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1618405

全球资料整理市场规模：按业务功能、按组件、按部署模型、按组织规模、按最终用户、按地区、范围和预测

Global Data Wrangling Market Size By Business Function, By Component, By Deployment Model, By Organization Size, By End User, By Geographic Scope And Forecast

出版日期: 2024年11月08日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3个工作天内

价格

简介目录

资料整理市场规模与预测

2024 年资料整理市场规模为 16.3 亿美元，预计到 2031 年将达到 32 亿美元，在 2024-2031 年预测期内复合年增长率为 8.80%。推动市场成长的主要因素包括各种组织（尤其是依赖人工智慧和机器学习等技术的机构）可以获得大量数据。此外，计算技术的技术进步进一步增加了数据量，推动了市场成长。全球数据整理市场报告提供了对市场的整体评估。它对关键细分市场、趋势、市场推动因素、市场限制、竞争格局以及在市场中发挥关键作用的因素进行了全面分析。

数据争论的全球市场推动因素

资料整理市场的市场推动因素可能受到多种因素的影响

资料成长：来自感测器、社群媒体、物联网设备和其他来源的资料量呈指数级增长，这促使来自感测器、社群媒体、物联网设备、和其他来源。技术。资料整理工具透过自动化和简化资料准备步骤来满足这项需求。

资料复杂性：

目前可用的资料有多种格式、结构和品质等级。需要能够管理复杂资料转换、资料整合和资料品质保证的复杂技术来应对这种多样化且经常脏的资料。

自助服务：

随着业务使用者寻求自行分析数据，而不严重依赖 IT 和数据工程团队，分析变得越来越受欢迎。数据管理工具允许非技术人员独立准备和分析数据，从而加快决策过程。

资料治理与合规性：

鑑于围绕资料保护和治理（例如 CCPA 和 GDPR）的要求不断提高，组织需要确保其资料准确、一致且合规。资料管理技术不仅执行资料治理原则，还支援资料完整性和品质保证。

大数据和分析的兴起：

随着企业变得更加数据驱动，对海量数据进行高级分析和洞察的需求不断增长。资料分析过程中一个重要的阶段是资料整理，它可以帮助企业更有效地从资料中提取有洞察力的资讯。

与人工智慧和机器学习整合：

透过为训练模型准备数据，数据整理在人工智慧和机器学习专案中非常重要。随着这些技术在各个领域的采用，对能够轻鬆与人工智慧和机器学习介面的资料整理工具的需求不断增长。

云采用：

由于云端运算的广泛采用，组织正在将越来越多的资料和分析工作负载转移到云端。由于基于云端的数据管理解决方案的可扩展性、灵活性和经济性，该行业正在不断扩张。

关注资料民主化：

公司正在努力让资料更容易访问，以便更多的人可以使用它来做出决策。资料整理工具透过简化公司内部人员的资料存取、准备和分析来帮助实现资料民主化。

限制全球资料争用市场的因素

资料整理市场存在一些抑制因素与课题。其中包括：

复杂性与学习曲线：

有效使用资料整理工具通常需要一定程度的技术熟练度。这些工具对于非技术用户来说可能很难理解和使用，这可能会限制它们的使用，特别是在员工不太精通技术的公司中。

资料安全性问题：

处理敏感资料（通常是私人资料）是资料管理的一部分。资料管理工具的使用可能会因资料安全、侵犯隐私以及遵守 CCPA 和 GDPR 等法律的担忧而受到阻碍，特别是在金融和医疗保健等具有严格安全要求的行业。

整合课题：

将资料管理工具与目前 IT 架构、资料管理系统和分析平台整合可能既困难又耗时。特别是在不同的 IT 环境中，相容性问题、资料格式不一致和互通性问题可能会延迟资料整理解决方案的实施。

