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세계의 AI 데이터 관리 시장 규모, 점유율, 동향 분석 리포트 : 도입 모드별, 제공 서비스별, 기술별, 용도별, 데이터 종류별, 업종별, 지역별 전망과 예측(2023-2030년)
Global AI Data Management Market Size, Share & Trends Analysis Report By Deployment Mode, By Offering (Platform, Software Tools, and Services), By Technology, By Application, By Data Type, By Vertical, By Regional Outlook and Forecast, 2023 - 2030
상품코드 : 1432386
리서치사 : KBV Research
발행일 : 2024년 02월
페이지 정보 : 영문 469 Pages
 라이선스 & 가격 (부가세 별도)
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한글목차

AI 데이터 관리 시장 규모는 2030년까지 1,001억 달러에 달할 것으로 예측되며, 예측 기간 중 CAGR은 22.3%의 시장 성장률로 상승할 전망입니다.

KBV Cardinal matrix의 분석에 따르면 Microsoft Corporation과 Google LLC가 이 시장의 선두주자이며, 2023년 10월, Microsoft Corporation은 미국의 다국적 피자 레스토랑 체인점인 Domino's Pizza, Inc.와 손을 잡았습니다. 양사는 이번 협력을 통해 Azure OpenAI와 Microsoft Cloud Services를 강화하여 개인화되고 간소화된 주문 프로세스를 통해 고객 경험을 향상시킬 수 있도록 했습니다. 또한 이번 협업은 매장 관리자들이 재고 관리 및 직원 스케줄링과 같은 여러 업무에 소요되는 시간을 절약할 수 있도록 지원했으며, Oracle Corporation, SAP SE, Teradata Corporation과 같은 기업은 이 시장의 이 시장의 주요 혁신가입니다.

시장 성장 요인

클라우드 컴퓨팅 플랫폼은 확장 가능하고 유연한 인프라를 제공하므로 기업은 데이터 관리 요구에 따라 규모를 확장하거나 축소할 수 있습니다. 이러한 확장성은 특히 대규모 데이터 세트와 복잡한 계산을 수반하는 AI 용도의 동적 요구사항에 적합합니다. 클라우드 기반 솔루션은 기존 온프레미스 인프라에 대한 비용 효율적인 대안을 제공합니다. 기업은 클라우드 서비스를 종량제 방식으로 이용할 수 있으므로 선투자를 피하고 운영 비용을 절감할 수 있습니다. 또한 클라우드 프로바이더들은 머신러닝 API, 사전 학습된 모델 등 다양한 AI 서비스를 제공합니다. 기업은 이러한 서비스를 AI 데이터 관리 워크플로우에 통합하여 기능을 강화하고 자체 모델 개발의 필요성을 줄일 수 있습니다. 이처럼 클라우드 컴퓨팅의 채택이 증가하고 있는 것이 시장 성장의 기반이 되고 있습니다.

개인화는 마케팅 전략에서 매우 중요한 요소로, AI 데이터 관리를 통해 기업은 고객 데이터, 선호도, 행동을 분석하여 타깃팅된 개인화된 마케팅 캠페인을 진행할 수 있습니다. 개인화된 이메일, 광고, 컨텐츠 추천 등이 포함되며, E-Commerce 및 소매업에서 개인화는 온라인 쇼핑 경험을 향상시키는 핵심 요소입니다. 이를 통해 기업은 고객의 구매 이력, 검색 패턴 및 선호도를 분석하여 개인화된 상품 추천 및 프로모션을 제공할 수 있습니다. 이는 다양한 플랫폼의 컨텐츠 개인화에도 활용되고 있습니다. 기업이 개별 소비자와 사용자의 기대에 부응하기 위해 노력함에 따라 시장은 계속 성장하고 있습니다.

시장 성장 억제요인

AI 데이터 관리를 도입하려면 강력한 컴퓨팅 리소스, 스토리지 시스템, 네트워크 기능 등 인프라에 많은 투자를 해야 하는 경우가 많습니다. 조직은 AI 용도을 지원하기 위해 인프라를 설정하고 업그레이드하는 데 비용이 많이 든다고 느낄 수 있으며, AI 기술, 소프트웨어 라이선스, 자체 알고리즘을 확보하는 데 드는 비용이 상당할 수 있습니다. 조직은 고급 AI 툴에 대한 라이선스 비용을 지불해야 할 수 있으며, 이는 전체 도입 비용을 증가시킬 수 있습니다. 이러한 솔루션을 조직의 필요에 맞게 커스터마이징하거나 기존 시스템과 통합하는 것도 비용 상승의 원인이 될 수 있습니다. 커스터마이징 및 통합 작업에는 전문 지식과 리소스가 필요한 경우가 많습니다. 결과적으로 이러한 측면은 시장 성장을 저해하는 요인으로 작용할 수 있습니다.

