资料角力市场－全球产业规模、份额、趋势、机会和预测：按组件、部署模型、企业模型、最终用户、地区和竞争格局划分，2021-2031年

全球资料角力市场预计将从 2025 年的 39.2 亿美元成长到 2031 年的 89.8 亿美元，复合年增长率达到 14.81%。

资料角力是指将原始或复杂资料组织、结构化和丰富为标准化格式的技术流程，这对于实现准确的分析和决策至关重要。该市场的成长主要受非结构化资料量呈指数级增长以及高品质资料集对人工智慧 (AI) 和机器学习计划支援的巨大需求驱动。此外，对自助式分析日益增长的需求也使业务用户能够自行准备数据，从而减少对中央 IT 团队的依赖，并帮助企业加快获得洞察的速度。

儘管存在这些成长要素，但由于缺乏精通复杂数据整合和管治的人才，市场仍面临严峻挑战。这种人才短缺常常阻碍自动化资料角力工具的成功应用，因为企业难以使其技术能力与策略目标一致。根据智慧资讯管理协会 (AIIM) 的数据显示，2024 年，33% 的受访者认为，缺乏熟练人才是有效利用人工智慧 (AI) 和自动化技术进行资讯管理营运的主要障碍。

市场驱动因素

巨量资料规模和种类的指数级成长是全球资料角力市场的主要驱动力。随着企业从社群媒体、物联网设备和交易系统等各种来源收集大量讯息，资料处理的复杂性也显着增加。原始资料通常不完整、分散且格式各异，因此，强大的资料整理解决方案对于将其转化为可执行的洞察至关重要。 EdgeDelta 在 2024 年 3 月发表的报导《非结构化资料洞察：解锁关键统计资料》指出，非结构化资料将占当今所有产生资料的 80%，这凸显了用于建立和提炼这些大型复杂资料集以供企业使用的工具的重要性。

同时，人工智慧 (AI) 和机器学习 (ML) 的日益融合正在重塑市场格局，它们能够自动化劳动密集的资料准备任务，并推动对高品质训练资料的需求。先进的资料角力平台正在整合 AI 演算法，以智慧方式检测模式、清理异常值并标准化格式，无需人工干预，从而消除资料角力瓶颈。为 AI倡议准备资料集的紧迫性进一步强化了这一趋势。根据 Komprise 发布的《2024 年非结构化资料管理现状》报告（2024 年 8 月），57% 的组织将「AI 准备」列为非结构化资料管理面临的首要业务挑战。此外，这些解决方案对于消除不同系统之间的障碍至关重要。考虑到 MuleSoft 发布的《2024 年连结性基准报告》（2024 年 1 月）指出，81% 的 IT 领导者认为资料孤岛正在阻碍数位转型，这一点尤其重要。

市场挑战

缺乏精通复杂数据整合的人才，是全球资料角力市场扩张的一大障碍。儘管自动化工具的普及程度日益提高，但资料角力和管治通讯协定的有效执行仍然高度依赖人工专业知识。缺乏技术人才的组织常常面临营运瓶颈，抵销了自动化带来的预期效率提升。这种人才缺口迫使企业推迟采用资料角力解决方案，因为它们缺乏内部能力来准确地建立、检验和管理复杂的资料集，这需要大量的人工干预。

技术资源与策略目标无法有效对接，直接阻碍了市场发展。 ISACA预测，到2024年，53%的数位信任专业人员将把员工技能和培训不足视为实现有效资讯管理和组织内部信任的关键障碍。这项数据凸显了一个重要的市场认知：如果没有足够的合格专业人员来监管数据生命週期，企业将被迫推迟或缩减对数据处理技术的投资，最终阻碍整个行业的成长动能。

市场趋势

将资料角力工具整合到资料湖屋生态系统中，透过整合储存层和准备层，从根本上改变了企业资料架构。越来越多的组织正在摒弃传统的模式，即维护独立的资料湖用于储存非结构化数据，资料仓储用于结构化分析。取而代之的是，他们正在采用开放的湖屋架构，利用 Apache Iceberg 和 Delta Lake 等格式，并允许资料角力流程直接在低成本的物件储存上运行。这种转变消除了传统 ETL 管道中高成本且冗余的资料移动，使资料工程师能够在湖屋的管治边界内将原始资产转换为可用的表。根据 Dremio 于 2025 年 1 月发布的《人工智慧时代资料湖屋现况报告》，目前 55% 的组织在资料湖屋平台上运行其大部分分析，证实了向这种整合环境的广泛转变。