安装与维护成本：

对于 IT 预算有限的中小型企业 (SME)，资料整理解决方案的实施和维护成本可能很高。采用障碍包括许可证费、订阅费、硬体要求和持续维护成本。

反对改变：

习惯于手动资料准备程序的员工可能会抵制组织内的变革。资料管理工具有被广泛采用的潜力，但文化障碍、对失业的恐惧以及对新技术的抵制可能意味着资料管理工具在生产力和效率方面提供了许多好处，甚至工具也会阻碍采用。

缺乏标准化：

资料管理市场是分散的，许多供应商提供不同的工具和解决方案。资料整理技术、工具和最佳实践缺乏统一性会让客户感到困惑，阻碍他们比较和评估不同的服务，并阻碍采用过程。

效能和可扩充性问题：

根据资料管理技术，可能难以有效管理复杂的资料转换任务或大量资料。效能瓶颈、可扩展性限制和处理延迟可能会让使用者感到沮丧并阻碍资料管理解决方案的采用，特别是当资料速度和多样性很高时。

法规和合规性限制：

组织可能会受到有关资料收集、处理和使用的行业标准、监管义务和合规义务的限制。在组织资料时保持对 HIPAA、PCI-DSS、SOX 和其他法规的遵守可能非常复杂且耗时，这可能会阻碍资料组织工作。

摘要
北美
- 美国
- 加拿大
- 墨西哥
欧洲
- 德国
- 英国
- 法国
- 义大利
- 西班牙
- 欧洲其他地区
亚太地区
- 中国
- 日本
- 印度
- 其他亚太地区
拉丁美洲
- 巴西
- 阿根廷
- 其他拉丁美洲地区
中东/非洲
- 阿拉伯联合大公国
- 沙乌地阿拉伯
- 南非
- 其他中东和非洲地区

第11章全球资料整理市场的竞争态势

摘要
各公司的市场排名
主要发展策略

第12章公司简介

IBM
Oracle
SAS Institute
Trifacta
Datawatch
Talend
Alteryx
Dataiku
TIBCO Software
Paxata
Mindtech Global Ltd.

第13章主要进展

产品发布/开发
併购
业务扩展
合作伙伴与联盟

第14章附录

相关研究

简介目录

Product Code: 8846

Data Wrangling Market Size And Forecast

Data Wrangling Market size was valued at USD 1.63 Billion in 2024 and is projected to reach USD 3.2 Billion by 2031, growing at a CAGR of 8.80 % during the forecast period 2024-2031. Major factors which drive the market growth include the availability of large volumes of data at various organizations specifically the institutions relying on the technologies such as AI and machine learning. Moreover, technological advancements in computing technologies further drive the volume of the data thereby fueling the growth of the market. The Global Data Wrangling Market report provides a holistic evaluation of the market. The report offers a comprehensive analysis of key segments, trends, drivers, restraints, competitive landscape, and factors that are playing a substantial role in the market.

Global Data Wrangling Market Drivers

The market drivers for the Data Wrangling Market can be influenced by various factors. These may include:

Data Growth: The amount of data coming from sensors, social media, IoT devices, and other sources is growing exponentially, and this means that new tools and methods are needed to clean, process, and get this data ready for analysis. This need is met by data wrangling tools, which automate and streamline the data preparation procedure.

Complexity of Data:

There are many different forms, structures, and quality levels of data available today. Sophisticated technologies capable of managing intricate data transformations, data integration, and data quality assurance are needed to deal with this diverse and frequently dirty data.

Self-service :

analytics is becoming more and more popular as business users seek to analyse data on their own without heavily depending on IT or data engineering teams. Data wrangling tools expedite the decision-making process by enabling non-technical individuals to independently prepare and analyse data.

Data Governance and Compliance:

Organisations must make sure that their data is correct, consistent, and compliant in light of the growing requirements surrounding data protection and governance (such as the CCPA and GDPR). Data wrangling technologies support data integrity and quality assurance as well as the enforcement of data governance principles.

The rise of big data and analytics:

As businesses work to become more data-driven, there is an increasing need for sophisticated analytics and insights obtained from vast amounts of data. An essential phase in the data analytics process is data wrangling, which helps businesses more effectively extract insightful information from their data.

