The Knowledge Graph Market is expected to grow rapidly, reaching $2.4 billion by 2028 (up from $0.9 billion in 2023). This increase (21.8% CAGR) reflects the development of innovative solutions to manage the growing volume of data.
Knowledge graphs are a powerful new approach that provides a structured framework for integrating, organising, and analysing data from multiple sources.
To know about the assumptions considered for the study download the pdf brochure
Knowledge Graph Market is poised for substantial growth in the coming years, driven by several key factors:
-
Increasing demand for data integration and interoperability.
-
Rising adoption of AI and ML technologies.
-
Growing need for enhanced data analytics and insights.
-
Expansion of personalized recommendation systems.
-
Proliferation of real-time data processing requirements.
-
Advancements in natural language processing (NLP) and semantic search.
-
Enhanced focus on enterprise knowledge management.
-
Rising investments in healthcare and life sciences applications.
-
Growing cybersecurity concerns and requirements.
-
Increased use of hybrid and multi-cloud environments.
Increasing Demand for Data Integration and Interoperability:
-
As organizations collect vast amounts of data from various sources, there is a growing need to integrate and make sense of this data in a unified manner. Knowledge graphs facilitate seamless data integration and interoperability, breaking down silos and enabling a holistic view of information. This capability is essential for businesses looking to leverage comprehensive data insights for strategic decision-making and operational efficiency.
Rising Adoption of AI and ML Technologies:
-
Artificial Intelligence (AI) and Machine Learning (ML) technologies rely heavily on high-quality, interconnected data to function effectively. Knowledge graphs provide the structured data framework needed to enhance AI and ML capabilities, such as predictive analytics, automated reasoning, and intelligent search. The synergy between knowledge graphs and AI/ML is driving their adoption across various industries.
Growing Need for Enhanced Data Analytics and Insights:
-
The ability to derive meaningful insights from complex data sets is a key competitive advantage. Knowledge graphs enable advanced data analytics by revealing relationships and patterns that are not easily identifiable through traditional data management methods. This enhanced analytical capability helps organizations make data-driven decisions, optimize operations, and identify new opportunities.
Expansion of Personalized Recommendation Systems:
-
Personalized experiences are becoming a standard expectation among consumers. Knowledge graphs are crucial in powering recommendation systems by connecting diverse data points about user behavior, preferences, and interactions. This interconnected data allows businesses to deliver highly tailored recommendations, improving customer satisfaction and driving engagement.
Proliferation of Real-time Data Processing Requirements:
-
In today’s fast-paced environment, real-time data processing is essential for timely decision-making and responsive business operations. Knowledge graphs support real-time querying and updates, enabling applications such as fraud detection, dynamic pricing, and real-time customer interactions. This capability is increasingly important for maintaining competitiveness in various sectors.
Advancements in Natural Language Processing (NLP) and Semantic Search:
-
Natural Language Processing (NLP) and semantic search technologies are becoming more sophisticated, requiring robust underlying data structures. Knowledge graphs enhance these technologies by providing context and understanding to queries, improving the accuracy and relevance of search results and conversational AI applications.
Enhanced Focus on Enterprise Knowledge Management:
-
Efficient knowledge management is critical for organizations to leverage their collective intelligence and maintain a competitive edge. Knowledge graphs help in organizing, storing, and retrieving information more effectively, facilitating better knowledge sharing and collaboration across the enterprise.
Rising Investments in Healthcare and Life Sciences Applications:
-
The healthcare and life sciences sectors are increasingly adopting knowledge graphs to integrate disparate data sources such as electronic health records, research data, and clinical trials information. This integration supports personalized medicine, improves patient outcomes, and accelerates drug discovery processes, driving growth in these industries.
Growing Cybersecurity Concerns and Requirements:
Cybersecurity is a top priority for many organizations, and knowledge graphs play a crucial role in this area. By mapping relationships between various network entities and identifying potential threats, knowledge graphs enhance threat detection and prevention capabilities, providing a more robust security posture.
Increased Use of Hybrid and Multi-cloud Environments :
-
Organizations are adopting hybrid and multi-cloud strategies to leverage the best capabilities of different cloud providers. Knowledge graphs offer the flexibility and scalability needed to manage and analyze data across these environments, ensuring resilience, optimizing costs, and providing a unified data view.
Knowledge Graph Market Share - Global Industry Landscape:
Oracle (US):
-
Market Share: Oracle holds a significant market share in the knowledge graph market, leveraging its extensive experience and large customer base in the database and enterprise software sectors.
-
Key Offering: Oracle's primary knowledge graph offering is Oracle Spatial and Graph, which is integrated into Oracle Database. This solution provides tools for managing and analyzing spatial and graph data, supporting complex queries and large-scale data analytics. Oracle Spatial and Graph is used in various applications, including geospatial data analysis, social network analysis, and fraud detection.
Stardog (US):
-
Market Share: Stardog is a prominent player in the knowledge graph market, known for its innovation and focus on enterprise solutions. It has a growing market share, particularly among organizations seeking flexible and scalable knowledge graph platforms.
-
Key Offering: Stardog's main product is the Stardog Enterprise Knowledge Graph platform. It offers a robust, scalable solution for data integration, unification, and semantic reasoning. The platform supports SPARQL, RDF, and other graph standards, making it suitable for applications in data governance, customer 360 views, and AI-driven analytics.
Franz Inc (US):
-
Market Share: Franz Inc. has a niche but influential presence in the knowledge graph market, particularly in industries that require advanced graph database capabilities and semantic technology.
-
Key Offering: Franz Inc.'s flagship product is AllegroGraph, a high-performance, scalable graph database that supports RDF and SPARQL. AllegroGraph is designed for handling complex, connected data, and is widely used in applications such as healthcare analytics, cybersecurity, and knowledge management. It emphasizes capabilities like reasoning, inferencing, and real-time data integration.
Ontotext (Bulgaria):
-
Market Share: Ontotext is a notable European player in the knowledge graph market, with a solid market share in the region and a growing global presence. It is recognized for its expertise in semantic technology and linked data.
-
Key Offering: Ontotext's key offering is the GraphDB database, which supports RDF and SPARQL for creating and managing knowledge graphs. GraphDB is known for its strong semantic capabilities, making it ideal for applications in publishing, life sciences, and cultural heritage. Ontotext also provides various tools and services to enhance data integration, curation, and analytics.
Related Reports:
Knowledge Graph Market by Offering (Solutions, Services), By Data Source (Structured, Unstructured, Semi-structured), Industry (BFSI, IT & ITeS, Telecom, Healthcare), Model Type, Application, Type and Region - Global Forecast to 2028