同时，即时串流资料处理能力的普及正推动着资料处理方式从高延迟的批次转向持续的资料精炼。随着决策视窗的日益缩短，企业正将复杂的转换逻辑（例如过滤、连接和聚合）直接整合到串流处理引擎中。这种方法能够在数据到达资料库之前对其进行动态清洗和丰富，确保下游系统和人工智慧代理能够获得最新的上下文信息，从而执行诸如欺诈检测和实时个性化等动态任务。这种对即时的追求是资料架构现代化的策略必然要求。根据 Confluent 于 2025 年 5 月发布的《2025 年资料流报告》，89% 的 IT 领导者认为资料流平台是实现其资料目标的关键，这印证了最大限度地减少资料角力工作流程延迟的迫切需求。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章 全球资料角力市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 按组件（工具、服务）
  • 依部署模式（云端、本机部署）
  • 按公司规模（中小企业/大型企业）
  • 按最终用户（IT与电信、零售、银行、金融服务和保险）
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美资料角力市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 我们
  • 加拿大
  • 墨西哥

第七章：欧洲资料角力市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

8. 亚太地区资料角力市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

9. 中东与非洲资料角力市场展望

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美洲资料角力市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

第十三章 全球资料角力市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• Trifacta Software Inc
• Altair Engineering Inc.
• TIBCO Software Inc
• Teradata Corporation
• Oracle Corporation
• SAS Institute Inc
• Talend SA
• Alteryx Inc
• DataRobot, Inc
• Cloudera, Inc

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Data Wrangling Market is projected to expand from USD 3.92 Billion in 2025 to USD 8.98 Billion by 2031, achieving a CAGR of 14.81%. Data wrangling, the technical process involving the cleaning, structuring, and enrichment of raw, complex data into standardized formats, is essential for enabling accurate analysis and decision-making. The market is primarily propelled by the exponential growth of unstructured data volumes and the critical need for high-quality datasets to support artificial intelligence and machine learning projects. Additionally, the rising demand for self-service analytics allows business users to prepare data independently, thereby reducing dependence on central IT teams and accelerating time-to-insight for enterprises.

Despite these growth drivers, the market faces a substantial challenge due to the shortage of a workforce skilled in complex data integration and governance. This talent gap often hampers the successful implementation of automated data preparation tools, as organizations struggle to align their technical capabilities with strategic goals. According to the Association for Intelligent Information Management, 33% of respondents in 2024 identified the lack of skilled personnel as a major obstacle to effectively leveraging artificial intelligence and automation technologies within their information management practices.

Market Driver

The exponential growth in the volume and variety of big data acts as a primary catalyst for the Global Data Wrangling Market. As organizations gather vast amounts of information from diverse sources such as social media, IoT devices, and transactional systems, the complexity of processing this data increases significantly. Since raw data is often messy, incomplete, and exists in various formats, robust wrangling solutions are required to transform it into actionable intelligence. According to EdgeDelta's March 2024 article 'Unstructured Data Insights: Key Statistics Revealed,' unstructured data now comprises 80% of all generated data, highlighting the critical need for tools capable of structuring and refining these massive, complex datasets for enterprise use.

Simultaneously, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is reshaping the market by automating labor-intensive preparation tasks and driving the demand for high-quality training data. Advanced wrangling platforms are increasingly embedding AI algorithms to intelligently detect patterns, clean anomalies, and standardize formats without manual intervention, thereby resolving data readiness bottlenecks. This trend is reinforced by the urgent requirement to prepare datasets for AI initiatives; according to Komprise's August 2024 '2024 State of Unstructured Data Management' report, 57% of enterprises cite preparing for AI as their top business challenge for unstructured data management. Furthermore, these solutions are essential for dismantling barriers between disparate systems, which is critical given that 81% of IT leaders report data silos hinder digital transformation, as noted in MuleSoft's '2024 Connectivity Benchmark Report' from January 2024.