Integration with AI and Machine Learning:

By preparing data for model training, data wrangling is important in AI and machine learning projects. The need for data wrangling tools that can easily interface with AI and ML is growing along with the adoption of these technologies across sectors.

Cloud Adoption:

Organisations are shifting more and more of their data and analytics workloads to the cloud as a result of the broad adoption of cloud computing. The industry is expanding due to the scalability, flexibility, and affordability of cloud-based data wrangling solutions.

Emphasis on Data Democratisation:

Businesses are working to make data access more accessible and enable more people to utilise it to inform decisions. Data wrangling tools help democratise data by simplifying the access, preparation, and analysis of data for people within the company.

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Global Data Wrangling Market Restraints

Several factors can act as restraints or challenges for the Data Wrangling Market. These may include:

Complexity and Learning Curve:

Effective use of data wrangling tools frequently necessitates a certain degree of technical proficiency. These tools may be difficult for non-technical users to understand and use, which might restrict their uptake, particularly in companies where employees are less tech-savvy.

Data Security Issues:

Working with sensitive and frequently private data is a part of data wrangling. The use of data wrangling tools may be impeded by worries about data security, privacy violations, and compliance with laws like the CCPA and GDPR, especially in sectors like finance and healthcare that have strict security requirements.

Integration Challenges:

It can be difficult and time-consuming to integrate data wrangling tools with the current IT architecture, data management systems, and analytics platforms. The implementation of data wrangling solutions may be slowed down by compatibility problems, data format inconsistencies, and interoperability difficulties, particularly in diverse IT settings.

Cost of Implementation and Maintenance:

Small and medium-sized businesses (SMEs) with tight IT budgets may find it expensive to deploy and maintain data wrangling solutions. Adoption hurdles may include licencing fees, subscription fees, hardware requirements, and continuing maintenance expenditures, particularly if the adoption payoff is not immediately evident.

Opposition to Change:

Workers used to manual data preparation procedures may be resistant to change within an organisation. Data wrangling tools can be widely adopted, however adoption can be hampered by cultural barriers, fear of losing one's job, and resistance to new technology, even when these tools have a lot to offer in terms of productivity and efficiency.

Lack of Standardisation:

There are many vendors offering a variety of tools and solutions, resulting in a fragmented market in the data wrangling space. The absence of uniformity in data wrangling techniques, tools, and best practices can be confusing to customers and hinder their ability to compare and assess various services, which will impede the adoption process.

Performance and Scalability Problems:

Some data wrangling technologies could find it difficult to effectively manage complicated data transformation activities or massive amounts of data. Particularly in contexts with high data velocity and variety, performance bottlenecks, scalability constraints, and processing delays can irritate users and prevent the adoption of data wrangling solutions.

Constraints arising from regulations and compliance:

Organisations may have limitations regarding the collection, processing, and utilisation of data due to industry standards, regulatory obligations, and compliance mandates. While organising data, maintaining compliance with laws like HIPAA, PCI-DSS, and SOX can be complicated and time-consuming, which could impede data wrangling efforts.

Global Data Wrangling Market Segmentation Analysis

The Global Data Wrangling Market is Segmented on the basis of Business Function, Component, Deployment Model, Organization Size, End User, And Geography.

Data Wrangling Market, By Business Function

Marketing and Sales
Finance
Human Resources
Operations
Legal

Based on Business Function, The market is classified into Marketing and Sales, Finance, Human Resources, Operations, and Legal. The finance segment dominated the segment. Operations such as identifying target customers, accessing profitability, detecting risk factors, anticipating future occurrences, and improving corporate operations require analysts. Thus in order to boost analytics data wrangling tools have a considerably high demand.

