全球數據整合市場 - 2023-2030

市場概況

全球數據整合市場在2022年達到116億美元，預計到2030年將達到263億美元，在2023-2030年的預測期內，年復合成長率為10.8%。隨著發展中國家政府服務的不斷數位化，全球數據整合市場將見證持續成長。由於政府正在努力改善公民獲得公共服務的機會並減少腐敗，來自不同來源的各種數據點的整合變得非常重要。這將導致對客製化數據整合工具和服務的需求增加。

全球數據整合市場正經歷著激烈的競爭。主要參與者正在進行戰略收購，以鞏固其在市場上的地位。例如，2023年5月，總部設在美國的軟體公司Qlik宣布收購另一家專門從事數據整合技術的美國公司Talend Inc.。這次收購預計將大大改善Qlik的數據整合平台產品的品質。

市場動態

業務流程自動化的興起

企業擴大採用業務流程自動化來簡化操作，提高效率，並減少人工操作。企業實施各種自動化系統，如企業資源規劃（ERP）、客戶關係管理（CRM）、供應鏈管理（SCM）和人力資源管理（HRM）系統，以最佳化其運作。

通過連接和整合各種自動化系統、應用程式和工作流程中的數據，數據整合在業務流程自動化中發揮了關鍵作用。整合後的數據能夠實現不同流程之間的無縫資訊流，使企業能夠實現端到端的業務流程自動化，並實現更大的營運效率。

數據整合確保自動化系統之間流動的數據是一致和準確的。它涉及數據驗證、清理、轉換和映射，以統一數據格式、結構和語義。通過整合保持數據的一致性和準確性，企業可以依靠可靠的數據進行決策、報告、合規性和其他關鍵業務流程。

對即時洞察力的需求不斷增加

即時洞察力使企業能夠做出及時和明智的決策。在現代快節奏的商業環境中，企業需要從各種來源獲得最新的資訊，以快速應對市場變化、客戶需求和新出現的機會。數據整合在即時收集、匯總和分析數據方面發揮了關鍵作用，確保組織對其營運有一個全面和準確的看法，以便作出有效的決策。

即時的洞察力使企業能夠不斷監測和最佳化其業務績效。通過即時整合多個來源的數據，企業可以獲得對其營運、客戶互動、供應鍊和其他關鍵領域的整體看法。即時可見性使他們能夠識別瓶頸，發現趨勢，並採取主動行動，以提高效率，降低成本，最佳化資源配置，並提高整體業務績效。

缺少互操作性

數據整合涉及連接和整合來自不同系統、應用程式和平台的數據。然而，不同的系統往往使用不同的數據格式、協議和標準，使得數據的無縫交換和整合變得困難。系統之間缺乏互操作性在數據整合過程中造成了障礙，阻礙了數據在不同環境中的順利流動。

許多企業在混合IT環境中運作，將企業內部系統與雲平台和第三方應用程式相結合。這些不同的環境之間缺乏互操作性，造成了整合的挑戰。數據整合解決方案必須能夠在這些不同的環境中無縫連接和整合數據，以確保數據的一致性和可訪問性。缺乏互操作性阻礙了混合環境下的數據整合。

許多組織變得依賴特定供應商的專有技術、格式或平台。這可能會限制數據整合解決方案與其他系統或平台的互操作性。由於兼容性問題，組織在整合來自不同供應商的數據或在供應商之間過渡時可能面臨挑戰。對供應商鎖定的恐懼成為採用數據整合解決方案的阻力。

COVID-19影響分析

COVID-19的大流行迫使企業加快他們的數位化轉型計劃，以適應遠程工作、線上操作和不斷變化的客戶行為。它導致了對數據整合解決方案的需求增加，以連接和整合來自不同系統的數據，並在分佈式環境中實現無縫操作。

