세계의 지식 그래프 시장 : 제공별, 모델 유형별, 용도별, 업계별, 지역별 - 예측(-2030년)
Knowledge Graph Market by Solution (Enterprise Knowledge Graph Platform, Graph Database Engine, Knowledge Management Toolset), Model Type (Resource Description Framework (RDF) Triple Stores, Labeled Property Graph) - Global Forecast to 2030
상품코드:1633537
리서치사:MarketsandMarkets
발행일:2025년 01월
페이지 정보:영문 360 Pages
라이선스 & 가격 (부가세 별도)
ㅁ Add-on 가능: 고객의 요청에 따라 일정한 범위 내에서 Customization이 가능합니다. 자세한 사항은 문의해 주시기 바랍니다.
한글목차
지식 그래프의 시장 규모는 2024년 10억 6,840만 달러, 2030년에는 69억 3,840만 달러에 달할 것으로 예상되며, 연평균 성장률(CAGR) 36.6%를 기록할 것으로 예측됩니다.
AI를 통한 지능형 지식 그래프 구축은 조직이 대규모 데이터세트를 다루는 방식을 변화시킬 것으로 예상됩니다. 서로 다른 데이터 포인트 간의 관계를 식별하고 추출할 때 사람의 개입을 크게 줄일 수 있습니다. 자동화에는 자연어 처리(NLP), 머신러닝 알고리즘 등 대부분의 AI 기반 도구가 비정형 또는 정형 데이터를 자동으로 해석하고, 관련 패턴을 식별하고, 이러한 관련 정보를 연관시키는 프로세스가 포함됩니다. 이러한 자동화를 통해 그래프 구축 속도가 빨라지고 정확도가 향상되며, 그래프에 표현된 관계가 최종사용자에게 최대한 적절하고 최신의 정보를 제공할 수 있습니다.
조사 범위
조사 대상 연도
2019-2030년
기준 연도
2024년
예측 기간
2024-2030년
검토 단위
금액(100만 달러)
부문별
제품별, 모델 유형별, 용도별, 산업별, 지역별
대상 지역
북미, 유럽, 아시아태평양, 중동 및 아프리카, 라틴아메리카, 기타 지역
그래프 데이터베이스 엔진은 그래프 관계(에지)에 의해 연관된 그래프 데이터 엔티티(노드)의 효율적인 저장, 관리, 검색을 위해 특별히 설계된 특수한 유형의 데이터베이스입니다. 그래프 데이터베이스는 기존 관계형 시스템처럼 데이터를 테이블로 정리하는 것이 아니라 관계로 정리하기 때문에 소셜 네트워크, 추천 엔진, 부정행위 탐지 등 데이터의 관계가 가장 중요한 애플리케이션 시나리오에서 유용합니다. 또한, 복잡하고 링크가 많은 데이터세트를 빠르게 쿼리하고 트래버스할 수 있어 보다 자연스럽고 직관적이며 유연한 데이터 쿼리 메커니즘을 구현합니다. 또한, SPARQL이나 Cypher와 같은 그래프 전용 쿼리 언어를 지원하며, 관계형 쿼리에 최적화되어 있어 그래프 애플리케이션의 성능과 확장성을 향상시킵니다.
지식 그래프 서비스는 지식 그래프 솔루션의 도입, 강화, 유지보수를 위한 전문 서비스 및 매니지드 서비스를 포함합니다. 전문 서비스는 전략 설계 및 개발, 데이터 통합, 비즈니스와 관련된 맞춤형 지식 그래프 제작에 대한 컨설팅으로 구성됩니다. 반면 매니지드 서비스는 지식 그래프 플랫폼의 성능, 확장성, 보안에 대한 지원, 유지보수, 모니터링을 제공합니다. 이 서비스들은 각기 다른 방식으로 고객이 더 나은 데이터, 의사결정 인텔리전스, AI를 얻는다는 점에서 지식 그래프를 조달하고 이점을 얻을 수 있도록 돕습니다.
