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1618405

全球資料整理市場規模：按業務功能、按組件、按部署模型、按組織規模、按最終用戶、按地區、範圍和預測

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個工作天內

價格

簡介目錄

資料整理市場規模與預測

2024 年資料整理市場規模為 16.3 億美元，預計到 2031 年將達到 32 億美元，在 2024-2031 年預測期內複合年增長率為 8.80%。推動市場成長的主要因素包括各種組織（尤其是依賴人工智慧和機器學習等技術的機構）可以獲得大量數據。此外，計算技術的技術進步進一步增加了數據量，推動了市場成長。全球數據整理市場報告提供了對市場的整體評估。它對關鍵細分市場、趨勢、市場推動因素、市場限制、競爭格局以及在市場中發揮關鍵作用的因素進行了全面分析。

數據爭論的全球市場推動因素

資料整理市場的市場推動因素可能受到多種因素的影響

資料成長：來自感測器、社群媒體、物聯網設備和其他來源的資料量呈指數級增長，這促使來自感測器、社群媒體、物聯網設備、和其他來源。技術。資料整理工具透過自動化和簡化資料準備步驟來滿足這項需求。

資料複雜性：

目前可用的資料有多種格式、結構和品質等級。需要能夠管理複雜資料轉換、資料整合和資料品質保證的複雜技術來應對這種多樣化且經常髒的資料。

自助服務：

隨著業務使用者尋求自行分析數據，而不嚴重依賴 IT 和數據工程團隊，分析變得越來越受歡迎。數據管理工具允許非技術人員獨立準備和分析數據，從而加快決策過程。

資料治理與合規性：

鑑於圍繞資料保護和治理（例如 CCPA 和 GDPR）的要求不斷提高，組織需要確保其資料準確、一致且合規。資料管理技術不僅執行資料治理原則，還支援資料完整性和品質保證。

大數據和分析的興起：

隨著企業變得更加數據驅動，對海量數據進行高級分析和洞察的需求不斷增長。資料分析過程中一個重要的階段是資料整理，它可以幫助企業更有效地從資料中提取有洞察力的資訊。

與人工智慧和機器學習整合：

透過為訓練模型準備數據，數據整理在人工智慧和機器學習專案中非常重要。隨著這些技術在各個領域的採用，對能夠輕鬆與人工智慧和機器學習介面的資料整理工具的需求不斷增長。

雲採用：

由於雲端運算的廣泛採用，組織正在將越來越多的資料和分析工作負載轉移到雲端。由於基於雲端的數據管理解決方案的可擴展性、靈活性和經濟性，該行業正在不斷擴張。

關注資料民主化：

公司正在努力讓資料更容易訪問，以便更多的人可以使用它來做出決策。資料整理工具透過簡化公司內部人員的資料存取、準備和分析來幫助實現資料民主化。

限制全球資料爭用市場的因素

資料整理市場存在一些抑制因素與課題。其中包括：

複雜性與學習曲線：

有效使用資料整理工具通常需要一定程度的技術熟練度。這些工具對於非技術用戶來說可能很難理解和使用，這可能會限制它們的使用，特別是在員工不太精通技術的公司中。

資料安全性問題：

處理敏感資料（通常是私人資料）是資料管理的一部分。資料管理工具的使用可能會因資料安全、侵犯隱私以及遵守 CCPA 和 GDPR 等法律的擔憂而受到阻礙，特別是在金融和醫療保健等具有嚴格安全要求的行業。

整合課題：

將資料管理工具與目前 IT 架構、資料管理系統和分析平台整合可能既困難又耗時。特別是在不同的 IT 環境中，相容性問題、資料格式不一致和互通性問題可能會延遲資料整理解決方案的實施。

安裝與維護成本：

對於 IT 預算有限的中小型企業 (SME)，資料整理解決方案的實施和維護成本可能很高。採用障礙包括許可證費、訂閱費、硬體要求和持續維護成本。

反對改變：

習慣於手動資料準備程序的員工可能會抵制組織內的變革。資料管理工具有被廣泛採用的潛力，但文化障礙、對失業的恐懼以及對新技術的抵制可能意味著資料管理工具在生產力和效率方面提供了許多好處，甚至工具也會阻礙採用。

缺乏標準化：

資料管理市場是分散的，許多供應商提供不同的工具和解決方案。資料整理技術、工具和最佳實踐缺乏統一性會讓客戶感到困惑，阻礙他們比較和評估不同的服務，並阻礙採用過程。

效能和可擴充性問題：

根據資料管理技術，可能難以有效管理複雜的資料轉換任務或大量資料。效能瓶頸、可擴展性限制和處理延遲可能會讓使用者感到沮喪並阻礙資料管理解決方案的採用，特別是當資料速度和多樣性很高時。

法規和合規性限制：

組織可能會受到有關資料收集、處理和使用的行業標準、監管義務和合規義務的限制。在組織資料時保持對 HIPAA、PCI-DSS、SOX 和其他法規的遵守可能非常複雜且耗時，這可能會阻礙資料組織工作。

摘要
北美
- 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
亞太地區
- 中國
- 日本
- 印度
- 其他亞太地區
拉丁美洲
- 巴西
- 阿根廷
- 其他拉丁美洲地區
中東/非洲
- 阿拉伯聯合大公國
- 沙烏地阿拉伯
- 南非
- 其他中東和非洲地區

第11章全球資料整理市場的競爭態勢

摘要
各公司的市場排名
主要發展策略

第12章公司簡介

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

第13章主要進展

產品發佈/開發
併購
業務擴展
合作夥伴與聯盟

第14章附錄

相關研究

簡介目錄

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