Data Wrangling Market, By Component

Tools
Services
Managed Services
Professional Services

Based on Component, The market is classified into Tools and Services. The services segment is further sub-segmented into managed and professional services. The tools segment held the highest share owing to the availability of several solutions by the players such as IBM, Oracle, etc. Moreover, these tools also help to format the large volumes of data generated. Moreover, these tools also help to merge several data sources into a single source for analysis, deleting unnecessary or irrelevant data, identifying empty cells or gaps in the data and identifying the outliers in the data, clarifying the inconsistencies, or deleting the irrelevant data in order to provide analysis.

Data Wrangling Market, By Deployment Model

Cloud
On-Premises

Based on Deployment Model, The market is classified into Cloud and On-Premises. The cloud segment dominated the market owing to the adoption of the cloud solutions due to the advantages offered by these solutions such as advanced security, low costs, access to data and requirement of less staff.

Data Wrangling Market, By Organization Size

Large Enterprises
Small and Medium-Sized Enterprises

Based on Organization Size, The market is classified into Large Enterprises and Small and Medium-Sized Enterprises. The large enterprises segment held the largest share owing to adoption of data wrangling tools for clean, standardized and profiled data which aids in informed decisions.

Data Wrangling Market, By End User

Automotive and Transportation
Banking, Financial Services, and Insurance (BFSI)
Energy and Utilities
Government and Public Sector
Healthcare and Life Sciences
Manufacturing
Retail and Ecommerce
Telecommunication and IT
Travel and Hospitality
Others

Based on End User, The market is classified into Automotive and Transportation, Banking, Financial Services, and Insurance (BFSI), Energy and Utilities, Government and Public Sector, Healthcare and Life Sciences, Manufacturing, Retail and Ecommerce, Telecommunication and IT, Travel and Hospitality, and Others. The BFSI segment held the largest share. The data wrangling tools have features that are personalized for these institutions and aid them to discover data from formats and sources, fraud detection, improve operational productivity and risk management.

Data Wrangling Market, By Geography

North America
Europe
Asia Pacific
Rest of the world
On the basis of Geography, The Global Data Wrangling Market is classified into North America, Europe, Asia Pacific, and the Rest of the world. North America is expected to witness fastest growth during the forecast period. Factors such as high disposable income, higher digital literacy among the population and favorable digital infrastructure are key factors which are expected to drive the growth of the market during the forecast period.

Key Players

The "Global Data Wrangling Market" study report will provide valuable insight with an emphasis on the global market including some of the major players such as
IBM, Oracle, SAS Institute, Trifacta, Datawatch, Talend, Alteryx, Dataiku, TIBCO Software, Paxata, Mindtech Global Ltd.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis.

Key Developments

In March 2022, Mindtech announced it had secured a n investment of USD 3.25 Million led by Appen. The investments will be used by the company to support the growth of the company.
In January 2022, Alteryx announced it had acquired Data Wrangler Trifacta for USD 400 Million. Trifecta is a provider of data wrangler solutions.
Ace Matrix Analysis
The Ace Matrix provided in the report would help to understand how the major key players involved in this industry are performing as we provide a ranking for these companies based on various factors such as service features & innovations, scalability, innovation of services, industry coverage, industry reach, and growth roadmap. Based on these factors, we rank the companies into four categories as
Active, Cutting Edge, Emerging, and Innovators.
Market Attractiveness
The image of market attractiveness provided would further help to get information about the region that is majorly leading in the Global Data Wrangling Market. We cover the major impacting factors that are responsible for driving the industry growth in the given region.
Porter's Five Forces
The image provided would further help to get information about Porter's five forces framework providing a blueprint for understanding the behavior of competitors and a player's strategic positioning in the respective industry. The porter's five forces model can be used to assess the competitive landscape in Global Data Wrangling Market, gauge the attractiveness of a certain sector, and assess investment possibilities.