大流行病造成的不確定性突出了即時洞察力對決策的重要性。大流行病引起的深刻變化在大流行病後時期才會加速。後大流行時期可能會見證全球主要行業擴大採用數據整合。

人工智慧影響分析

人工智慧技術，如機器學習和自然語言處理，正在被整合到數據整合解決方案中。由人工智慧驅動的數據整合能夠實現智慧數據映射、數據清洗和數據轉換，提高整合過程的效率和準確性。

人工智慧使數據整合中的重複性任務自動化，減少人工努力，加快整合過程。人工智慧算法可以分析數據結構並建議最佳的整合工作流程，簡化了整合管道的開發和維護。人工智慧還通過識別整合數據中的模式、異常和關聯性來增強人類的能力，使組織能夠獲得有價值的見解。

俄羅斯-烏克蘭戰爭的影響

衝突擾亂了烏克蘭的數據整合市場。許多烏克蘭私營企業在國外建立了自己的公司，這導致了對數據整合工具和服務的需求暫時上升，因為企業要確保連續性。此外，烏克蘭政府也看到了需求的增加，因為它試圖在戰爭期間維持一些關鍵的政府服務。

西方國家在衝突後對俄羅斯實施的製裁導致了俄羅斯數據整合市場的重大混亂。由於西方公司因制裁而停止營運，許多俄羅斯行業被剝奪了數據整合工具和服務。這導致了國內軟體供應商的需求增加。

目錄

第一章：方法和範圍

• 研究方法
• 報告的研究目標和範圍

第二章：定義和概述

第三章：執行摘要

• 按部署方式抽查
• 按組件分類
• 按應用分類
• 按終端用戶分類
• 按地區分類

第4章：動態變化

• 影響因素
  • 驅動因素
    • 對數據驅動決策的需求不斷成長
    • 擴大採用數位化轉型措施
    • 業務流程自動化的增加
    • 對即時洞察力的需求不斷增加
  • 限制因素
    • 對數據安全和隱私的關注度不斷提高
    • 缺少互操作性
  • 機會
  • 影響分析

第五章：行業分析

• 波特的五力分析
• 供應鏈分析
• 價格分析
• 監管分析

第六章：COVID-19分析

• 對COVID-19的分析
  • COVID之前的情況
  • COVID期間的情況
  • COVID之後的情況
• COVID-19期間的定價動態
• 需求-供應譜系
• 大流行期間與市場有關的政府計劃
• 製造商的戰略計劃
• 結語

第七章：按部署方式

• 內部部署
• 隨需應變

第8章：按組件分類

• 服務
• 工具

第九章：按應用分類

• 人力資源(HR)
• 市場和銷售
• 營運

第十章：按終端用戶分類

• 銀行業金融機構
• 政府和國防
• IT和電信
• 醫療保健和生命科學
• 其他行業

第十一章：按地區

• 北美洲
  • 美國
  • 加拿大
  • 墨西哥
• 歐洲
  • 德國
  • 英國
  • 法國
  • 義大利
  • 西班牙
  • 歐洲其他地區
• 南美洲
  • 巴西
  • 阿根廷
  • 南美其他地區
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 澳大利亞
  • 亞太其他地區
• 中東和非洲

第十二章：競爭格局

• 競爭格局
• 市場定位/佔有率分析
• 合併和收購分析

第十三章：公司簡介

• Cisco Systems, Inc.
  • 公司概述
  • 部署方法組合和說明
  • 財務概況
  • 最近的發展情況
• IBM
• Oracle Corporation
• SAP SE
• Microsoft
• Precisely
• QlikTech International AB
• Informatica Inc.
• SAS Institute Inc.
• Actian Corporation

第十四章：附錄

Market Overview

The Global Data Integration Market reached US$ 11.6 billion in 2022 and is expected to reach US$ 26.3 billion by 2030, growing with a CAGR of 10.8% during the forecast period 2023-2030. The global data integration market will witness continued growth with the ongoing digitization of government services in developing countries. As governments are striving to improve access to public services for citizens and reduce corruption, the integration of various data points from different sources attains importance. It will lead to an increase in demand for customized data integration tools and services.

