Abstract
Summary
The global AI data management market is valued at USD 25.1 billion in 2023 and is estimated to reach USD 70.2 billion by 2028, registering a CAGR of 22.8% during the forecast period. The advent and widespread adoption of cloud computing have redefined the way organizations store, process, and access data. Cloud technologies provide a scalable and flexible foundation, enabling businesses to efficiently manage their ever-growing volumes of data without the constraints of traditional on-premises infrastructure. The shift towards cloud-based platforms is instrumental in driving the demand for AI data management solutions. Cloud environments offer the agility required to adapt to dynamic business needs, allowing organizations to scale their data management capabilities seamlessly. AI-driven data management tools designed for the cloud are inherently more scalable, enabling businesses to handle diverse datasets and increasing workloads effectively. This scalability is particularly crucial as the sheer volume and variety of data continue to expand exponentially.
“By offering, the platform segment is projected to hold the largest market size during the forecast period.”
Platforms in AI data management serve as comprehensive ecosystems that facilitate the collection, storage, processing, analysis, and utilization of data using artificial intelligence technologies. These platforms integrate various tools, algorithms, and functionalities that enable organizations to manage their data efficiently and derive actionable insights. They often feature components for data ingestion, cleansing, and transformation, along with AI-driven analytics and visualization tools that help in extracting valuable patterns and trends from complex datasets.
“By data type, Text Data segment is registered to grow at the highest CAGR during the forecast period.”
AI data management for text data involves the structured organization, analysis, and utilization of unstructured textual information using artificial intelligence techniques. Text data presents unique challenges due to its unstructured nature, varying formats, and nuances in language. AI-based natural language processing (NLP) and text analytics are pivotal in this domain, allowing systems to comprehend, categorize, extract insights, and derive meaning from vast volumes of text. Techniques like sentiment analysis, named entity recognition, topic modeling, and language translation enable the extraction of valuable information, sentiment trends, and contextual understanding from text data. AI-driven text data management finds applications in customer feedback analysis, content categorization, document summarization, chatbots, and information retrieval systems, revolutionizing how organizations harness the wealth of unstructured textual information to make informed decisions and enhance user experiences.
“Asia Pacific is projected to witness the highest CAGR during the forecast period.”
Asia Pacific region has witnessed remarkable growth and evolution in AI data management. With a surge in technological advancements, increased digitalization, and a burgeoning tech ecosystem, countries within the APAC region have been actively embracing AI-driven data management solutions. Countries like China, India, Japan, South Korea, and Singapore have emerged as key players in advancing AI technologies for data management. China, for instance, has heavily invested in AI research and development, fostering innovation in data-driven technologies. India, known for its IT expertise, has been rapidly adopting AI in various sectors, especially in data-intensive industries like finance, healthcare, and e-commerce. Japan and South Korea have been focusing on leveraging AI for precision manufacturing and robotics, while Singapore has been actively promoting itself as a hub for AI development and deployment in the region. The diverse economies and industries across the APAC region have driven a growing demand for AI data management solutions.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI data management market.
By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
By Designation: C-Level Executives: 35%, Directors: 25%, and Others: 40%
By Region: North America: 45%, Europe: 20%, Asia Pacific: 30%, RoW: 5%
Major vendors offering AI data management platform, software/tools and services across the globe are Microsoft (US), AWS (US), IBM (US), Google (US), Oracle (US), Salesforce (US), SAP (Germany), SAS Institute (US), HPE (US), Snowflake (US), Teradata (US), Informatica (US), Databricks (US), TIBCO Software (US), Qlik (US), Collibra (US), Dataiku (US), Alteryx (US), Datamatics Business Solutions (US), Accenture (Ireland), Ataccama (Canada), Reltio (US), Tamr (US), ThoughtSpot (US), AtScale (US), Alation (US), Clarifai (US), DDN Storage (US), Dataloop AI (US) Astera Software (US).
