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市场调查报告书
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1850399

资料角力：市场占有率分析、产业趋势、统计资料、成长预测（2025-2030）

Data Wrangling - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 100 Pages | 商品交期: 2-3个工作天内

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简介目录

预计到 2025 年，资料角力市场规模将达到 34.8 亿美元，到 2030 年将达到 59.3 亿美元，年复合成长率为 11.3%。

资料整理-市场-IMG1

在整个预测期内，企业数据的加速成长、对即时分析日益增长的需求以及从传统 ETL 套件向 AI 就绪平台的转型，仍将是关键的成长引擎。供应商正在整合生成式 AI、低程式码转换流程和 Lakehouse 连接器，以加快洞察速度，并支援财务、行销和营运团队的自助服务。随着超大规模云端供应商整合原生资料整理功能，竞争日益激烈，迫使纯粹的资料准备公司透过特定领域的自动化和多模态支援来脱颖而出。要求建立强有力的管治和血缘关係彙报的新法规，进一步推动了这一趋势。

全球资料角力市场趋势与洞察

各行业数据量不断成长

麦肯锡预测，到2030年，全球资料中心投资将达到6.7兆美元，其中5.2兆美元将直接用于人工智慧工作负载。边缘设备、5G部署和生产线数位化正在推动资料生成，其处理能力已远超传统ETL（提取、转换和载入）流程。亚太地区到2024年将有12206兆瓦的资料中心投入运作，另有14338兆瓦正在建设中。因此，企业将转向能够处理多样化、高频次资料流的平台，以因应地方政府制定的各项监管规定。

人工智慧和巨量资料技术的进步推动了自动化。

Alteryx 等供应商正在整合生成式助手，这些助手可以推荐转换步骤并以自然语言产生摘要。 Gartner 发布的《2025 年代理分析分类法》指出，自主管道能够自动修正模式漂移并优化运算资源分配。 Databricks 收购 Lilac AI 后加速了这一趋势，在其 Lakehouse 堆迭中添加了基于 LLM 的资料品质评分功能。虽然人工智慧可以提高生产力，但企业为了应对飙升的运算成本，正在透过混合部署策略来限制其应用。

中小企业对资料组织工具的认知度较低

在中亚和西亚，中小企业占所有企业的98.9%，但由于缺乏数位化技能和预算限制，许多企业仍然依赖电子表格。政策机构正在倡导提供培训补贴和云端服务券以提高数位化应用率，而供应商则透过提供免费增值服务和与本地经销商伙伴关係来打入这一价格敏感型市场。

细分市场分析

至2024年，结构化资料将为资料角力市场贡献20.2亿美元（占58.2%）。关係表对于事务完整性和核心彙报仍然至关重要。然而，现代资料管道必须将日誌、点选流和感测器资料整合到资料仓储和湖仓环境中。随着资料行数的激增，能够自动产生使用者旅程图的以SQL为中心的视觉化建置工具可以帮助企业维护管治。

预计2025年至2030年间，非结构化资料市场规模将成长11.6亿美元，复合年增长率达12.7%。基于LLM的分类和电脑视觉技术释放合约、工程图和视讯帧中的资讯。服务提供者透过提供整合的向量索引、多模态元资料提取以及符合跨境法规的隐私保护型重新编辑模组来脱颖而出。

到2024年，软体工具将占据资料角力市场69.5%的份额，带来24.1亿美元的授权和订阅费用。云端原生套件将资料准备、编目和管治整合到统一的工作空间。供应商将资料准备功能与分析和机器学习工作负载捆绑在一起，使资料角力一种工作流程，而非一项独立任务，从而提高了其普及率。

预计业务收益将以每年 13.0% 的速度成长，这反映了市场对架构设计、迁移和託管营运的需求。德勤和 Databricks 在银行业资料即服务 (Data as a Service) 方面的合作，凸显了专家合作伙伴在现代化倡议中所扮演的重要角色。随着湖仓和分散式架构的日益成熟，许多公司正在将管道监控外包给专家，由他们根据基于结果的合约提供全天候支援。