The global data integration market is witnessing intense competition. Major players are undertaking strategic acquisitions to consolidate their position in the market. For instance, in May 2023, Qlik, a U.S.-based software company, announced the acquisition of Talend Inc., another U.S.-based company specializing in data integration technologies. The acquisition is expected to significantly improve the quality of Qlik's data integration platform offerings.

Market Dynamics

Rise in Business Process Automation

Organizations are increasingly adopting business process automation to streamline operations, improve efficiency, and reduce manual effort. Organizations implement various automated systems, such as enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM), and human resources management (HRM) systems, to optimize their operations.

Data integration plays a crucial role in business process automation by connecting and integrating data across various automated systems, applications, and workflows. Integrated data enables seamless information flow between different processes, allowing organizations to automate end-to-end business processes and achieve greater operational efficiency.

Data integration ensures that data flowing between automated systems is consistent and accurate. It involves data validation, cleansing, transformation, and mapping to align data formats, structures, and semantics. By maintaining data consistency and accuracy through integration, organizations can rely on reliable data for decision making, reporting, compliance, and other critical business processes.

Increasing Demand For Real-Time Insights

Real-time insights enable organizations to make timely and informed decisions. In the modern fast-paced business environment, organizations need access to up-to-date information from various sources to respond quickly to market changes, customer needs, and emerging opportunities. Data integration plays a crucial role in collecting, aggregating, and analyzing data in real time, ensuring that organizations have a comprehensive and accurate view of their operations for effective decision making.

Real-time insights empower organizations to monitor and optimize their business performance continuously. By integrating data from multiple sources in real time, organizations can gain a holistic view of their operations, customer interactions, supply chain, and other key areas. The real-time visibility allows them to identify bottlenecks, spot trends, and take proactive actions to improve efficiency, reduce costs, optimize resource allocation, and enhance overall business performance.

Lack of Interoperability

Data integration involves connecting and integrating data from various systems, applications, and platforms. However, different systems often use different data formats, protocols, and standards, making it difficult to seamlessly exchange and integrate data. The lack of interoperability between systems creates obstacles in the data integration process and hampers the smooth flow of data across different environments.

Many organizations operate in hybrid IT environments, combining on-premises systems with cloud platforms and third-party applications. The lack of interoperability between these disparate environments creates integration challenges. Data integration solutions must be able to seamlessly connect and integrate data across these diverse environments to ensure data consistency and accessibility. The absence of interoperability impedes the integration of data in hybrid environments.

Many organizations become dependent on a specific vendor's proprietary technologies, formats, or platforms. It can limit the interoperability of data integration solutions with other systems or platforms. Organizations may face challenges in integrating data from different vendors or transitioning between vendors due to compatibility issues. The fear of vendor lock-in acts as a deterrent to adopting data integration solutions.

COVID-19 Impact Analysis

The COVID-19 pandemic forced organizations to accelerate their digital transformation initiatives to adapt to remote work, online operations, and changing customer behaviors. It led to an increased demand for data integration solutions to connect and integrate data from various systems and enable seamless operations in a distributed environment.

The uncertainty caused by the pandemic highlighted the importance of real-time insights for decision-making. The profound changes caused by the pandemic only accelerated in the post-pandemic period. The post-pandemic period is likely to witness increasing adoption of data integration by major industries globally.

AI Impact Analysis

AI technologies, such as machine learning and natural language processing, are being integrated into data integration solutions. AI-powered data integration enables intelligent data mapping, data cleansing, and data transformation, improving the efficiency and accuracy of the integration process.

AI automates repetitive tasks in data integration, reducing manual effort and speeding up the integration process. AI algorithms can analyze data structures and suggest optimal integration workflows, simplifying the development and maintenance of integration pipelines. AI also augments human capabilities by identifying patterns, anomalies, and correlations in integrated data, enabling organizations to derive valuable insights.

