資料管理與分析市場：2024-2030

本報告是 IoT Analytics 正在進行的調查的一部分，該調查於 2023 年 7 月至 2024 年 2 月對 30 多名數據管理和分析供應商和最終用戶專家進行了調查。該報告概述了資料管理和分析市場的現狀，包括市場預測、採用驅動因素、競爭格局、技術和流程實施概述、顯著趨勢和發展，以及對與鄰近市場關係的富有洞察力的案例研究。

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關於數據管理市場

報告稱，數據管理和分析市場預計將以 16% 的複合年增長率成長，到 2030 年預計將達到 5,133 億美元。

資料庫技術、資料架構、分析和資料治理工具在滿足業務需求方面的重要性日益增加，推動了資料管理市場的近期成長。

預計數據分析將在未來六年內為數據管理市場的成長做出重大貢獻。特別是，由於對預測分析和產生人工智慧等人工智慧和機器學習工具的需求不斷增加，數據科學的成長速度快於整體市場。

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上市公司：

以下是本報告中提到的一些公司。

目錄

第一章執行摘要

第 2 章 簡介

• 資料管理：定義
• 資料管理的組成部分
• 資料庫模型和資料架構的演變
• 瞭解數據
• 產業領導者面臨的數據課題
• 資料管理案例研究：西南航空
• 資料管理案例研究：Netflix

第三章技術概述

• 現代資料堆疊
• 現代資料堆疊案例研究：Uber 的四個資料演進步驟
• 現代資料堆疊的組成部分
  • 來源
  • 攝取
  • 儲存：儲存技術、資料架構
  • 轉換
  • 分析：商業智慧、數據科學
  • 資料治理與安全
  • 資料編排

第 4 章 物聯網與資料管理

• 物聯網數據的特點
• IoT 資料管理與分析：範例
• 物聯網數據分析戰略框架
• 支援典型物聯網用例的 5 個資料管理範例

第 5 章人工智慧與資料管理的交互

• 數據管理與人工智慧的關係
• 人工智慧的產生是全球經濟的催化劑
• 生成人工智慧市場收入快速成長
• 追蹤人工智慧技術在您的企業中的採用情況
• 生成式人工智慧對資料管理的變革性影響
• 詳解AI世代帶來的顛覆性創新

第六章 市場規模與前景

• 全球資料管理和分析支出：依階段、細分市場、地區和國家劃分

第七章 競爭格局

• 現代資料管理供應商與傳統資料管理供應商
• 資料管理：依組件劃分的供應商比較
• 資料管理市佔率
• 資料管理與分析供應商概況

第8章案例研究

• 八個真實案例研究突顯了供應商技術的實際應用

第九章 融資與併購

• 前 15 輪投資輪次列表
• 前 15 名併購清單

第10章趨勢和發展

• 與技術/方法、架構演進、業務策略/經濟考量相關的八個趨勢

第十一章市場規模：定義及研究方法

A 246-page report detailing the market for data management and analytics solutions.

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"The Data Management and Analytics Market Report 2024-2030" is part of IoT Analytics' ongoing coverage of software/analytics topics. The content presented in this report is based on a compilation of primary research, including surveys and interviews with 30+ industry experts from data management and analytics vendors and end users conducted between July 2023 and February 2024. The report encompasses a holistic overview of the current state of the data management and analytics market and its intersection with adjacent markets, such as Generative AI (Gen AI) and IoT, including market projections, factors driving adoption, the competitive landscape, a technology and process implementation overview, notable trends and developments, and insightful case studies.

The primary objective of this document is to provide our readers with a comprehensive understanding of the current data management and analytics landscape, offering in-depth analysis, market sizing, and valuable insights to facilitate informed decision-making and strategic planning.

SAMPLE VIEW

How IoT Analytics defines Data Management

Data management is the systematic approach to handling data, which includes the collection, storage, processing, utilization, and safeguarding of information.

This process is integral to facilitating informed decision-making and supporting an organization's strategic goals. By doing so, data management becomes a cornerstone in driving operational improvements, enhancing customer satisfaction, and achieving a competitive advantage in the marketplace.

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Challenges faced by industry leaders

• 1. The ever-increasing growth of data presents a key data management challenge. To tackle this, companies are deploying data management platforms that enhance data ingestion and analytics capabilities, helping to maintain clarity and control over their sprawling data assets.