区域分析

到2024年，北美将占全球收入的37.5%，这反映了云端运算的普及、超大规模资料中心网路的建立以及对人工智慧优先平台的持续创业融资。美国公司是支出的主力军，微软2025年第一季。加拿大正在调整自身以适应技能和法律规范，而墨西哥的製造业丛集则正在采用本地湖畔资料中心配置，以符合资料居住法。成本压力正促使许多公司转向基于工作负载的分层存储，将频繁存取的资料集放置在高速物件存储中，并将冷资料归檔在本地。

亚太地区预计将以11.9%的复合年增长率成长，成为资料角力市场成长最快的地区。亚太地区的企业受惠于12,206兆瓦的资料中心、不断成长的5G用户群以及中国、印度和印尼的自主云端服务。本地供应商正与全球平台合作，提供满足延迟和监管限制的智慧优势。新加坡和香港强大的电子商务和金融科技生态系统对即时客户360度解决方案的需求不断增长，推动了对可扩展就绪引擎的需求。

欧洲是一个成熟但监管严格的市场环境，GDPR 和营运风险需求对采购标准做出了明确规定。德国汽车製造商正在实施数位双胞胎，将工厂遥测资料与企业资源规划 (ERP) 资料整合。英国银行正在实现资料溯源自动化，以满足审慎监理局 (PRA) 的要求。同时，南美洲和中东及非洲地区仍在发展中，但前景广阔。巴西的开放银行计画正在推动 API 流量的成长，而这些流量必须进行标准化；沙乌地阿拉伯的「云端优先」政策则推动了对兼顾文化和法律因素的在地化资料架构的需求。

其他福利：

  • Excel格式的市场预测（ME）表
  • 3个月的分析师支持

目录

第一章 引言

  • 研究假设和市场定义
  • 调查范围

第二章调查方法

第三章执行摘要

第四章 市场情势

  • 市场概览
  • 市场驱动因素
    • 各行业产生的数据量日益增加
    • 人工智慧和巨量资料技术的进步推动了自动化。
    • 企业用户对自助式资料准备的需求日益增长。
    • 更严格的数据品质和管治法规
    • 向资料湖屋架构的转变推动了跨格式资料整理。
    • 无程式码LLM协同驾驶模式的出现加速了变革
  • 市场限制
    • 中小企业对资料角力工具的认知度较低
    • 出于资料安全主导，对敏感资料集实施存取限制
    • 大规模云端资料工程人才短缺。
    • 人工智慧增强型资料处理工作负载的云端运算成本不断上涨
  • 价值链分析
  • 监管环境
  • 技术展望
  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争对手之间的竞争
  • 投资分析
  • 评估宏观经济趋势对市场的影响

第五章 市场规模与成长预测

  • 依资料类型
    • 结构化资料
    • 半结构化数据
    • 非结构化数据
  • 按组件
    • 软体
      • 自助式资料准备平台
      • BI/AI 套件中内建了准备模组
    • 服务
      • 託管服务
      • 专业服务/咨询服务
  • 按业务职能
    • 金融
    • 行销与销售
    • 手术
    • 人力资源
    • 法律与合规
  • 按最终用户行业划分
    • 资讯科技/通讯
    • BFSI
    • 零售与电子商务
    • 卫生保健
    • 政府和公共部门
    • 其他终端用户产业
  • 按地区
    • 北美洲
      • 美国
      • 加拿大
      • 墨西哥
    • 欧洲
      • 德国
      • 英国
      • 法国
      • 义大利
      • 西班牙
      • 其他欧洲地区
    • 亚太地区
      • 中国
      • 日本
      • 印度
      • 韩国
      • 澳洲
      • 亚太其他地区
    • 南美洲
      • 巴西
      • 阿根廷
      • 其他南美洲
    • 中东和非洲
      • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 土耳其
      • 其他中东地区
      • 非洲
      • 南非
      • 埃及
      • 奈及利亚
      • 其他非洲地区