2021년 Neo4j는 Graphs4APAC 이니셔티브(Graphs4APAC Initiative)를 출범하여 아시아태평양 전역에 걸쳐 그래프 기술 도입 및 적용을 지원하기 위한 노력과 혁신이 활발히 이루어지고 있습니다. 전문가들에게 무료 교육, 자료, 도구를 제공했습니다. 이 오픈 소스 이니셔티브는 인도네시아와 싱가포르에서 성공적으로 시행되고 있습니다. 후지쯔는 또한 지식 그래프를 생성하고 그러한 그래프를 추론할 수 있는 전용 대규모 언어 모델(LLM)을 만드는 데 중점을 둔 GENIAC(Generative AI Accelerator Challenge) 프로그램을 통해 인공지능이 제공하는 공급되는 지식 그래프의 프레임워크를 확장하기 위해 노력하고 있습니다. 이는 이 지역이 혁신적인 플랫폼과 데이터 기반 솔루션에 지식 그래프를 적용하는 데 얼마나 많은 관심을 기울이기 시작했는지를 보여주는 중요한 지표입니다.
세계의 지식 그래프 시장에 대해 조사했으며, 제공별, 모델 유형별, 용도별, 산업별, 지역별 동향, 시장 진입 기업 프로파일 등의 정보를 정리하여 전해드립니다.
목차
제1장 소개
제2장 조사 방법
제3장 주요 요약
제4장 주요 인사이트
제5장 시장 개요와 업계 동향
소개
시장 역학
고객 비즈니스에 영향을 미치는 동향/혼란
가격 분석
공급망 분석
생태계
기술 분석
특허 분석
2024-2025년의 주요 회의와 이벤트
규제 상황
Porter's Five Forces 분석
주요 이해관계자와 구입 기준
지식 그래프 간단한 역사
지식 그래프를 구축하는 절차
AI/생성형 AI가 지식 그래프 시장에 미치는 영향
투자와 자금 조달 시나리오
사례 연구 분석
제6장 지식 그래프 시장, 제공별
소개
솔루션
서비스
제7장 지식 그래프 시장, 모델 유형별
소개
자원 기술 프레임워크(RDF) 트리플 스토어
라벨 부착 프로퍼티 그래프(LPG)
제8장 지식 그래프 시장, 용도별
소개
데이터 거버넌스와 마스터 데이터 관리
데이터 분석과 비즈니스 인텔리전스
지식과 컨텐츠 관리
가상 비서, 셀프서비스 데이터, 디지털 자산 발견
제품 및 구성 관리
인프라와 자산 관리
프로세스 최적화와 자원 관리
리스크 관리, 컴플라이언스, 규제 보고
시장과 고객 정보, 판매 최적화
기타
제9장 지식 그래프 시장, 업계별
소개
BFSI
소매·E-Commerce
헬스케어, 생명과학, 의약품
통신·테크놀러지
정부
제조업·자동차
미디어·엔터테인먼트
에너지, 유틸리티, 인프라
여행·호스피탈리티
운송과 물류
기타
제10장 지식 그래프 시장, 지역별
소개
북미
북미 : 거시경제 전망
미국
캐나다
유럽
유럽 : 거시경제 전망
영국
독일
프랑스
이탈리아
스페인
북유럽 국가
기타
아시아태평양
아시아태평양 : 거시경제 전망
중국
일본
인도
한국
호주와 뉴질랜드
기타
중동 및 아프리카
중동 및 아프리카 : 거시경제 전망
GCC 국가
남아프리카공화국
기타
라틴아메리카
라틴아메리카 : 거시경제 전망
브라질
멕시코
아르헨티나
기타
제11장 경쟁 구도
소개
주요 진출 기업 전략/강점
매출 분석
시장 점유율 분석
시장 순위 분석
기업 평가 매트릭스 : 주요 진출 기업, 2023년
기업 평가 매트릭스 : 스타트업/중소기업, 2024년
경쟁 시나리오와 동향
브랜드/제품 비교
주요 지식 그래프 솔루션 프로바이더 기업 평가와 재무 지표
제12장 기업 개요
주요 진출 기업
NEO4J
AMAZON WEB SERVICES, INC
TIGERGRAPH
GRAPHWISE
RELATIONALAI
IBM
MICROSOFT
SAP
ORACLE
STARDOG
ONTOTEXT
FRANZ INC.