Research Coverage
The market study covers AI data management across segments. It aims at estimating the market size and the growth potential across different segments, such as offering by type, offering by deployment mode, data type, application, technology, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall market for AI data management and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
• Analysis of key drivers (AI-powered data fabric solutions and automated integration fueling market expansion, the evolution in cloud technology to drive the AI Data Management market growth, Rapid advancements in AI and ML to propel the adoption of transformative data management solutions), restraints (Issues related to data availability and quality and susceptibility to bias and inaccuracies), opportunities (Automated data cleaning to revolutionize data preparation for better insights, enhanced predictive analytics to empower businesses to anticipate future trends, personalized and adaptive systems to emerge as a significant opportunity in the market), and challenges (Training AI on large and diverse datasets to enhance data quality poses market challenges, acquiring skilled AI experts in data management to challenge the market, limitations in bridging the educational gap for a skilled workforce) influencing the growth of the AI data management market
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI data management market.
• Market Development: Comprehensive information about lucrative markets – the report analyses the AI data management market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in AI data management market strategies; the report also helps stakeholders understand the pulse of the AI data management market and provides them with information on key market drivers, restraints, challenges, and opportunities.
• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as Microsoft (US), IBM (US), AWS (US), Google (US), Oracle (US) among others in the AI data management market.
Table of Contents
1 INTRODUCTION 32
1.1 STUDY OBJECTIVES 32
1.2 MARKET DEFINITION 32
1.3 MARKET SCOPE 33
1.3.1 MARKET SEGMENTATION 33
1.3.2 INCLUSIONS AND EXCLUSIONS 34
1.3.3 REGIONS COVERED 34
1.4 YEARS CONSIDERED 35
1.5 CURRENCY CONSIDERED 35
1.6 STAKEHOLDERS 35
2 RESEARCH METHODOLOGY 36
2.1 RESEARCH DATA 36
2.1.1 SECONDARY DATA 37
2.1.2 PRIMARY DATA 37
- 2.1.2.1 Breakup of primary profiles 38
- 2.1.2.2 Key insights from industry experts 38
2.2 DATA TRIANGULATION AND MARKET BREAKUP 39
2.3 MARKET SIZE ESTIMATION 40
2.3.1 TOP-DOWN APPROACH 40
2.3.2 BOTTOM-UP APPROACH 41
2.4 MARKET FORECAST 44
2.5 ASSUMPTIONS 45
2.6 LIMITATIONS 47
2.7 IMPACT OF RECESSION 47
3 EXECUTIVE SUMMARY 49
4 PREMIUM INSIGHTS 56
4.1 ATTRACTIVE OPPORTUNITIES FOR KEY PLAYERS IN AI DATA MANAGEMENT MARKET 56
4.2 AI DATA MANAGEMENT MARKET, BY APPLICATION 56
4.3 AI DATA MANAGEMENT MARKET, BY OFFERING & KEY VERTICAL 57