第六章 竞争情势

  • 市场集中度
  • 策略趋势
  • 市占率分析
  • 公司简介
    • Alteryx Inc.
    • TIBCO Software Inc.
    • Altair Engineering Inc.
    • Teradata Corporation
    • Oracle Corporation
    • SAS Institute Inc.
    • Datameer Inc.
    • DataRobot Inc.
    • Cloudera Inc.
    • Cambridge Semantics Inc.
    • Informatica Inc.
    • Microsoft Corporation
    • IBM Corporation
    • QlikTech International AB（Talend）
    • Databricks Inc.
    • KNIME GmbH
    • Dataiku SAS
    • Matillion Ltd.
    • Paxata（DataRobot）
    • Tamr Inc.
    • Astera Software
    • Savant Labs
    • Airbyte Inc.

第七章 市场机会与未来展望

简介目录
Product Code: 64268

The data wrangling market size stood at USD 3.48 billion in 2025 and is on track to expand at an 11.3% CAGR to reach USD 5.93 billion by 2030.

Data Wrangling - Market - IMG1

Over the forecast period, the accelerating growth of enterprise data, mounting demand for real-time analytics, and the pivot from traditional ETL suites to AI-enabled preparation platforms will remain the principal growth engines. Vendors are embedding generative AI, low-code transformation flows, and lakehouse connectors to shorten time-to-insight and support self-service across finance, marketing, and operations teams. Competitive intensity is rising as hyperscale cloud providers integrate native wrangling features, forcing pure-play data preparation firms to differentiate through domain-specific automation and multimodal support. Emerging regulations that mandate strong governance frameworks and lineage reporting further reinforce adoption momentum, even as escalating compute costs push enterprises toward hybrid deployment models.

Global Data Wrangling Market Trends and Insights

Growing Volumes of Data Generated Across Industries

McKinsey estimates that global data-center outlays will reach USD 6.7 trillion by 2030, of which USD 5.2 trillion relates directly to AI workloads. Edge devices, 5G rollouts, and digitization of manufacturing lines are fueling data creation that outpaces legacy ETL capacity. Asia-Pacific exemplifies this trajectory with 12,206 MW of operational data-center power and 14,338 MW under development in 2024. Enterprises therefore pivot to platforms capable of processing diverse, high-frequency feeds in local jurisdictions that impose sovereignty guardrails.

Advancement in AI and Big-Data Technologies Enabling Automation

Vendors such as Alteryx have embedded generative assistants that recommend transformation steps and generate summaries in natural language. Gartner's 2025 taxonomy of agentic analytics points to autonomous pipelines that self-correct for schema drift and optimize compute allocation. Databricks accelerated this trend by acquiring Lilac AI, adding LLM-based data-quality scoring to its lakehouse stack. While AI raises productivity, organizations temper adoption with hybrid deployment strategies that mitigate compute cost spikes.

Limited Awareness of Data-Wrangling Tools Among SMEs

MSMEs account for 98.9% of all businesses in Central and West Asia, yet scarce digital skills and budget constraints leave many reliant on spreadsheets. Policy bodies advocate training subsidies and cloud vouchers to broaden adoption, while vendors pursue freemium tiers and local reseller partnerships to penetrate this price-sensitive segment.

Other drivers and restraints analyzed in the detailed report include:

  1. Rising Demand for Self-Service Data Preparation Among Business Users
  2. Stricter Data-Quality and Governance Regulations
  3. Escalating Cloud-Compute Costs for Gen-AI-Enhanced Wrangling Workloads

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Structured data contributed USD 2.02 billion to the data wrangling market size in 2024, equal to 58.2% revenue. Relational tables remain pivotal for transactional integrity and core reporting. Even so, modern pipelines must fuse logs, clickstreams, and sensor feeds into warehouse and lakehouse environments. SQL-centric visual builders that auto-generate lineage maps help enterprises maintain governance as row counts surge.