ALTAIR
PROGRESS SOFTWARE CORPORATION
ESRI
SEMANTIC WEB COMPANY
OPENLINK SOFTWARE
중소기업/스타트업
DATAVID
GRAPHBASE
CONVERSIGHT
ECCENCA
ARANGODB
FLUREE
DIFFBOT
BITNINE
MEMGRAPH
GRAPHAWARE
ONLIM
SMABBLER
WISECUBE
METAPHACTS
제13장 인접/관련 시장
제14장 부록
ksm
영문 목차
영문목차
The Knowledge Graph market is estimated at USD 1,068.4 million in 2024 to USD 6,938.4 million by 2030, at a Compound Annual Growth Rate (CAGR) of 36.6%. The construction of intelligent knowledge graphs through AI is expected to change how organizations deal with large datasets. The effort of human intervention is drastically reduced when it comes to identifying and extricating relationships between different data points. The automation includes the processes carried out by most types of AI-driven tools such as natural language processing (NLP), machine learning algorithms, etc., to automatically interpret, unstructured or structured data, identify relevant patterns, and correlate such relevant information. This automation speeds up the construction of the graphs and at the same time increases accuracy, ensuring that the relationships represented in it are as relevant and up to date as possible to an end user.
Scope of the Report
Years Considered for the Study
2019-2030
Base Year
2024
Forecast Period
2024-2030
Units Considered
Value (USD Million)
Segments
By Solutions, Services, Model Type, Vertical.
Regions covered
North America, Europe, Asia Pacific, Middle East & Africa, Latin America
"By solution, Graph Database Engine segment to hold the largest market size during the forecast period."
Graph Database Engine is a specialized type of database, designed specifically for the efficient storage, management and retrieval of graph data entities (nodes) related by graph relationships (edges). Graph databases do not organize data in tables as in traditional relational systems, but rather as relationships, making them useful in application scenarios where data relationships are paramount, such as social networks, recommendation engines, and fraud detection. It allows high-speed querying and traversing complex and heavily linked datasets, thus enables a more natural, intuitive, and flexible mechanism of data querying. It further supports graph-specific query languages such as SPARQL and Cypher, which are optimized for querying relationships, thus affording better performance and scalability for graph applications.
"The services segment to register the fastest growth rate during the forecast period."
Knowledge graph services encompass professional and managed services to an organization for deploying, enhancing, and maintaining knowledge graph solutions. Professional services consist of consulting on the design and development of a strategy, integration of the data, and the creation of a custom-built knowledge graph relevant to a business. On the other hand, managed services offer support maintenance, and monitoring of the knowledge graph platform for performance, scalability, and security. These services, in their own way, assist clients in sourcing knowledge graphs to their advantage in terms of getting better data, decision intelligence, and AI, and without the burden of their internal management, which is a resource-intensive and cumbersome process.
"Asia Pacific to witness the highest market growth rate during the forecast period."
In Asia Pacific, the landscape is characterized by initiatives and innovations that try to help adopt and apply graph technologies across the region. In 2021, Neo4j launched Graphs4APAC initiative, which provides free training, materials, and tools to professionals across Asia Pacific to develop and improve their knowledge and skills in graph technology. This open-source initiative encourages collaborative and local adaptation, and has been successfully implemented in, Indonesia and Singapore. Fujitsu, also, strives to expand the frameworks of knowledge graphs fed by artificial intelligence in the Generative AI Accelerator Challenge (GENIAC) program that focuses on producing dedicated large language models (LLMs) that generate knowledge graphs and allow for inferring such graphs. These are emerging indicators that are significant in portraying how much the region has begun to pay attention to applying knowledge graphs across innovative platforms and data-driven solutions.
In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the Knowledge Graph market.
By Company Type: Tier 1 - 40%, Tier 2 - 35%, and Tier 3 - 25%
By Designation: C-level -40%, D-level - 35%, and Others - 25%
By Region: North America - 35%, Europe - 40%, Asia Pacific - 20, RoW-5%
The major players in the Knowledge Graph market include 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), Datavid (UK), and SAP (Germany), 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). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, enhancements, and acquisitions to expand their Knowledge Graph market footprint.