4.4 AI DATA MANAGEMENT MARKET, BY REGION 57
5 MARKET OVERVIEW AND INDUSTRY TRENDS 58
5.1 INTRODUCTION 58
5.2 MARKET DYNAMICS 58
5.2.1 DRIVERS 59
- 5.2.1.1 Adoption of AI-powered data fabric solutions and automated integration 59
- 5.2.1.2 Emergence and widespread adoption of cloud computing and evolution in cloud technology 59
- 5.2.1.3 Rapid advancements in AI and ML and adoption of transformative data management solutions 60
5.2.2 RESTRAINTS 60
- 5.2.2.1 Scarcity of relevant, comprehensive, and high-quality data 60
- 5.2.2.2 Susceptibility to biases and inaccuracies 61
5.2.3 OPPORTUNITIES 61
- 5.2.3.1 Automated data cleaning to revolutionize data preparation for better insights 61
- 5.2.3.2 Enhanced predictive analytics to empower businesses to anticipate future trends 62
- 5.2.3.3 Rise of personalized and adaptive systems 62
5.2.4 CHALLENGES 63
- 5.2.4.1 Acquiring and curating large datasets leading to need for substantial resources 63
- 5.2.4.2 Lack of professional knowledge and scarcity of skilled AI experts 63
- 5.2.4.3 Limited access to specialized education and training programs tailored to AI data management 64
5.3 AI DATA MANAGEMENT MARKET: ARCHITECTURE 64
5.4 AI DATA MANAGEMENT MARKET: EVOLUTION 65
5.5 SUPPLY CHAIN ANALYSIS 66
5.6 ECOSYSTEM/MARKET MAP 67
5.6.1 PLATFORM PROVIDERS 69
5.6.2 SOFTWARE PROVIDERS 69
5.6.3 SERVICE PROVIDERS 69
5.6.4 REGULATORY BODIES 69
5.7 CASE STUDY ANALYSIS 70
5.7.1 CASE STUDY 1: TAMR’S HEALTHCARE PROVIDERS DATA PRODUCT UNIFIED VAST DATA SOURCES AND CREATED RELIABLE RECORDS FOR P360 70
5.7.2 CASE STUDY 2: ALATION EMPOWERED AUSTRALIAN ENERGY COMPANY'S DIGITAL TRANSFORMATION THROUGH DATA GOVERNANCE AND CURATED INSIGHTS 71
5.7.3 CASE STUDY 3: VIACOM18 DEPLOYED AZURE DATABRICKS TO ACCELERATE VIEWER PERSONALIZATION, BOOST PRODUCTIVITY, AND ENHANCE INSIGHTS 71
5.7.4 CASE STUDY 4: CNP ASSURANCES DEPLOYED INFORMATICA MDM TO FACILITATE REAL-TIME AND BATCH-PROCESSING UPDATES TO MANAGE EVOLVING RISK PROFILES EFFECTIVELY 72
5.7.5 CASE STUDY 5: COGNIWARE ADOPTED ARGOS PLATFORM INTEGRATED WITH IBM WATSONX.DATA TO STREAMLINE DATA COLLECTION PROCESS AND EMPOWER USERS TO VISUALIZE CONNECTIONS 73
5.8 TARIFF AND REGULATORY LANDSCAPE 74
5.8.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 75
5.8.2 NORTH AMERICA 80
- 5.8.2.1 US 80
- 5.8.2.2 Canada 80
5.8.3 EUROPE 80
5.8.4 ASIA PACIFIC 80
- 5.8.4.1 South Korea 80
- 5.8.4.2 China 80
- 5.8.4.3 India 81
5.8.5 MIDDLE EAST & AFRICA 81
- 5.8.5.1 UAE 81
- 5.8.5.2 KSA 81
- 5.8.5.3 Bahrain 81
5.8.6 LATIN AMERICA 81
- 5.8.6.1 Brazil 81
- 5.8.6.2 Mexico 81
5.9 PATENT ANALYSIS 82
5.9.1 METHODOLOGY 82
5.9.2 PATENTS FILED, BY DOCUMENT TYPE 82
5.9.3 INNOVATION AND PATENT APPLICATIONS 82
- 5.9.3.1 Top 10 applicants in AI data management market 83
5.10 TRADE ANALYSIS FOR AI DATA MANAGEMENT SOFTWARE 86
5.10.1 IMPORT SCENARIO OF COMPUTER SOFTWARE 86