• 2. The evolving regulatory landscape complicates compliance and increases risk. A streamlined data management process with embedded compliance checks as part of the data governance framework, ensuring that data are processed in line with current laws, reduces the risk of breaches and non-compliance penalties.

• 3. The cost and complexity associated with data security and privacy are rising. Well-managed data pipelines, equipped with proper security measures such as access controls, can enhance data protection mechanisms, making privacy management more methodical and reducing the financial burden on organizations.

The challenges mentioned above are just the tip of the iceberg. The right data management tools are thus critical in addressing these issues, enabling efficient data access and analysis that supports proactive and informed decision-making in a rapidly evolving market.

About the data management market

According to the "Data Management and Analytics Market Report 2024-2030" by IoT Analytics, the market is predicted to grow at a compound annual growth rate (CAGR) of 16%. By 2030, it's expected to be worth $513.3 billion.

The rising relevance of database technologies, data architectures, analytics, and data governance tools in fulfilling business needs has been instrumental in the recent expansion of the data management market.

Over the next six years, data analytics is predicted to contribute significantly to the growth of the data management market. Notably, data science is seeing an uptick, surpassing the overall market growth due to the increased demand for AI and ML tools, such as predictive analytics and generative AI.

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Questions answered:

• What constitutes data management, and how does its evolution reflect the strategic needs of contemporary business operations and decision-making?
• What is the market size for data management and analytics solutions? What is the projected growth?
• What specific developments and synergies within the market are projected to impact market size and outlook?
• Which companies lead the market in terms of the market share?
• How is the competitive landscape within data management evolving, particularly between hyperscalers and niche vendors?
• How are organizations redefining data management practices to adapt to Gen AI's growing influence across technology and industry sectors?
• How are different end users leveraging the tools and technologies from data management vendors to solve real-world challenges?
• Which pockets of data management are receiving the most funding, and what does the M&A situation look like?
• What are the notable trends shaping the data management and analytics landscape? How do the trends influence the direction of business strategy?

Companies mentioned:

A selection of companies mentioned in the report.

Table of Contents

1. Executive summary

2. Introduction

• 2.1. Data management - definition
• 2.2. Components of data management
• 2.3. Evolution of database models and data architecture
• 2.4. Understanding data
• 2.5. Data challenges faced by industry leaders
• 2.6. Data management case study 1-Southwest's Christmas 2022 debacle
• 2.7. Data management case study 2-Netflix's approach to global web traffic

3. Technology overview

• 3.1. Modern data stack
• 3.2. Modern data stack case study: The four data evolution steps at Uber
• 3.3. Components of the data stack- technological deep dive with examples
  • 3.3.1. Sources
  • 3.3.2. Ingestion
  • 3.3.3. Storage - Storage technologies, Data architecture
  • 3.3.4. Transform
  • 3.3.5. Analytics - Business intelligence, Data science
  • 3.3.6. Data governance & security
  • 3.3.7. Data orchestration

4. IoT and data management

• 4.1. Exploring the characteristics of the IoT data
• 4.2. IoT data management and analytics - Example
• 4.3. Strategic framework for IoT data analytics
• 4.3. Five examples of data management aiding typical IoT use cases

5. Interplay between AI and data management

• 5.1. Relationship between data management and AI
• 5.2. Gen AI as a global economic catalyst
• 5.3. Exponential revenue rise of the Gen AI market
• 5.4. Tracking the adoption of AI technologies in business
• 5.5. Gen AI's transformative impact on data management
• 5.6. In-depth coverage of the Gen AI-led disruption

6. Market size and outlook

• 6.1. Global spending on data management and analytics by - stages, segment, region, and country

7. Competitive Landscape

• 7.1. Modern data mgt. vendors Vs. Legacy data mgt. vendors
• 7.2. Data management-vendor comparison by component
• 7.3. Data management market share 2023
• 7.4. Data management and analytics vendor profiles

8. Case Studies

• 8 real-world case studies focusing on the practical applications of vendor technologies

9. Funding and M&A

• 9.1. List of top 15 investment rounds
• 9.2. List of top 15 mergers and acquisitions

10. Trends and developments

• 8 trends related to technologies and methodologies, architectural evolution, and business strategy and economic consideration

11. Market Sizing Definitions and Methodology