1 INTRODUCTION OF THE GLOBAL DATA WRANGLING MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL DATA WRANGLING MARKET MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porter's Five Force Model
4.4 Value Chain Analysis

5 GLOBAL DATA WRANGLING MARKET, BY BUSINESS FUNCTION

5.1 Overview
5.2 Marketing and Sales
5.3 Finance
5.4 Human Resources
5.5 Operations
5.6 Legal

6 GLOBAL DATA WRANGLING MARKET, BY COMPONENT

6.1 Overview
6.2 Tools
6.3 Services
- 6.3.1 Managed Services
- 6.3.2 Professional Services

7 GLOBAL DATA WRANGLING MARKET, BY DEPLOYMENT MODEL

7.1 Overview
7.2 Cloud
7.3 On-Premise

8 GLOBAL DATA WRANGLING MARKET, BY ORGANIZATION SIZE

8.1 Overview
8.2 Large Enterprises
8.3 Small and Medium-Sized Enterprises

9 GLOBAL DATA WRANGLING MARKET, BY END USER

9.1 Overview
9.2 Automotive and Transportation
9.3 Banking, Financial Services, and Insurance (BFSI)
9.4 Energy and Utilities
9.5 Government and Public Sector
9.6 Healthcare and Life Sciences
9.7 Manufacturing
9.8 Retail and Ecommerce
9.9 Telecommunication and IT
9.10 Travel and Hospitality
9.11 Others

10 GLOBAL DATA WRANGLING MARKET, BY GEOGRAPHY

10.1 Overview
10.2 North America
- 10.2.1 The U.S.
- 10.2.2 Canada
- 10.2.3 Mexico
10.3 Europe
- 10.3.1 Germany
- 10.3.2 The U.K.
- 10.3.3 France
- 10.3.4 Italy
- 10.3.5 Spain
- 10.3.6 Rest of Europe
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 Japan
- 10.4.3 India
- 10.4.4 Rest of Asia Pacific
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Argentina
- 10.5.3 Rest of LATAM
10.6 Middle East and Africa
- 10.6.1 UAE
- 10.6.2 Saudi Arabia
- 10.6.3 South Africa
- 10.6.4 Rest of the Middle East and Africa

11 GLOBAL DATA WRANGLING MARKET COMPETITIVE LANDSCAPE

11.1 Overview
11.2 Company Market Ranking
11.3 Key Development Strategies

12 COMPANY PROFILES

12.1 IBM
- 12.1.1 Overview
- 12.1.2 Financial Performance
- 12.1.3 Product Outlook
- 12.1.4 Key Developments
12.2 Oracle
- 12.2.1 Overview
- 12.2.2 Financial Performance
- 12.2.3 Product Outlook
- 12.2.4 Key Developments
12.3 SAS Institute
- 12.3.1 Overview
- 12.3.2 Financial Performance
- 12.3.3 Product Outlook
- 12.3.4 Key Developments
12.4 Trifacta
- 12.4.1 Overview
- 12.4.2 Financial Performance
- 12.4.3 Product Outlook
- 12.4.4 Key Developments
12.5 Datawatch
- 12.5.1 Overview
- 12.5.2 Financial Performance
- 12.5.3 Product Outlook
- 12.5.4 Key Developments
12.6 Talend
- 12.6.1 Overview
- 12.6.2 Financial Performance
- 12.6.3 Product Outlook
- 12.6.4 Key Developments
12.7 Alteryx
- 12.7.1 Overview
- 12.7.2 Financial Performance
- 12.7.3 Product Outlook
- 12.7.4 Key Developments
12.8 Dataiku
- 12.8.1 Overview
- 12.8.2 Financial Performance
- 12.8.3 Product Outlook
- 12.8.4 Key Developments
12.9 TIBCO Software
- 12.9.1 Overview
- 12.9.2 Financial Performance
- 12.9.3 Product Outlook
- 12.9.4 Key Developments
12.10 Paxata
- 12.10.1 Overview
- 12.10.2 Financial Performance
- 12.10.3 Product Outlook
- 12.10.4 Key Developments
12.11 Mindtech Global Ltd.
- 12.11.1 Overview
- 12.11.2 Financial Performance
- 12.11.3 Product Outlook
- 12.11.4 Key Developments