5.10.2 EXPORT SCENARIO OF COMPUTER SOFTWARE 87
5.11 TECHNOLOGY ANALYSIS 88
5.11.1 AI DATA MANAGEMENT MARKET: TECHNOLOGY ANALYSIS 89
- 5.11.1.1 Key Technologies 89
- 5.11.1.1.1 ML 89
- 5.11.1.1.2 NLP 90
- 5.11.1.1.3 Computer Vision 90
- 5.11.1.1.4 Edge AI 91
- 5.11.1.2 Complementary Technologies 91
- 5.11.1.2.1 Big Data 91
- 5.11.1.2.2 Cloud Computing 92
- 5.11.1.2.3 5G 92
- 5.11.1.2.4 Predictive Analytics 93
- 5.11.1.3 Adjacent Technologies 94
- 5.11.1.3.1 IoT 94
- 5.11.1.3.2 Edge Computing 94
- 5.11.1.3.3 Blockchain 95
- 5.11.1.3.4 Digital Twins 95
- 5.11.1.1 Key Technologies 89
5.12 PRICING ANALYSIS 96
5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, TOP 3 SOFTWARE TOOLS 96
5.12.2 INDICATIVE PRICING ANALYSIS OF AI DATA MANAGEMENT, BY OFFERING 97
5.13 PORTER’S FIVE FORCES ANALYSIS 98
5.13.1 THREAT OF NEW ENTRANTS 99
5.13.2 THREAT OF SUBSTITUTES 99
5.13.3 BARGAINING POWER OF SUPPLIERS 99
5.13.4 BARGAINING POWER OF BUYERS 99
5.13.5 INTENSITY OF COMPETITIVE RIVALRY 100
5.14 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS 100
5.15 KEY CONFERENCES & EVENTS, 2023-2025 101
5.16 KEY STAKEHOLDERS & BUYING CRITERIA 102
5.16.1 KEY STAKEHOLDERS IN BUYING PROCESS 102
5.16.2 BUYING CRITERIA 103
5.17 TECHNOLOGY ROADMAP OF AI DATA MANAGEMENT MARKET 103
5.18 BEST PRACTICES IN AI DATA MANAGEMENT MARKET 105
5.19 BUSINESS MODELS OF AI DATA MANAGEMENT MARKET 106
5.19.1 DATA AS A SERVICE (DAAS) 106
5.19.2 AI-POWERED ANALYTICS AND INSIGHTS 107
5.19.3 DATA GOVERNANCE AND COMPLIANCE SOLUTIONS 107
5.19.4 PERSONALIZATION AND RECOMMENDATION ENGINES 107
5.19.5 AI INFRASTRUCTURE AND TOOLING 107
6 AI DATA MANAGEMENT MARKET, BY OFFERING 108
6.1 INTRODUCTION 109
6.1.1 OFFERING: AI DATA MANAGEMENT MARKET DRIVERS 109
6.2 OFFERING, BY TYPE 110
6.2.1 PLATFORM 110
- 6.2.1.1 AI data management platforms to provide end-to-end solutions that streamline data handling across AI lifecycle 110
- 6.2.1.2 Data collection & ingestion 113
- 6.2.1.3 Data preparation & processing 113
- 6.2.1.4 Data warehousing 113
- 6.2.1.5 Data analytics 113
- 6.2.1.6 Data governance 114
- 6.2.1.7 Data quality management 114
- 6.2.1.8 Data lifecycle management 115
- 6.2.1.9 Other platforms 115
6.2.2 SOFTWARE TOOLS 116
- 6.2.2.1 AI data management software tools to streamline data handling and analysis within AI ecosystems 116
- 6.2.2.2 Data integration & ETL 118
- 6.2.2.3 Data visualization & reporting 118
- 6.2.2.4 Data modeling 119
- 6.2.2.5 Data security & privacy 119
- 6.2.2.6 Data labeling & annotation 119
- 6.2.2.7 Data cleaning & normalization 120
- 6.2.2.8 Data versioning 120
6.2.3 SERVICES 120
- 6.2.3.1 Need for expertise in data governance, data quality, and AI-driven analytics to drive demand for AI data management services 120
- 6.2.3.2 Consulting 122
- 6.2.3.3 System integration & implementation 122
- 6.2.3.4 Support & maintenance 122
- 6.2.3.5 Data migration 123
- 6.2.3.6 AI change management & adoption 123