Research Coverage
The market study covers the Knowledge Graph market size across different segments. It aims at estimating the market size and the growth potential across various segments, including by offering (solutions (enterprise knowledge graph platform, graph database engine, knowledge management toolset), services ( professional services, managed services), by model type (Resource Description Framework (RDF) Triple Stores, Labeled Property Graph (LPG)), by applications (data governance and master data management, data analytics and business intelligence, knowledge and content management , virtual assistants, self-service data and digital asset discovery, product and configuration management, infrastructure and asset management, process optimization and resource management, risk management, compliance, regulatory reporting, market and customer intelligence, sales optimization, other applications), by vertical (Banking, Financial Services, and Insurance (BFSI), retail and eCommerce, healthcare, life sciences, and pharmaceuticals telecom and technology, government, manufacturing and automotive, media & entertainment, energy, utilities and infrastructure, travel and hospitality, transportation and logistics, other vertical), and Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The study includes an in-depth competitive analysis of the leading market players, their company profiles, key observations related to product and business offerings, recent developments, and market strategies.
Key Benefits of Buying the Report
The report will help the market leaders/new entrants with information on the closest approximations of the global Knowledge Graph market's revenue numbers and subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market's pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (rising demand for AI/generative AI solutions, rapid growth in data volume and complexity, growing demand for semantic search), restraints (data quality and Integration challenges, scalability Issues) opportunities (data unification and rapid proliferation of knowledge graphs, increasing adoption in healthcare and life sciences), and challenges (lack of expertise and awareness, standardization and interoperability) influencing the growth of the Knowledge Graph market.
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Knowledge Graph market.
Market Development: The report provides comprehensive information about lucrative markets and analyses the Knowledge Graph market across various regions.
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the Knowledge Graph market.
Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading include include 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).
TABLE OF CONTENTS
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 STUDY SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 Primary interviews with experts
2.1.2.2 Breakdown of primary interviews
2.1.2.3 Key insights from industry experts
2.2 MARKET SIZE ESTIMATION
2.2.1 TOP-DOWN APPROACH
2.2.1.1 Supply-side analysis
2.2.2 BOTTOM-UP APPROACH
2.2.2.1 Demand-side analysis
2.3 DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
2.5 RESEARCH LIMITATIONS
2.6 RISK ASSESSMENT
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR KEY PLAYERS IN KNOWLEDGE GRAPH MARKET
4.2 KNOWLEDGE GRAPH MARKET, BY OFFERING
4.3 KNOWLEDGE GRAPH MARKET, BY SERVICE
4.4 KNOWLEDGE GRAPH MARKET, BY MODEL TYPE