Market Challenge

The scarcity of a workforce proficient in complex data integration serves as a formidable barrier to the expansion of the Global Data Wrangling Market. Although automated tools are becoming more readily available, the effective execution of data cleaning and governance protocols relies heavily on human expertise. When organizations face a deficit in technical talent, they frequently encounter operational bottlenecks that negate the efficiency gains promised by automation. This talent gap compels enterprises to slow their adoption of data wrangling solutions, as they lack the internal capability to structure, validate, and manage complex datasets accurately without significant manual intervention.

Consequently, this inability to align technical resources with strategic objectives directly impedes market development. According to ISACA, in 2024, 53% of digital trust professionals identified the lack of staff skills and training as the primary obstacle to achieving effective information management and reliability within their organizations. This statistic underscores a critical market reality: without a sufficient pool of qualified experts to oversee data lifecycles, companies are forced to delay or scale back their investment in wrangling technologies, thereby stifling the overall momentum of the industry.

Market Trends

The unification of wrangling tools within Data Lakehouse ecosystems is fundamentally altering enterprise data architectures by consolidating storage and preparation layers. Organizations are increasingly moving away from the traditional model of maintaining separate data lakes for unstructured data and data warehouses for structured analysis. Instead, they are adopting open lakehouse architectures that allow wrangling processes to execute directly on low-cost object storage using formats like Apache Iceberg and Delta Lake. This shift eliminates the expensive and redundant movement of data associated with legacy ETL pipelines, enabling data engineers to transform raw assets into consumption-ready tables within the governance boundary of the lakehouse. According to Dremio's '2025 State of the Data Lakehouse in the AI Era Report' from January 2025, 55% of organizations now run the majority of their analytics on data lakehouse platforms, confirming the widespread transition toward these unified environments.

Simultaneously, the adoption of real-time streaming data wrangling capabilities is replacing high-latency batch processing with continuous data refinement. As the operational window for decision-making narrows, enterprises are embedding complex transformation logic-such as filtering, joining, and aggregating-directly into stream processing engines. This approach allows data to be cleaned and enriched in motion before it ever lands in a database, ensuring that downstream systems and artificial intelligence agents receive up-to-the-second context for dynamic tasks like fraud detection and live personalization. This move toward immediacy is a strategic necessity for modernizing data stacks; according to Confluent's '2025 Data Streaming Report' from May 2025, 89% of IT leaders identify data streaming platforms as critical to achieving their data goals, underscoring the urgent imperative to minimize latency in data preparation workflows.

Key Market Players

• Trifacta Software Inc
• Altair Engineering Inc.
• TIBCO Software Inc
• Teradata Corporation
• Oracle Corporation
• SAS Institute Inc
• Talend SA
• Alteryx Inc
• DataRobot, Inc
• Cloudera, Inc

Report Scope

In this report, the Global Data Wrangling Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Data Wrangling Market, By Component

• Tools
• Service

Data Wrangling Market, By Deployment Model

• On Cloud
• On Premises

Data Wrangling Market, By Enterprise Model

• Small and medium-Sized
• Large

Data Wrangling Market, By End User

• IT and Telecommunication
• Retail
• BFSI

Data Wrangling Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Data Wrangling Market.

Available Customizations:

Global Data Wrangling Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Data Wrangling Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Component (Tools, Service)
  • 5.2.2. By Deployment Model (On Cloud, On Premises)
  • 5.2.3. By Enterprise Model (Small and medium-Sized, Large)
  • 5.2.4. By End User (IT and Telecommunication, Retail, BFSI)
  • 5.2.5. By Region
  • 5.2.6. By Company (2025)
• 5.3. Market Map

6. North America Data Wrangling Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Component
  • 6.2.2. By Deployment Model
  • 6.2.3. By Enterprise Model
  • 6.2.4. By End User
  • 6.2.5. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Data Wrangling Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Component
      • 6.3.1.2.2. By Deployment Model
      • 6.3.1.2.3. By Enterprise Model
      • 6.3.1.2.4. By End User
  • 6.3.2. Canada Data Wrangling Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Component
      • 6.3.2.2.2. By Deployment Model
      • 6.3.2.2.3. By Enterprise Model
      • 6.3.2.2.4. By End User
  • 6.3.3. Mexico Data Wrangling Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Component
      • 6.3.3.2.2. By Deployment Model
      • 6.3.3.2.3. By Enterprise Model
      • 6.3.3.2.4. By End User