- 6.2.3.7 AI platform administration 123
6.3 OFFERING, BY DEPLOYMENT MODE 124
6.3.1 CLOUD 125
- 6.3.1.1 Scalability and accessibility of cloud-based AI data management solutions to fuel its demand 125
6.3.2 ON-PREMISES 126
- 6.3.2.1 Stringent data governance and security requirements to propel demand for on-premises AI data management solutions 126
7 AI DATA MANAGEMENT MARKET, BY DATA TYPE 127
7.1 INTRODUCTION 128
7.1.1 DATA TYPE: AI DATA MANAGEMENT MARKET DRIVERS 128
7.2 AUDIO DATA 130
7.2.1 RISING ADOPTION OF VOICE-ENABLED DEVICES AND PERSONALIZED AUDIO EXPERIENCES TO PROPEL MARKET 130
7.3 SPEECH & VOICE DATA 131
7.3.1 RISE OF VOICE-CONTROLLED DEVICES AND DEMAND FOR SEAMLESS SPEECH RECOGNITION TECHNOLOGIES TO DRIVE MARKET 131
7.4 IMAGE DATA 132
7.4.1 NEED FOR IMAGE RECOGNITION TECHNOLOGIES ACROSS INDUSTRIES TO BOOST DEMAND FOR AI DATA MANAGEMENT FOR IMAGE DATA 132
7.5 TEXT DATA 133
7.5.1 REQUIREMENT FOR SENTIMENT ANALYSIS AND LANGUAGE PROCESSING TECHNOLOGIES TO DRIVE MARKET 133
7.6 VIDEO DATA 134
7.6.1 NEED FOR VIDEO ANALYTICS IN VARIOUS VERTICALS TO FUEL GROWTH OF AI DATA MANAGEMENT FOR VIDEO DATA 134
8 AI DATA MANAGEMENT MARKET, BY TECHNOLOGY 135
8.1 INTRODUCTION 136
8.1.1 TECHNOLOGY: AI DATA MANAGEMENT MARKET DRIVERS 136
8.2 MACHINE LEARNING 137
8.2.1 NEED FOR ACCURATE AND PREDICTIVE MACHINE LEARNING MODELS TO FUEL DEMAND FOR ROBUST AI DATA MANAGEMENT 137
8.2.2 DEEP LEARNING 138
- 8.2.2.1 CNNs 138
- 8.2.2.2 RNNs 139
- 8.2.2.3 Generative AI 139
8.3 NATURAL LANGUAGE PROCESSING 139
8.3.1 INCREASING DEMAND FOR ADVANCED LANGUAGE UNDERSTANDING AND COMMUNICATION CAPABILITIES TO DRIVE MARKET 139
8.4 COMPUTER VISION 140
8.4.1 GROWING NEED FOR ACCURATE IMAGE AND VIDEO ANALYSIS ACROSS INDUSTRIES TO PROPEL DEMAND FOR COMPUTER VISION TECHNOLOGY 140
8.5 CONTEXT AWARENESS 141
8.5.1 CONTEXT AWARENESS IN AI DATA MANAGEMENT TO DELIVER TAILORED EXPERIENCES, OPTIMIZE DECISION-MAKING, AND PERSONALIZE INTERACTIONS 141
9 AI DATA MANAGEMENT MARKET, BY APPLICATION 143
9.1 INTRODUCTION 144
9.1.1 APPLICATION: AI DATA MANAGEMENT MARKET DRIVERS 144
9.2 PROCESS AUTOMATION 146
9.2.1 INCREASED OPERATIONAL EFFICIENCY AND REDUCED MANUAL INTERVENTION TO FUEL DEMAND FOR PROCESS AUTOMATION 146
9.3 DATA VALIDATION & NOISE REDUCTION 147
9.3.1 NEED TO ENSURE DATA ACCURACY AND RELIABILITY AND REDUCE UNWANTED DISTORTIONS TO PROPEL MARKET 147
9.4 DATA AUGMENTATION 148
9.4.1 NEED TO ENHANCE MODEL GENERALIZATION AND PERFORMANCE BY ARTIFICIALLY EXPANDING DATASETS TO DRIVE MARKET 148
9.5 EXPLORATORY DATA ANALYSIS 149
9.5.1 NECESSITY OF UNCOVERING VALUABLE INSIGHTS, UNDERSTANDING DATA DISTRIBUTIONS, AND PREPARING DATA FOR SUBSEQUENT MODELING OR ANALYSIS TO DRIVE MARKET 149
9.6 IMPUTATION & PREDICTIVE MODELING 150
9.6.1 NEED TO HANDLE MISSING DATA EFFECTIVELY AND GENERATE ACCURATE PREDICTIONS TO PROPEL MARKET 150
9.7 DATA ANONYMIZATION & COMPRESSION 151
