资料管理与分析市场：2024-2030

本报告是 IoT Analytics 正在进行的调查的一部分，该调查于 2023 年 7 月至 2024 年 2 月对 30 多名数据管理和分析供应商和最终用户专家进行了调查。该报告概述了资料管理和分析市场的现状，包括市场预测、采用驱动因素、竞争格局、技术和流程实施概述、显着趋势和发展，以及对与邻近市场关係的富有洞察力的案例研究。

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关于数据管理市场

报告称，数据管理和分析市场预计将以 16% 的复合年增长率成长，到 2030 年预计将达到 5,133 亿美元。

资料库技术、资料架构、分析和资料治理工具在满足业务需求方面的重要性日益增加，推动了资料管理市场的近期成长。

预计数据分析将在未来六年内为数据管理市场的成长做出重大贡献。特别是，由于对预测分析和产生人工智慧等人工智慧和机器学习工具的需求不断增加，数据科学的成长速度快于整体市场。

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上市公司：

以下是本报告中提到的一些公司。

目录

第一章执行摘要

第 2 章 简介

• 资料管理：定义
• 资料管理的组成部分
• 资料库模型和资料架构的演变
• 瞭解数据
• 产业领导者面临的数据课题
• 资料管理案例研究：西南航空
• 资料管理案例研究：Netflix

第三章技术概述

• 现代资料堆迭
• 现代资料堆迭案例研究：Uber 的四个资料演进步骤
• 现代资料堆迭的组成部分
  • 来源
  • 摄取
  • 储存：储存技术、资料架构
  • 转换
  • 分析：商业智慧、数据科学
  • 资料治理与安全
  • 资料编排

第 4 章 物联网与资料管理

• 物联网数据的特点
• IoT 资料管理与分析：范例
• 物联网数据分析战略框架
• 支援典型物联网用例的 5 个资料管理范例

第 5 章人工智慧与资料管理的交互

• 数据管理与人工智慧的关係
• 人工智慧的产生是全球经济的催化剂
• 生成人工智慧市场收入快速成长
• 追踪人工智慧技术在您的企业中的采用情况
• 生成式人工智慧对资料管理的变革性影响
• 详解AI世代带来的颠覆性创新

第六章 市场规模与前景

• 全球资料管理和分析支出：依阶段、细分市场、地区和国家划分

第七章 竞争格局

• 现代资料管理供应商与传统资料管理供应商
• 资料管理：依组件划分的供应商比较
• 资料管理市占率
• 资料管理与分析供应商概况

第8章案例研究

• 八个真实案例研究突显了供应商技术的实际应用

第九章 融资与併购

• 前 15 轮投资轮次列表
• 前 15 名併购清单

第10章趋势和发展

• 与技术/方法、架构演进、业务策略/经济考量相关的八个趋势

第十一章市场规模：定义及研究方法

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

SAMPLE VIEW

"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.

SAMPLE VIEW

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