7. Europe Data Wrangling Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Component
  • 7.2.2. By Deployment Model
  • 7.2.3. By Enterprise Model
  • 7.2.4. By End User
  • 7.2.5. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Data Wrangling Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Component
      • 7.3.1.2.2. By Deployment Model
      • 7.3.1.2.3. By Enterprise Model
      • 7.3.1.2.4. By End User
  • 7.3.2. France Data Wrangling Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Component
      • 7.3.2.2.2. By Deployment Model
      • 7.3.2.2.3. By Enterprise Model
      • 7.3.2.2.4. By End User
  • 7.3.3. United Kingdom Data Wrangling Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Component
      • 7.3.3.2.2. By Deployment Model
      • 7.3.3.2.3. By Enterprise Model
      • 7.3.3.2.4. By End User
  • 7.3.4. Italy Data Wrangling Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Component
      • 7.3.4.2.2. By Deployment Model
      • 7.3.4.2.3. By Enterprise Model
      • 7.3.4.2.4. By End User
  • 7.3.5. Spain Data Wrangling Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Component
      • 7.3.5.2.2. By Deployment Model
      • 7.3.5.2.3. By Enterprise Model
      • 7.3.5.2.4. By End User

8. Asia Pacific Data Wrangling Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Component
  • 8.2.2. By Deployment Model
  • 8.2.3. By Enterprise Model
  • 8.2.4. By End User
  • 8.2.5. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Data Wrangling Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Component
      • 8.3.1.2.2. By Deployment Model
      • 8.3.1.2.3. By Enterprise Model
      • 8.3.1.2.4. By End User
  • 8.3.2. India Data Wrangling Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Component
      • 8.3.2.2.2. By Deployment Model
      • 8.3.2.2.3. By Enterprise Model
      • 8.3.2.2.4. By End User
  • 8.3.3. Japan Data Wrangling Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Component
      • 8.3.3.2.2. By Deployment Model
      • 8.3.3.2.3. By Enterprise Model
      • 8.3.3.2.4. By End User
  • 8.3.4. South Korea Data Wrangling Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Component
      • 8.3.4.2.2. By Deployment Model
      • 8.3.4.2.3. By Enterprise Model
      • 8.3.4.2.4. By End User
  • 8.3.5. Australia Data Wrangling Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Component
      • 8.3.5.2.2. By Deployment Model
      • 8.3.5.2.3. By Enterprise Model
      • 8.3.5.2.4. By End User

9. Middle East & Africa Data Wrangling Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Component
  • 9.2.2. By Deployment Model
  • 9.2.3. By Enterprise Model
  • 9.2.4. By End User
  • 9.2.5. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Data Wrangling Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Component
      • 9.3.1.2.2. By Deployment Model
      • 9.3.1.2.3. By Enterprise Model
      • 9.3.1.2.4. By End User
  • 9.3.2. UAE Data Wrangling Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Component
      • 9.3.2.2.2. By Deployment Model
      • 9.3.2.2.3. By Enterprise Model
      • 9.3.2.2.4. By End User
  • 9.3.3. South Africa Data Wrangling Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Component
      • 9.3.3.2.2. By Deployment Model
      • 9.3.3.2.3. By Enterprise Model
      • 9.3.3.2.4. By End User

10. South America Data Wrangling Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Component
  • 10.2.2. By Deployment Model
  • 10.2.3. By Enterprise Model
  • 10.2.4. By End User
  • 10.2.5. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Data Wrangling Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Component
      • 10.3.1.2.2. By Deployment Model
      • 10.3.1.2.3. By Enterprise Model
      • 10.3.1.2.4. By End User
  • 10.3.2. Colombia Data Wrangling Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Component
      • 10.3.2.2.2. By Deployment Model
      • 10.3.2.2.3. By Enterprise Model
      • 10.3.2.2.4. By End User
  • 10.3.3. Argentina Data Wrangling Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Deployment Model
      • 10.3.3.2.3. By Enterprise Model
      • 10.3.3.2.4. By End User

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Data Wrangling Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. Trifacta Software Inc
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Altair Engineering Inc.
• 15.3. TIBCO Software Inc
• 15.4. Teradata Corporation
• 15.5. Oracle Corporation
• 15.6. SAS Institute Inc
• 15.7. Talend SA
• 15.8. Alteryx Inc
• 15.9. DataRobot, Inc
• 15.10. Cloudera, Inc

16. Strategic Recommendations

17. About Us & Disclaimer