9.7.1 NEED TO ENSURE PRIVACY, REDUCE STORAGE REQUIREMENTS, AND OPTIMIZE DATA PROCESSING TO DRIVE MARKET 151
9.8 OTHER APPLICATIONS 152
10 AI DATA MANAGEMENT MARKET, BY VERTICAL 154
10.1 INTRODUCTION 155
10.1.1 VERTICAL: AI DATA MANAGEMENT MARKET DRIVERS 155
10.2 BANKING, FINANCIAL SERVICES, AND INSURANCE 157
10.2.1 FRAUD DETECTION 158
10.2.2 CUSTOMER CHURN PREDICTION 159
10.2.3 CREDIT RISK ASSESSMENT 159
10.2.4 ANTI-MONEY LAUNDERING (AML) COMPLIANCE 160
10.2.5 OTHER BFSI APPLICATION TYPES 160
10.3 RETAIL & ECOMMERCE 161
10.3.1 PERSONALIZED PRODUCT RECOMMENDATION 162
10.3.2 INVENTORY MANAGEMENT 162
10.3.3 DYNAMIC PRICING 162
10.3.4 CUSTOMER SENTIMENT ANALYSIS 163
10.3.5 OTHER RETAIL & ECOMMERCE APPLICATION TYPES 163
10.4 TELECOMMUNICATIONS 164
10.4.1 NETWORK PERFORMANCE OPTIMIZATION 165
10.4.2 NETWORK SECURITY 165
10.4.3 QUALITY OF SERVICE MONITORING 165
10.4.4 CUSTOMER SUPPORT CHATBOTS 166
10.4.5 OTHER TELECOMMUNICATIONS APPLICATION TYPES 166
10.5 HEALTHCARE & LIFE SCIENCES 167
10.5.1 DISEASE DIAGNOSIS 168
10.5.2 DRUG DISCOVERY 168
10.5.3 PATIENT MONITORING 168
10.5.4 CLINICAL TRAILS OPTIMIZATION 169
10.5.5 OTHER HEALTHCARE & LIFE SCIENCES APPLICATION TYPES 169
10.6 MANUFACTURING 170
10.6.1 QUALITY CONTROL 171
10.6.2 ROBOTIC PROCESS AUTOMATION 171
10.6.3 DEMAND FORECASTING 171
10.6.4 PRODUCTION OPTIMIZATION 172
10.6.5 OTHER MANUFACTURING APPLICATION TYPES 172
10.7 IT & ITES 172
10.7.1 IT SERVICE MANAGEMENT 173
10.7.2 PREDICTIVE MAINTENANCE 174
10.7.3 CAPACITY PLANNING 174
10.7.4 DATA BACKUP RECOVERY 174
10.7.5 OTHER IT & ITES APPLICATION TYPES 174
10.8 GOVERNMENT & DEFENSE 175
10.8.1 NATURAL DISASTER RESPONSE 176
10.8.2 PREDICTIVE POLICING 176
10.8.3 BORDER CONTROL & IMMIGRATION 177
10.8.4 PUBLIC HEALTH MONITORING 177
10.8.5 OTHER GOVERNMENT & DEFENSE APPLICATION TYPES 177
10.9 MEDIA & ENTERTAINMENT 178
10.9.1 CONTENT RECOMMENDATION 179
10.9.2 CONTENT CREATION 179
10.9.3 AUDIENCE ENGAGEMENT ANALYSIS 179
10.9.4 REAL-TIME STREAMING OPTIMIZATION 180
10.9.5 OTHER MEDIA & ENTERTAINMENT APPLICATION TYPES 180
10.10 ENERGY & UTILITIES 180
10.10.1 GRID MANAGEMENT 181
10.10.2 ENERGY CONSUMPTION ANALYSIS 182
10.10.3 RENEWABLE ENERGY FORECASTING 182
10.10.4 ENVIRONMENTAL IMPACT ASSESSMENT 183
10.10.5 OTHER ENERGY & UTILITIES APPLICATION TYPES 183
10.11 OTHER VERTICALS 184
11 AI DATA MANAGEMENT MARKET, BY REGION 185
11.1 INTRODUCTION 186
11.2 NORTH AMERICA 188
11.2.1 NORTH AMERICA: AI DATA MANAGEMENT MARKET DRIVERS 188
11.2.2 NORTH AMERICA: RECESSION IMPACT ANALYSIS 189
11.2.3 US 197
- 11.2.3.1 Robust innovation ecosystem, collaborative efforts, and government initiatives to drive market 197
11.2.4 CANADA 197
- 11.2.4.1 Demand for tech infrastructure, skilled workforce, well-established ecosystems, and National AI Strategy to propel market 197
11.3 EUROPE 198
11.3.1 EUROPE: AI DATA MANAGEMENT MARKET DRIVERS 198
11.3.2 EUROPE: RECESSION IMPACT ANALYSIS 199
11.3.3 UK 206