4.5 KNOWLEDGE GRAPH MARKET, BY APPLICATION 60
4.6 KNOWLEDGE GRAPH MARKET, BY VERTICAL
4.7 NORTH AMERICA: KNOWLEDGE GRAPH MARKET, SOLUTIONS AND SERVICES
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Rising demand for AI/generative AI solutions
5.2.1.2 Rapid growth in data volume and complexity
5.2.1.3 Growing demand for semantic search
5.2.2 RESTRAINTS
5.2.2.1 Data quality and integration challenges
5.2.2.2 Navigation of saturated data management tool landscape
5.2.2.3 Scalability issues
5.2.3 OPPORTUNITIES
5.2.3.1 Leveraging LLMs to reduce knowledge graph construction costs
5.2.3.2 Data unification and rapid proliferation of knowledge graphs
5.2.3.3 Increasing adoption in healthcare and life sciences to revolutionize data management and enhance patient outcomes
5.2.4 CHALLENGES
5.2.4.1 Lack of expertise and awareness
5.2.4.2 Standardization and interoperability
5.2.4.3 Difficulty in demonstrating full value of knowledge graphs through single use cases
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.4 PRICING ANALYSIS
5.4.1 PRICE TREND OF KEY PLAYERS, BY SOLUTION
5.4.2 INDICATIVE PRICING ANALYSIS OF KEY PLAYERS
5.5 SUPPLY CHAIN ANALYSIS
5.6 ECOSYSTEM
5.7 TECHNOLOGY ANALYSIS
5.7.1 KEY TECHNOLOGIES
5.7.1.1 Graph Databases (GDB)
5.7.1.2 Semantic web technologies
5.7.1.3 Generative AI and Natural Language Processing (NLP)
5.7.1.4 GraphRAG
5.7.2 COMPLEMENTARY TECHNOLOGIES
5.7.2.1 Artificial Intelligence (AI) and Machine Learning (ML)
5.7.2.2 Big data
5.7.2.3 Graph Neural Networks (GNNS)
5.7.2.4 Cloud computing
5.7.2.5 Vector databases and Full-Text Search Engines (FTS)
5.7.2.6 Multi-model databases
5.7.3 ADJACENT TECHNOLOGIES
5.7.3.1 Digital twin
5.7.3.2 Internet of Things (IoT)
5.7.3.3 Blockchain
5.7.3.4 Edge computing
5.8 PATENT ANALYSIS
5.8.1 METHODOLOGY
5.8.1.1 List of major patents
5.9 KEY CONFERENCES AND EVENTS, 2024-2025
5.10 REGULATORY LANDSCAPE
5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.10.2 KEY REGULATIONS
5.10.2.1 North America
5.10.2.1.1 SCR 17: Artificial Intelligence Bill (California)
5.10.2.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut)
5.10.2.1.3 National Artificial Intelligence Initiative Act (NAIIA)
5.10.2.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada
5.10.2.2 Europe
5.10.2.2.1 The European Union (EU) - Artificial Intelligence Act (AIA)
5.10.2.2.2 EU Data Governance Act
5.10.2.2.3 General Data Protection Regulation (Europe)
5.10.2.3 Asia Pacific
5.10.2.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China)
5.10.2.3.2 The National AI Strategy (Singapore)
5.10.2.3.3 The Hiroshima AI Process Comprehensive Policy Framework (Japan)
5.10.2.4 Middle East & Africa
5.10.2.4.1 The National Strategy for Artificial Intelligence (UAE)
5.10.2.4.2 The National Artificial Intelligence Strategy (Qatar)
5.10.2.4.3 The AI Ethics Principles and Guidelines (Dubai)
5.10.2.5 Latin America
5.10.2.5.1 The Santiago Declaration (Chile)
5.10.2.5.2 The Brazilian Artificial Intelligence Strategy (EBIA)
5.11 PORTER'S FIVE FORCES ANALYSIS
5.11.1 THREAT OF NEW ENTRANTS
5.11.2 THREAT OF SUBSTITUTES
5.11.3 BARGAINING POWER OF BUYERS
5.11.4 BARGAINING POWER OF SUPPLIERS
5.11.5 INTENSITY OF COMPETITIVE RIVALRY 92
5.12 KEY STAKEHOLDERS & BUYING CRITERIA
5.12.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.12.2 BUYING CRITERIA
5.13 BRIEF HISTORY OF KNOWLEDGE GRAPH
5.14 STEPS TO BUILD KNOWLEDGE GRAPH
5.14.1 DEFINE OBJECTIVES
5.14.2 ENGAGE STAKEHOLDERS
5.14.3 IDENTIFY KNOWLEDGE DOMAIN
