Global Data Wrangling Market Size By Business Function (Marketing And Sales, Finance), By Component (Tools, Services), By Deployment Model (Cloud, On-Premises), By Organization Size (Large Enterprises, Small And Medium-Sized Enterprises), By End User (Automotive And Transportation, Banking), By Geog

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 includeData GrowthThe 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.

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
  1. Managed Services
  2. 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.