- 11.3.3.1 Need for diverse and talented workforce, thriving ecosystem, and strategic initiatives taken by government to drive market 206
11.3.4 GERMANY 207
- 11.3.4.1 Rising focus on AI research and implementation and skilled labor force to propel market 207
11.3.5 FRANCE 207
- 11.3.5.1 Strategic focus on open-source AI initiatives to transform AI data management market 207
11.3.6 SPAIN 208
- 11.3.6.1 Spanish government’s active support and initiatives to accelerate growth of AI to boost demand for AI data management market 208
11.3.7 ITALY 209
- 11.3.7.1 Well-established technology industry and strategic government initiatives to fuel demand for AI data management solutions 209
11.3.8 REST OF EUROPE 209
11.4 ASIA PACIFIC 210
11.4.1 ASIA PACIFIC: AI DATA MANAGEMENT MARKET DRIVERS 211
11.4.2 ASIA PACIFIC: RECESSION IMPACT ANALYSIS 211
11.4.3 CHINA 220
- 11.4.3.1 Substantial investments in AI research and development and rising government initiatives for R&D to drive market 220
11.4.4 JAPAN 221
- 11.4.4.1 AI-driven healthcare industry and strategic government support to fuel demand for AI data management solutions 221
11.4.5 INDIA 221
- 11.4.5.1 Increasing investment in AI-driven R&D and establishment of National Program on AI to propel market 221
11.4.6 SOUTH KOREA 222
- 11.4.6.1 Robust ICT industry, high production of semiconductors, and AI patent dominance to accelerate market 222
11.4.7 AUSTRALIA & NEW ZEALAND 223
- 11.4.7.1 Increasing reliance on software solutions and rising awareness regarding strategic value of AI to propel market 223
11.4.8 REST OF ASIA PACIFIC 223
11.5 MIDDLE EAST & AFRICA 224
11.5.1 MIDDLE EAST & AFRICA: AI DATA MANAGEMENT MARKET DRIVERS 224
11.5.2 MIDDLE EAST & AFRICA: RECESSION IMPACT ANALYSIS 225
11.5.3 UAE 232
- 11.5.3.1 Implementation of National Artificial Intelligence Strategy 2031 and investment in innovative technologies to propel market 232
11.5.4 KSA 233
- 11.5.4.1 Saudi Arabia’s Vision 2030 commitment to AI to fuel demand for AI data management solutions 233
11.5.5 SOUTH AFRICA 233
- 11.5.5.1 Growing ICT sector and adoption of AI technologies to fuel demand for diverse AI-driven solutions 233
11.5.6 TURKEY 234
- 11.5.6.1 Rapid growth of cloud and online services driving demand for high-speed data delivery to propel market 234
11.5.7 REST OF MIDDLE EAST & AFRICA 234
11.6 LATIN AMERICA 235
11.6.1 LATIN AMERICA: AI DATA MANAGEMENT MARKET DRIVERS 236
11.6.2 LATIN AMERICA: RECESSION IMPACT ANALYSIS 236
11.6.3 BRAZIL 244
- 11.6.3.1 Robust software development and implementation of stringent regulations to create favorable environment for AI data management to drive market 244
11.6.4 MEXICO 245
- 11.6.4.1 Burgeoning IT industry and adoption of comprehensive national AI strategy to drive market 245
11.6.5 ARGENTINA 245
- 11.6.5.1 Increasing tech-savvy population, significant broadband penetration rates, and rising development of new technologies to propel market 245