The unstructured segment is projected to add USD 1.16 billion in incremental revenue between 2025 and 2030 at a 12.7% CAGR, the highest pace among data types. LLM-powered classification and computer vision capabilities unlock insights within contracts, engineering drawings, and video frames. Providers differentiate by offering integrated vector indexing, multimodal metadata extraction, and privacy-aware redaction modules that comply with cross-border regulations.

Software tools held 69.5% of the data wrangling market in 2024, translating to USD 2.41 billion in license and subscription fees. Cloud-native suites weave preparation, cataloging, and governance into one workspace. Vendors cement stickiness by bundling prep functionality inside analytics or ML workloads, turning data wrangling into a workflow rather than a standalone task.

Services revenue, forecast to grow 13.0% annually, reflects demand for architecture design, migration, and managed operations. Deloitte's collaboration with Databricks on Data as a Service for Banking underscores the lift that expert partners provide during modernization initiatives. As lakehouses and distributed fabrics mature, many firms outsource pipeline monitoring to specialists who deliver 24 X 7 support under outcome-based contracts.

The Data Wrangling Market Report is Segmented by Data Type (Structured Data, Semi-Structured Data, and Unstructured Data), Component (Software and Services), Business Function (Finance, Marketing and Sales, Operations, and More), End-User Industry (IT and Telecommunication, BFSI, Retail and E-Commerce, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America held 37.5% of global revenue in 2024, reflecting deep cloud penetration, established hyperscale data-center networks, and sustained venture funding for AI-first platforms. United States enterprises drive the bulk of spend, illustrated by Microsoft's USD 42.4 billion cloud revenue in Q1 2025 and Fabric's 80% customer surge. Canada aligns with skills and regulatory frameworks, whereas Mexico's manufacturing clusters embrace local lakehouse deployments to comply with data-residency laws. Cost pressures are pushing many firms toward workload-aware tiering that keeps frequently accessed datasets on fast object storage and archives cold data on-premises.

Asia-Pacific is forecast to log an 11.9% CAGR, making it the fastest-growing theater for the data wrangling market. Regional enterprises benefit from the 12,206 MW operational data-center footprint, an expanding 5G user base, and sovereign cloud offerings in China, India, and Indonesia. Local providers collaborate with global platforms to offer in-territory edges that satisfy latency and regulation constraints. Strong e-commerce and fintech ecosystems in Singapore and Hong Kong demand real-time customer 360 solutions, intensifying the call for scalable preparation engines.

Europe holds a mature but regulation-heavy environment where GDPR and operational risk mandates dictate procurement criteria. German automotive manufacturers deploy digital twins that blend plant telemetry with enterprise resource planning data. United Kingdom banks advance lineage automation to satisfy Prudential Regulation Authority expectations. Meanwhile, South America, and Middle East, and Africa remain nascent but promising. Brazil's open banking initiative stimulates API traffic that must be standardized, and Saudi Arabia's cloud-first directives increase demand for localized data fabrics that balance cultural and legal considerations.