Russia- Ukraine War Impact

The conflict disrupted the data integration market in Ukraine. Many private Ukrainian businesses established themselves abroad which led to a temporary rise in demand for data integration tools and services as businesses sought to ensure continuity. Furthermore, increased demand was also witnessed by the Ukrainian government as it sought to maintain some critical government services during the war.

Sanctions imposed on Russia by Western countries in the wake of the conflict have led to major disruptions in the Russian data integration market. Many Russian industries have been deprived of data integration tools and services as western companies ceased operations due to sanctions. It has led to increased demand from domestic software vendors.

Segment Analysis

The global data integration market is segmented based on deployment method, component, application, end-user and region.

Data Integration Tools are the Most Widely Used Component of the Global Market

Data integration tools provide the necessary software and infrastructure to facilitate the integration of data from various sources. The tools offer features like data extraction, transformation, cleansing, mapping, and loading (ETL), data synchronization, data virtualization, and data replication. The tools provide a user-friendly interface and a range of functionalities to support the integration process.

Data integration tools cater to a wide range of use cases, making them applicable across industries and business functions. The tools support integration requirements for data warehousing, business intelligence, cloud migration, application integration, master data management, data governance, and more. The versatility of data integration tools makes them widely adopted by organizations across various sectors.

Geographical Analysis

Asia-Pacific is Expected to Grow at a Faster Pace During the Forecast Period

The Asia-Pacific data integration market is expected to grow at a faster CAGR of 12.5% during the forecast period. Asia-Pacific is witnessing robust economic growth, with countries like China, India, Vietnam and Malaysia having some of the highest growth rates. The rapid economic growth is expected to create new growth opportunities for the data integration market.

Asia-Pacific is embracing digital transformation initiatives across various industries. Organizations are adopting advanced technologies such as cloud computing, big data analytics, AI, and IoT to drive efficiency, innovation, and customer-centricity. Data integration is playing a critical role in connecting and integrating data from different sources and systems, enabling organizations to leverage the utility of these technologies.

The adoption of cloud computing is witnessing a significant rise in Asia-Pacific. Organizations are leveraging cloud-based data integration solutions to overcome infrastructure limitations, improve scalability, and reduce IT costs. The region's expanding cloud infrastructure and investment in data centers is also contributing to the growth of the Asia-Pacific data integration market.

Competitive Landscape

The major global players include: Cisco Systems, Inc., IBM, Oracle Corporation, SAP SE, Microsoft, Precisely, QlikTech International AB, Informatica Inc., SAS Institute Inc. and Actian Corporation.

Why Purchase the Report?

• To visualize the global data integration market segmentation based on deployment method, component, application, end-user and region, as well as understand key commercial assets and players.
• Identify commercial opportunities by analyzing trends and co-development.
• Excel data sheet with numerous data points of data integration market-level with all segments.
• PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
• Product mapping available as Excel consisting of key products of all the major players.

The global data integration market report would provide approximately 64 tables, 67 figures and 210 Pages.

Target Audience 2023

• Data-Driven Businesses
• Data Management System Companies
• Industry Investors/Investment Bankers
• Research Professionals

Table of Contents

1. Methodology and Scope

• 1.1. Research Methodology
• 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

• 3.1. Snippet by Deployment Method
• 3.2. Snippet by Component
• 3.3. Snippet by Application
• 3.4. Snippet by End-User
• 3.5. Snippet by Region

4. Dynamics

• 4.1. Impacting Factors
  • 4.1.1. Drivers
    • 4.1.1.1. Growing Need for Data-Driven Decision Making
    • 4.1.1.2. Increasing Adoption of Digital Transformation Initiatives
    • 4.1.1.3. Rise in Business Process Automation
    • 4.1.1.4. Increasing Demand for Real-Time Insights
  • 4.1.2. Restraints
    • 4.1.2.1. Growing Concerns about Data Security and Privacy
    • 4.1.2.2. Lack of Interoperability
  • 4.1.3. Opportunity
  • 4.1.4. Impact Analysis

5. Industry Analysis

• 5.1. Porter's Five Force Analysis
• 5.2. Supply Chain Analysis
• 5.3. Pricing Analysis
• 5.4. Regulatory Analysis