11.6.6 REST OF LATIN AMERICA 246
12 COMPETITIVE LANDSCAPE 247
12.1 OVERVIEW 247
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 247
12.3 REVENUE ANALYSIS 249
12.4 MARKET SHARE ANALYSIS 249
12.5 BRAND/PRODUCT COMPARATIVE ANALYSIS 251
12.6 COMPANY EVALUATION MATRIX 251
12.6.1 STARS 251
12.6.2 EMERGING LEADERS 251
12.6.3 PERVASIVE PLAYERS 252
12.6.4 PARTICIPANTS 252
12.6.5 COMPANY FOOTPRINT OF KEY PLAYERS 253
12.7 START-UP/SME EVALUATION MATRIX, 2022 257
12.7.1 PROGRESSIVE COMPANIES 257
12.7.2 RESPONSIVE COMPANIES 257
12.7.3 DYNAMIC COMPANIES 257
12.7.4 STARTING BLOCKS 257
12.7.5 COMPETITIVE BENCHMARKING 259
12.8 COMPETITIVE SCENARIO AND TRENDS 260
12.8.1 PRODUCT LAUNCHES 260
12.8.2 DEALS 263
12.8.3 OTHERS 266
12.9 VALUATION AND FINANCIAL METRICS OF KEY VENDORS 267
12.10 YTD PRICE TOTAL RETURN AND 5-YEAR STOCK BETA OF KEY VENDORS 267
13 COMPANY PROFILES 268
13.1 INTRODUCTION 268
13.2 KEY PLAYERS 268
13.2.1 MICROSOFT 268
13.2.2 IBM 276
13.2.3 AWS 284
13.2.4 GOOGLE 292
13.2.5 ORACLE 305
13.2.6 SALESFORCE 312
13.2.7 SAP 320
13.2.8 SAS INSTITUTE 327
13.2.9 HPE 334
13.2.10 SNOWFLAKE 339
13.2.11 TERADATA 345
13.2.12 INFORMATICA 351
13.2.13 DATABRICKS 361
13.2.14 TIBCO SOFTWARE 371
13.2.15 QLIK 376
13.2.16 COLLIBRA 384
13.2.17 DATAIKU 385
13.2.18 ALTERYX 386
13.2.19 DATAMATICS BUSINESS SOLUTIONS 387
13.2.20 ACCENTURE 388
13.3 START-UPS/SMES 389
13.3.1 ATACCAMA CORPORATION 389
13.3.2 RELTIO 390
13.3.3 TAMR 391
13.3.4 THOUGHTSPOT 392
13.3.5 ATSCALE 393
13.3.6 ALATION 394
13.3.7 CLARIFAI 395
13.3.8 DDN STORAGE 396
13.3.9 DATALOOP AI 397
13.3.10 ASTERA SOFTWARE 398
14 ADJACENT AND RELATED MARKETS 399
14.1 INTRODUCTION 399
14.2 MASTER DATA MANAGEMENT MARKET 399
14.2.1 MARKET DEFINITION 399
14.2.2 MARKET OVERVIEW 399
- 14.2.2.1 Master data management market, by component 400
- 14.2.2.2 Master data management market, by deployment type 401
- 14.2.2.3 Master data management market, by organization size 402
- 14.2.2.4 Master data management market, by vertical 402
- 14.2.2.5 Master data management market, by region 403
14.3 METADATA MANAGEMENT TOOLS MARKET 404
14.3.1 MARKET DEFINITION 404
14.3.2 MARKET OVERVIEW 404
- 14.3.2.1 Metadata management tools market, by application 406
- 14.3.2.2 Metadata management tools market, by business function 407
- 14.3.2.3 Metadata management tools market, by organization size 408
- 14.3.2.4 Metadata management tools market, by vertical 409
- 14.3.2.5 Metadata management tools market, by component 410
- 14.3.2.6 Metadata management tools market, by metadata type 411
- 14.3.2.7 Metadata management tools market, by deployment mode 412
- 14.3.2.8 Metadata management tools market, by region 412
15 APPENDIX 414
15.1 DISCUSSION GUIDE 414
15.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 420
15.3 CUSTOMIZATION OPTIONS 422
15.4 RELATED REPORTS 422
15.5 AUTHOR DETAILS 423