5.14.4 GATHER AND ANALYZE DATA
5.14.5 CLEAN AND PREPROCESS DATA
5.14.6 CREATE SEMANTIC DATA MODEL
5.14.7 SCHEMA DEFINITION
5.14.8 DATA INTEGRATION
5.14.9 HARMONIZATION OF DATA
5.14.10 BUILD KNOWLEDGE GRAPH
5.14.11 AUGMENT GRAPH
5.14.12 TESTING AND VALIDATION
5.14.13 MAXIMIZE USABILITY
5.14.14 CONTINUOUS MAINTENANCE AND EVOLUTION
5.15 IMPACT OF AI/GENERATIVE AI ON KNOWLEDGE GRAPH MARKET
5.15.1 USE CASES OF GENERATIVE KNOWLEDGE GRAPH
5.16 INVESTMENT AND FUNDING SCENARIO
5.17 CASE STUDY ANALYSIS
5.17.1 TRANSMISSION SYSTEM OPERATOR LEVERAGED ONTOTEXT'S SOLUTIONS TO MODERNIZE ASSET MANAGEMENT
5.17.2 BOSTON SCIENTIFIC STREAMLINED MEDICAL SUPPLY CHAIN USING NEO4J'S GRAPH DATA SCIENCE SOLUTION
5.17.3 NATIONAL RETAIL CHAIN FROM UK ENHANCED OPERATIONAL EFFICIENCY USING TIGERGRAPHS'S SOLUTION
5.17.4 SCHNEIDER ELECTRIC USED STARDOG TO LEAD SMART BUILDING TRANSFORMATION
5.17.5 MEDIA ORGANIZATION USED PROGRESS SEMAPHORE TO CLASSIFY CONTENT FOR BETTER AUDIENCE ENGAGEMENT
5.17.6 YAHOO7 REPRESENTED CONTENT WITHIN KNOWLEDGE GRAPH WITH ASSISTANCE OF BLAZEGRAPH
5.17.7 DATABASE GROUP HELPED SPRINGERMATERIALS ACCELERATE RESEARCH WITH SEMANTIC SEARCH
5.17.8 RFS OPTIMIZED ITS GLOBAL PRODUCT AND INVENTORY MANAGEMENT BY USING ECCENCA'S SOLUTION 104
6 KNOWLEDGE GRAPH MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERINGS: KNOWLEDGE GRAPH MARKET DRIVERS
6.2 SOLUTIONS
6.2.1 SPIKE IN DEMAND FOR SOPHISTICATED DATA MANAGEMENT AND ANALYSIS TO DRIVE MARKET
6.2.2 ENTERPRISE KNOWLEDGE GRAPH PLATFORM
6.2.2.1 Need to improve discovery of data, promote better decision-making, and enable real-time insights using semantic technologies to propel market
6.2.3 GRAPH DATABASE ENGINE
6.2.3.1 Features like parallel query execution and AI-driven insights in graph database engines to accelerate market growth
6.2.4 KNOWLEDGE MANAGEMENT TOOLSET
6.2.4.1 Knowledge management toolsets to enhance operational efficiency by enabling seamless access to organizational knowledge
8.2.1 NEED FOR ENHANCED SEARCH FUNCTIONALITIES TO BOLSTER MARKET GROWTH
8.3 DATA ANALYTICS & BUSINESS INTELLIGENCE
8.3.1 INTEGRATION OF KNOWLEDGE FROM SEVERAL DISCIPLINES AND OFFERING PERSONALIZED RECOMMENDATIONS TO BOOST MARKET GROWTH
8.4 KNOWLEDGE & CONTENT MANAGEMENT
8.4.1 WIDESPREAD KNOWLEDGE OF INTRICATE IDEAS THROUGH CROSS-DOMAIN INFORMATION INTEGRATION TO BOOST MARKET
8.5 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY
8.5.1 STREAMLINING OF TEAMWORK AND KNOWLEDGE EXCHANGE TO ACCELERATE MARKET GROWTH
8.6 PRODUCT & CONFIGURATION MANAGEMENT
8.6.1 NEED TO ENSURE ACCURACY AND REDUCES TIME-TO-MARKET ENHANCING CUSTOMER SATISFACTION TO FUEL MARKET GROWTH
8.7 INFRASTRUCTURE & ASSET MANAGEMENT
8.7.1 INFRASTRUCTURE AND ASSET MANAGEMENT TO REDUCE DOWNTIME AND EXTEND ASSET LIFECYCLES THROUGH INFORMED DECISION-MAKING PROCESSES
8.8 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT
8.8.1 NEED FOR REAL-TIME RESOURCE UTILIZATION MONITORING ACROSS DIFFERENT PROJECTS OR DEPARTMENTS TO PROPEL MARKET
8.9 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING
8.9.1 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING TO HELP MAP DATA FLOWS, RELATIONSHIPS, AND CONTROLS TO IDENTIFY VULNERABILITIES AND ENSURE COMPLIANCE
8.10 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION
8.10.1 NEED TO IDENTIFY TRENDS INFORMING TARGETED MARKETING STRATEGIES TO DRIVE MARKET
8.11 OTHER APPLICATIONS
9 KNOWLEDGE GRAPH MARKET, BY VERTICAL
9.1 INTRODUCTION
9.1.1 VERTICALS: KNOWLEDGE GRAPH MARKET DRIVERS
