements such as GDPR and CCPA.
Knowledge graphs support real-time data analysis, enabling organizations to quickly uncover patterns, detect anomalies, and derive actionable insights. This capability is crucial for industries like finance, healthcare, and e-commerce, where rapid decision-making is essential.
Major Highlights of the Knowledge Graph Market
Stricter data protection laws, such as GDPR and CCPA, are compelling businesses to adopt knowledge graphs to ensure compliance. These tools enhance data transparency, traceability, and accountability, enabling organizations to handle sensitive data responsibly.
As organizations grapple with siloed data and fragmented systems, knowledge graphs have emerged as a powerful solution for integrating disparate datasets. Their ability to unify and connect data sources is driving their adoption in industries like healthcare, finance, and retail.
The integration of knowledge graphs with AI and machine learning is transforming industries. By providing structured, contextual data, knowledge graphs enhance AI algorithms’ accuracy and relevance, supporting applications like predictive analytics, natural language processing, and intelligent recommendations.
Companies are leveraging knowledge graphs to deliver personalized and dynamic customer experiences. By analyzing relationships and preferences, knowledge graphs enable tailored marketing campaigns, product recommendations, and customer support solutions.
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Top Companies in the Knowledge Graph Market
IBM Corporation (US), Oracle (US), Microsoft Corporation (US), AWS (US), Neo4j (US), Progress Software (US), TigerGraph (US), Stardog (US), Franz Inc (US), Ontotext (Bulgaria), Openlink Software (US), Graphwise (US), Altair (US), Bitnine ( South Korea) ArangoDB (US), Fluree (US), Memgraph UK), GraphBase (Australia), Metaphacts (Germany), Relational AI (US), Wisecube (US), Smabbler (Poland), Onlim (Austria), Graphaware (UK), Diffbot (US), Eccenca (Germany), Conversight (US), Semantic Web Company (Austria), ESRI (US), Datavid (UK), and SAP (Germany). The market players have adopted various strategies to strengthen their Knowledge Graph market position. Organic and inorganic strategies have helped the market players expand globally by providing Knowledge Graph solutions & services.
Stardog
Stardog is a provider of enterprise data unification platform renowned for its robust capabilities in connecting, querying, and searching diverse data sources regardless of their structure or location. With a strong emphasis on its graph-based data model, Stardog enables organizations to build comprehensive knowledge graphs that capture intricate data relationships and dependencies. This powerful feature is instrumental in facilitating advanced applications in artificial intelligence, machine learning, and data analytics. With its robust graph database solutions, the company facilitates the integration of diverse data sources into a unified knowledge graph, empowering organizations to glean valuable insights and optimize decision-making. Stardog partnered with Caden to provide semantic and data virtualization capabilities for its Open Data platform, enabling users to manage and monetize personal data while connecting disparate data sets into comprehensive knowledge graphs.
Franz Inc.
Franz Inc. is a provider of Graph Database technology; it specializes in developing and deploying Knowledge Graph and Data Fabric solutions. It offers AllegroGraph, a high-performance, transactional, and highly scalable Graph Database, which provides the solid storage layer for powerful enterprise grade Knowledge Graph and AI applications. Franz Inc’s competitive edge lies in its emphasis on scalability, resilience, and its adeptness in managing complex data analytics tasks. The company has become a trusted collaborator for diverse entities, including enterprises, research institutions, and government agencies, seeking to harness advanced data management and analytical capabilities.
Altair Engineering Inc
Altair Engineering Inc. is an American multinational information technology company. It provides software and cloud solutions for simulation, IoT, high performance computing (HPC), data analytics, and AI. Altair has integrated powerful knowledge graph capabilities into its existing platforms, notably Altair RapidMiner and Altair Graph Studio. RapidMiner enhances data analytics and Al initiatives, allowing organizations to modernize their operations effectively. Meanwhile, Graph Studio provides an all-in-one toolset for building and managing knowledge graphs to integrate structured and unstructured data seamlessly. Altair acquired Cambridge Semantics to enhance its data analytics and AI capabilities. This acquisition integrates Cambridge’s graph-powered data fabric technology into Altair’s RapidMiner platform, enabling the creation of comprehensive knowledge graphs that improve data management and support generative AI applications.
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