According to Intent Market Research, the Knowledge Graph market is expected to increase from USD 856 million in 2023 to USD 3275 million by 2030, with a CAGR of 21.1% over the forecast period (2024-2030).
The report's primary objective is to estimate the present market potential in terms of total addressable market for all categories, sub-segments, and geographies. Throughout the process, all high-growth and emerging technologies were discovered and examined to determine their impact on the existing and future markets. The paper also analyzes key players, business gaps, and purchasing patterns. This knowledge is critical for generating efficient marketing strategies and designing products or services that fulfill the needs of the target market.
Major Players:
· Google
· Amazon
· Microsoft
· Neo4j
· IBM
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Evolution of the Knowledge Graph Industry
The concept of the Knowledge Graph gained prominence with Google's introduction of its Knowledge Graph in 2012. Google's initiative aimed to enhance search results by understanding the context and relationships between entities on the web. Since then, the Knowledge Graph concept has evolved significantly, extending beyond search engines to diverse sectors such as healthcare, finance, e-commerce, and more.
Growth Drivers:
- Data Explosion: The exponential growth of structured and unstructured data fuels the demand for Knowledge Graph solutions, as organizations seek efficient ways to manage and derive value from their information assets.
- AI and Machine Learning: Advances in AI and machine learning techniques enhance the capabilities of Knowledge Graphs, enabling automated reasoning, natural language understanding, and predictive analytics.
- Industry Applications: Knowledge Graphs find applications across diverse industries, including healthcare (patient data integration), finance (fraud detection), and logistics (supply chain optimization), driving adoption and market growth.
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Segmentation:
- Type of Knowledge Graph Solutions:
- Generic Knowledge Graph Platforms: Offer general-purpose tools for building and querying Knowledge Graphs across domains.
- Industry-Specific Solutions: Tailored Knowledge Graph solutions catering to the unique needs of specific sectors, such as healthcare, finance, or retail.
Deployment Models:
- On-Premises: Deployed within the organization's infrastructure, offering greater control over data privacy and security.
- Cloud-Based: Hosted on cloud platforms, providing scalability, flexibility, and ease of management.
Functionalities:
- Data Integration: Capabilities to integrate and harmonize data from disparate sources into a unified Knowledge Graph.
- Querying and Analysis: Tools for querying, analyzing, and visualizing the interconnected data within the Knowledge Graph.
- AI Integration: Integration with AI and machine learning algorithms for advanced analytics, prediction, and automation.
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