목차

제1장 시장 범위와 조사 방법

제2장 시장 요약

제3장 시장 개요

제4장 경쟁 분석 - 세계

제5장 세계 시장 : 도입 모드별

제6장 세계 시장 : 제공별

제7장 세계 시장 : 기술별

제8장 세계 시장 : 용도별

제9장 세계 시장 : 데이터형별

제10장 세계 시장 : 업계별

제11장 세계 시장 : 지역별

제12장 기업 개요

제13장 AI 데이터 관리 시장의 성공 필수 조건

KSA
영문 목차

영문목차

The Global AI Data Management Market size is expected to reach $100.1 billion by 2030, rising at a market growth of 22.3% CAGR during the forecast period.

AI algorithms analyze sensor data from machinery to predict equipment failures before they occur. Therefore, the manufacturing segment captured $1,613.6 million revenue in the market in 2022. This enables proactive maintenance, reducing downtime and preventing costly breakdowns. AI-powered computer vision systems can inspect and analyze products for defects on the production line. This ensures high-quality manufacturing and minimizes the chances of faulty products reaching the market. AI algorithms analyze historical sales data, market trends, and other relevant factors to forecast demand more accurately. This allows manufacturers to adjust production levels, minimize overstock, and effectively meet customer demand.

The major strategies followed by the market participants are Partnerships, Collaborations & Agreements as the key developmental strategy to keep pace with the changing demands of end users. For instance, In August, 2023, Salesforce, Inc. signed a collaboration with IBM Corporation. Through this collaboration, both companies would assist clients in transforming customer, partner, and employee experiences while ensuring the security of their data. Additionally, In March, 2023, Amazon Web Services, Inc., a subsidiary of Amazon.com, Inc. company, joined hands with NVIDIA Corporation. The new product offers 20 exaFLOPS of compute performance that aids training and building deep learning models.

Based on the Analysis presented in the KBV Cardinal matrix; Microsoft Corporation and Google LLC (Alphabet Inc.) are the forerunners in the market. In October, 2023, Microsoft Corporation joined hands with Domino's Pizza, Inc., an American multinational pizza restaurant chain. Under this collaboration, the companies enhanced the Azure OpenAI and Microsoft Cloud Services to improve customer experiences through a personalized and simplified ordering process. Additionally, the collaboration assisted the store managers in saving time on several tasks, like inventory management and staff scheduling. Companies such as Oracle Corporation, SAP SE, Teradata Corporation are some of the key innovators in the market.

Market Growth Factors

Cloud computing platforms offer scalable and flexible infrastructure, allowing associations to scale up or down based on their data management needs. This scalability aligns well with the dynamic requirements of AI applications, especially those involving large datasets and complex computations. Cloud-based solutions provide a cost-effective alternative to traditional on-premises infrastructure. Organizations can leverage cloud services pay-as-you-go, avoiding upfront capital investments and reducing operational costs. Moreover, cloud providers offer various AI services, such as machine learning APIs and pre-trained models. Organizations can integrate these services into their AI data management workflows, enhancing functionality and reducing the need for in-house model development. Thus, the rising adoption of cloud computing provides a foundation for the growth of the market.

Personalization is crucial in marketing strategies. AI data management allows businesses to analyze customer data, preferences, and behavior to create targeted and personalized marketing campaigns. This includes personalized emails, advertisements, and content recommendations. In the e-commerce and retail sectors, personalization is key to enhancing the online shopping experience. This enables businesses to analyze customer purchase history, browsing patterns, and preferences to offer personalized product recommendations and promotions. This is used to personalize content across various platforms. As companies strive to satisfy the expectations of individual consumers and users, the market continues to rise.

Market Restraining Factors

Implementing AI data management often requires significant investments in infrastructure, including powerful computing resources, storage systems, and networking capabilities. Organizations may find setting up or upgrading their infrastructure to support AI applications is expensive. The costs of acquiring AI technologies, software licenses, and proprietary algorithms can be substantial. Organizations may need to pay licensing fees for advanced AI tools, which can contribute to the overall high cost of implementation. Tailoring these solutions to meet organizational needs and integrating them with existing systems can contribute to higher costs. Customization and integration efforts often require specialized expertise and resources. As a result, the above aspects will cause the market growth to decline.

By Deployment Mode Analysis

Based on deployment mode, the market is divided into cloud and on-premise. The on-premise segment garnered a significant revenue share in the market in 2022. On-premise deployment provides organizations with direct control over their data and infrastructure. This level of control is particularly crucial for businesses dealing with sensitive or regulated data that must be kept on-premises for compliance reasons. On-premise solutions allow organizations to customize and tailor these systems according to their specific needs. This is particularly helpful for businesses with unique data processing requirements or specialized workflows. On-premise deployment often results in lower latency as data does not need to travel over the internet to external servers.