  1. Alteryx Inc.
  2. TIBCO Software Inc.
  3. Altair Engineering Inc.
  4. Teradata Corporation
  5. Oracle Corporation
  6. SAS Institute Inc.
  7. Datameer Inc.
  8. DataRobot Inc.
  9. Cloudera Inc.
  10. Cambridge Semantics Inc.
  11. Informatica Inc.
  12. Microsoft Corporation
  13. IBM Corporation
  14. QlikTech International AB (Talend)
  15. Databricks Inc.
  16. KNIME GmbH
  17. Dataiku SAS
  18. Matillion Ltd.
  19. Paxata (DataRobot)
  20. Tamr Inc.
  21. Astera Software
  22. Savant Labs
  23. Airbyte Inc.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Growing volumes of data generated across industries
    • 4.2.2 Advancement in AI and big-data technologies enabling automation
    • 4.2.3 Rising demand for self-service data preparation among business users
    • 4.2.4 Stricter data-quality and governance regulations
    • 4.2.5 Migration to data-lakehouse architectures driving cross-format wrangling
    • 4.2.6 Emergence of no-code LLM co-pilots that accelerate transformations
  • 4.3 Market Restraints
    • 4.3.1 Limited awareness of data-wrangling tools among SMEs
    • 4.3.2 Data-security driven access restrictions on sensitive datasets
    • 4.3.3 Shortage of cloud data-engineering talent for large-scale wrangling
    • 4.3.4 Escalating cloud-compute costs for Gen-AI-enhanced wrangling workloads
  • 4.4 Value Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Bargaining Power of Suppliers
    • 4.7.2 Bargaining Power of Buyers
    • 4.7.3 Threat of New Entrants
    • 4.7.4 Threat of Substitutes
    • 4.7.5 Intensity of Competitive Rivalry
  • 4.8 Investment Analysis
  • 4.9 Assessment of the Impact of Macroeconomic Trends on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Data Type
    • 5.1.1 Structured Data
    • 5.1.2 Semi-structured Data
    • 5.1.3 Unstructured Data
  • 5.2 By Component
    • 5.2.1 Software
      • 5.2.1.1 Self-service data-preparation platforms
      • 5.2.1.2 Embedded prep modules in BI/AI suites
    • 5.2.2 Services
      • 5.2.2.1 Managed Services
      • 5.2.2.2 Professional / Consulting Services
  • 5.3 By Business Function
    • 5.3.1 Finance
    • 5.3.2 Marketing and Sales
    • 5.3.3 Operations
    • 5.3.4 Human Resources
    • 5.3.5 Legal and Compliance
  • 5.4 By End-user Industry
    • 5.4.1 IT and Telecommunication
    • 5.4.2 BFSI
    • 5.4.3 Retail and E-commerce
    • 5.4.4 Healthcare
    • 5.4.5 Government and Public Sector
    • 5.4.6 Other End-user Industries
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 Europe
      • 5.5.2.1 Germany
      • 5.5.2.2 United Kingdom
      • 5.5.2.3 France
      • 5.5.2.4 Italy
      • 5.5.2.5 Spain
      • 5.5.2.6 Rest of Europe
    • 5.5.3 Asia-Pacific
      • 5.5.3.1 China
      • 5.5.3.2 Japan
      • 5.5.3.3 India
      • 5.5.3.4 South Korea
      • 5.5.3.5 Australia
      • 5.5.3.6 Rest of Asia-Pacific
    • 5.5.4 South America
      • 5.5.4.1 Brazil
      • 5.5.4.2 Argentina
      • 5.5.4.3 Rest of South America
    • 5.5.5 Middle East and Africa
      • 5.5.5.1 Middle East
      • 5.5.5.1.1 Saudi Arabia
      • 5.5.5.1.2 United Arab Emirates
      • 5.5.5.1.3 Turkey
      • 5.5.5.1.4 Rest of Middle East
      • 5.5.5.2 Africa
      • 5.5.5.2.1 South Africa
      • 5.5.5.2.2 Egypt
      • 5.5.5.2.3 Nigeria
      • 5.5.5.2.4 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global-level Overview, Market-level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
    • 6.4.1 Alteryx Inc.
    • 6.4.2 TIBCO Software Inc.
    • 6.4.3 Altair Engineering Inc.
    • 6.4.4 Teradata Corporation
    • 6.4.5 Oracle Corporation
    • 6.4.6 SAS Institute Inc.
    • 6.4.7 Datameer Inc.
    • 6.4.8 DataRobot Inc.
    • 6.4.9 Cloudera Inc.
    • 6.4.10 Cambridge Semantics Inc.
    • 6.4.11 Informatica Inc.
    • 6.4.12 Microsoft Corporation
    • 6.4.13 IBM Corporation
    • 6.4.14 QlikTech International AB (Talend)
    • 6.4.15 Databricks Inc.
    • 6.4.16 KNIME GmbH
    • 6.4.17 Dataiku SAS
    • 6.4.18 Matillion Ltd.
    • 6.4.19 Paxata (DataRobot)
    • 6.4.20 Tamr Inc.
    • 6.4.21 Astera Software
    • 6.4.22 Savant Labs
    • 6.4.23 Airbyte Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-Need Assessment