データ管理およびアナリティクス市場：2024-2030年

当レポートは、IoT Analyticsによる継続的調査の一部をまとめたものであり、データ管理およびアナリティクスのベンダーやエンドユーザーの30人以上の専門家を対象に、2023年7月から2024年2月にかけて実施した調査およびインタビューに基づいています。当レポートは、市場予測、普及の促進要因、競合情勢、技術およびプロセス導入の概要、注目すべき動向・展開、洞察に満ちたケーススタディなど、データ管理およびアナリティクス市場の現状と、生成AIやIoTなどの隣接市場との関係性を包括的に網羅しています。

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データ管理市場について

当レポートによると、データ管理およびアナリティクスの市場規模はCAGR16％で成長すると予測されており、2030年には5,133億米ドルの規模に達すると予測されています。

ビジネスニーズを満たす上で、データベース技術、データアーキテクチャ、アナリティクス、データガバナンスツールの重要性が高まっており、最近のデータ管理市場の成長に寄与しています。

今後6年間は、データアナリティクスがデータ管理市場の成長に大きく貢献すると予測されています。特に、データサイエンスは、予測分析や生成AIなどのAIやMLツールの需要増加により、市場全体の成長を上回る伸びを示しています。

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掲載企業：

本レポートで言及されている企業の一部をご紹介します。

目次

第1章 エグゼクティブサマリー

第2章 イントロダクション

• データ管理：定義
• データ管理の構成要素
• データベースモデルとデータアーキテクチャの進化
• データの理解
• 業界リーダーが直面するデータの課題
• データ管理のケーススタディ：Southwest
• データ管理のケーススタディ：Netflix

第3章 技術概要

• Modern Data Stack
• Modern Data Stackのケーススタディ：Uberにおける4つのデータ進化ステップ
• Modern Data Stackのコンポーネント
  • ソース
  • インジェスチョン
  • ストレージ：ストレージ技術、データアーキテクチャ
  • トランスフォーム
  • アナリティクス：ビジネスインテリジェンス、データサイエンス
  • データガバナンスとセキュリティ
  • データオーケストレーション

第4章 IoTとデータ管理

• IoTデータの特性
• IoTデータ管理と分析：例
• IoTデータアナリティクスのための戦略的フレームワーク
• 典型的なIoTユースケースを支援するデータ管理の5つの例

第5章 AIとデータ管理の相互作用

• データ管理とAIの関係
• 世界経済の触媒としてのAI世代
• 生成AI市場の急激な収益増加
• ビジネスにおけるAI技術の導入状況を追跡
• 生成AIがデータ管理に与える変革的影響
• AI世代がもたらす破壊的イノベーションを詳細に解説

第6章 市場規模・展望

• データ管理およびアナリティクスへの世界の支出：段階・セグメント・地域・国別

第7章 競合情勢

• モダンデータ管理ベンダー vs 従来型データ管理ベンダー
• データ管理：コンポーネント別ベンダー比較
• データ管理市場のシェア
• データ管理およびアナリティクスベンダーのプロファイル

第8章 ケーススタディ

• ベンダー技術の実用的応用に焦点を当てた8つの実際のケーススタディ

第9章 資金調達とM&A

• 投資ラウンドのトップ15のリスト
• M&Aトップ15のリスト

第10章 動向と発展

• 技術・手法、アーキテクチャの進化、ビジネス戦略・経済的考慮に関連する8つの動向

第11章 市場規模：定義・調査手法

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.

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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