By Offering Analysis

On the basis of offering, the market is segmented into platform, software tools, and services. In 2022, the platform segment dominated the market with the maximum revenue share. Platform-type solutions provide a centralized and integrated environment for managing diverse aspects of AI data management. This includes data integration, quality management, analytics, and other functionalities. Platforms are designed to scale horizontally to handle growing data volumes and evolving business requirements. They often support flexible deployment options, including on-premises, cloud, or hybrid architectures. AI-driven platforms automate routine data management tasks such as cleansing, validation, and integration. Automation reduces manual effort, minimizes errors, and enhances overall operational efficiency.

By Technology Analysis

On the basis of technology, the market is classified into machine learning, natural language processing, computer vision, and context awareness. In 2022, the context awareness segment witnessed a considerable revenue share in the market. Context-aware AI systems can analyze user behavior, preferences, and historical interactions with data to understand the context in which they are accessing or manipulating information. These systems equipped with context awareness can dynamically adjust data processing methods based on the specific context of the data. For example, data cleansing algorithms can be adapted based on the quality and source of incoming data. Context awareness allows for the dynamic adjustment of data access policies based on the current context. For instance, they tighten security measures in sensitive contexts or relax restrictions in less critical situations.

By Application Analysis

Based on application, the market is categorised into data augmentation & exploratory data analysis, data anonymization & customization, imputation predictive modeling, data validation & noise reduction, process automation, and others. The data augmentation and exploratory data analysis segment acquired a substantial revenue share in the market in 2022. These tools facilitate data enrichment by adding new data points or features to existing datasets. This augmentation process can significantly improve the performance of machine learning models trained on the enriched data, enhancing their predictive capabilities and robustness. Moreover, these tools excel in integrating data from various sources and formats, simplifying the analysis of diverse datasets in EDA applications. This integration capability is essential for deriving comprehensive insights and making informed decisions based on a holistic view of the data.

By Data Type Analysis

By data type, the market is fragmented into audio data, speech & voice data, image data, text data, and video data. The speech & voice data segment recorded a remarkable revenue share in the market in 2022. This is extensively used in speech distinction systems to convert spoken language into text. This technology is applied in virtual assistants, transcription services, voice-activated devices, and more. AI-driven voice search technology uses natural language processing to understand and respond to spoken queries. Organizations can optimize content for voice search to improve visibility in search engine results. This is employed in speech analytics solutions to analyze and derive insights from customer service calls. This includes sentiment analysis, identification of trends, and monitoring agent performance.

By Vertical Analysis

By vertical, the market is segmented into BFSI, retail & eCommerce, government & public sector, healthcare & life sciences, manufacturing, energy & utilities, media & entertainment, IT & telecom, and others. The retail & e-commerce segment procured a remarkable revenue share in the market in 2022. AI analyzes customer data to deliver personalized product recommendations, including purchase history, browsing behavior, and preferences. This enhances the shopping experience, increases customer satisfaction, and encourages repeat business. AI analyzes transaction data and user behavior to detect and prevent fraud, such as unauthorized transactions or account takeovers. This improves security and builds trust among consumers. AI enables visual search capabilities, allowing customers to search for products using images. Image recognition technology can also tag products and automate catalog management.

By Regional Analysis

Region-wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. In 2022, the North America region registered the highest revenue share in the market. The market in North America is a global powerhouse characterized by the innovation and technological ability of the US and Canada. The United States, with Silicon Valley as a prominent hub, has been a breeding ground for AI startups and tech giants pioneering cutting-edge solutions in data management. The market in North America thrives on a culture of innovation, strong R&D initiatives, and a business landscape that readily embraces AI-driven data management to enhance operations, decision-making, and overall efficiency across diverse industries.

Recent Strategies Deployed in the Market

List of Key Companies Profiled

Global AI Data Management Market Report Segmentation

By Deployment Mode

By Offering

By Technology

By Application

By Data Type

By Vertical

By Geography

Table of Contents

Chapter 1. Market Scope & Methodology

Chapter 2. Market at a Glance

Chapter 3. Market Overview

Chapter 4. Competition Analysis - Global

Chapter 5. Global AI Data Management Market by Deployment Mode

Chapter 6. Global AI Data Management Market by Offering

Chapter 7. Global AI Data Management Market by Technology

Chapter 8. Global AI Data Management Market by Application

Chapter 9. Global AI Data Management Market by Data Type

Chapter 10. Global AI Data Management Market by Vertical

Chapter 11. Global AI Data Management Market by Region

Chapter 12. Company Profiles

Chapter 13. Winning Imperatives of AI Data Management Market

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