6. COVID-19 Analysis

• 6.1. Analysis of COVID-19
  • 6.1.1. Scenario Before COVID
  • 6.1.2. Scenario During COVID
  • 6.1.3. Scenario Post COVID
• 6.2. Pricing Dynamics Amid COVID-19
• 6.3. Demand-Supply Spectrum
• 6.4. Government Initiatives Related to the Market During Pandemic
• 6.5. Manufacturers Strategic Initiatives
• 6.6. Conclusion

7. By Deployment Method

• 7.1. Introduction
  • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Method
  • 7.1.2. Market Attractiveness Index, By Deployment Method
• 7.2. On-premises*
  • 7.2.1. Introduction
  • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 7.3. On-demand

8. By Component

• 8.1. Introduction
  • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
  • 8.1.2. Market Attractiveness Index, By Component
• 8.2. Services*
  • 8.2.1. Introduction
  • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 8.3. Tools

9. By Application

• 9.1. Introduction
  • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 9.1.2. Market Attractiveness Index, By Application
• 9.2. Human Resources (HR)*
  • 9.2.1. Introduction
  • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 9.3. Marketing & Sales
• 9.4. Operations

10. By End-User

• 10.1. Introduction
  • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 10.1.2. Market Attractiveness Index, By End-User
• 10.2. BFSI*
  • 10.2.1. Introduction
  • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 10.3. Government & Defence
• 10.4. IT and Telecommunications
• 10.5. Healthcare & Life Sciences
• 10.6. Others

11. By Region

• 11.1. Introduction
  • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
  • 11.1.2. Market Attractiveness Index, By Region
• 11.2. North America
  • 11.2.1. Introduction
  • 11.2.2. Key Region-Specific Dynamics
  • 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Method
  • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
  • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 11.2.7.1. The U.S.
    • 11.2.7.2. Canada
    • 11.2.7.3. Mexico
• 11.3. Europe
  • 11.3.1. Introduction
  • 11.3.2. Key Region-Specific Dynamics
  • 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Method
  • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
  • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 11.3.7.1. Germany
    • 11.3.7.2. The UK
    • 11.3.7.3. France
    • 11.3.7.4. Italy
    • 11.3.7.5. Spain
    • 11.3.7.6. Rest of Europe
• 11.4. South America
  • 11.4.1. Introduction
  • 11.4.2. Key Region-Specific Dynamics
  • 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Method
  • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
  • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 11.4.7.1. Brazil
    • 11.4.7.2. Argentina
    • 11.4.7.3. Rest of South America
• 11.5. Asia-Pacific
  • 11.5.1. Introduction
  • 11.5.2. Key Region-Specific Dynamics
  • 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Method
  • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
  • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 11.5.7.1. China
    • 11.5.7.2. India
    • 11.5.7.3. Japan
    • 11.5.7.4. Australia
    • 11.5.7.5. Rest of Asia-Pacific
• 11.6. Middle East and Africa
  • 11.6.1. Introduction
  • 11.6.2. Key Region-Specific Dynamics
  • 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Method
  • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
  • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

• 12.1. Competitive Scenario
• 12.2. Market Positioning/Share Analysis
• 12.3. Mergers and Acquisitions Analysis

13. Company Profiles

• 13.1. Cisco Systems, Inc.*
  • 13.1.1. Company Overview
  • 13.1.2. Deployment Method Portfolio and Description
  • 13.1.3. Financial Overview
  • 13.1.4. Recent Developments
• 13.2. IBM
• 13.3. Oracle Corporation
• 13.4. SAP SE
• 13.5. Microsoft
• 13.6. Precisely
• 13.7. QlikTech International AB
• 13.8. Informatica Inc.
• 13.9. SAS Institute Inc.
• 13.10. Actian Corporation

LIST NOT EXHAUSTIVE

14. Appendix

• 14.1. About Us and Services
• 14.2. Contact Us