9.2 BFSI
9.2.1 INCREASING NEED TO MANAGE COMPLEX DATA TO SUPPORT MARKET GROWTH
9.2.2 CASE STUDY
9.2.2.1 Anti-money laundering (AML)
9.2.2.1.1 Major US Financial Institutions enhanced anti-money laundering capabilities with TigerGraph
9.2.2.2 Fraud detection & risk management
9.2.2.2.1 BNP Paribas Personal Finance achieved 20% fraud reduction with Neo4j Graph Database
9.2.2.3 Identity & access management
9.2.2.3.1 Intuit safeguarded data of 100 million customers with Neo4j
9.2.2.4 Risk management
9.2.2.4.1 Global bank enhanced trade surveillance for risk management in BFSI
9.2.2.5 Data integration & governance
9.2.2.5.1 Optimizing data integration and governance for real-time risk management and compliance
9.2.2.6 Operational resilience for bank IT systems
9.2.2.6.1 Basel Institute on Governance enhanced asset recovery and financial intelligence with knowledge graphs for global financial institutions with Onto text 141
9.2.2.7 Regulatory compliance
9.2.2.7.1 Multinational auditing company enhanced regulatory compliance and operational efficiency with knowledge graphs of Ontotext
9.2.2.8 Customer 360° view
9.2.2.8.1 Intuit enhanced security and data protection using Neo4j knowledge graph for customer data
9.2.2.9 Know Your Customer (KYC) processes
9.2.2.9.1 AI-powered knowledge graphs streamlined KYC compliance and adverse media analysis in financial services
9.2.2.10 Market analysis and trend detection
9.2.2.10.1 Leading investment bank enhanced investment insights through comprehensive company knowledge graph
9.2.2.11 Policy impact analysis
9.2.2.11.1 Delinian enhanced content production and analysis with semantic publishing platform
9.2.2.12 Customer support
9.2.2.12.1 Banks and insurance companies improved AI-powered knowledge graphs to revolutionize customer support in BFSI
9.2.2.13 Self-service data & digital asset discovery and data integration & governance
9.2.2.13.1 HSBC revolutionized data governance with knowledge graphs in BFSI
9.3 RETAIL & ECOMMERCE
9.3.1 NEED TO OPTIMIZE INVENTORY MANAGEMENT FACILITATED BY KNOWLEDGE GRAPHS TO DRIVE MARKET
9.3.2 CASE STUDY
9.3.2.1 Fraud detection in eCommerce
9.3.2.1.1 PayPal enhanced fraud detection with knowledge graphs
9.3.2.2 Dynamic pricing optimization
9.3.2.2.1 Belgian company revolutionized new product development with food pairing knowledge graph
9.3.2.3 Personalized recommendations
9.3.2.3.1 Xandr created industry-leading identity graph for personalized advertising with TigerGraph
9.5.2.6.1 Cisco utilized Neo4j to enhance and assign metadata to its vast document collection
9.5.2.7 Data integration & governance
9.5.2.7.1 Dun & Bradstreet enhanced compliance with Neo4j's graph technology
9.5.2.8 Self-service data & digital asset discovery
9.5.2.8.1 Telecom provider optimized telecom operations with Neo4j's self-service data and digital asset discovery
9.5.2.9 Service incident management
9.5.2.9.1 BT Group revolutionizing telecom inventory management with Neo4j knowledge graph
9.6 GOVERNMENT
9.6.1 SPEEDY DATA INTEGRATION AND INTEROPERABILITY TO BOOST MARKET GROWTH
9.6.2 CASE STUDY
9.6.2.1 Government service optimization
9.6.2.1.1 LODAC Museum project, initiated by Japan's National Institute of Informatics (NII), enhanced academic access to cultural heritage data through Linked Open Data
9.6.2.2 Legislative & regulatory analysis
9.6.2.2.1 Inter-American Development Bank (IDB) enhanced knowledge discovery with knowledge graphs at the IDB 159
