Global Data Wrangling Market Size, share, and Trends Analysis Report – Industry Overview and Forecast to 2032

Data Wrangling Market Size

• The data wrangling market was valued at USD 3.0 billion in 2024 and is expected to reach USD 6.6  billion by 2032
• During the forecast period of 2025 to 2032 the market is likely to grow at a CAGR of 10.7%, primarily driven by the high research optimization and growth in emerging sectors.
• The growth is driven by the increasing adoption of AI-powered automation, which enhances data preparation efficiency and reduces manual efforts.

Data Wrangling Market Analysis

• Data wrangling is increasingly adopted across industries such as finance, healthcare, retail, and telecommunications to streamline data processing, enhance decision-making, and drive operational efficiency
• Advancements in AI, machine learning, and automation are revolutionizing data wrangling, enabling faster, more accurate data preparation for analytics, business intelligence, and predictive modeling
• Organizations are shifting from manual data cleaning to automated data wrangling solutions to handle growing data complexities and improve scalability in cloud and big data environments
• Real-time data wrangling tools provide actionable insights by integrating structured and unstructured data sources, empowering businesses with better forecasting, personalized services, and higher ROI on data-driven strategies
• North America is projected to dominate the data wrangling market during the forecast period owing to the rising adoption of data wrangling services and also the data collected on a daily basis has increased the demand for data wrangling at a large scale

Report Scope and Data Wrangling Market Segmentation

Data Wrangling Market Trends

“Growing Adoption of Cloud-Based Data Wrangling Solutions”

• Cloud-based data wrangling solutions dynamically scale to handle massive datasets, ensuring high-speed processing, efficient resource allocation, and uninterrupted workflows across distributed data environments. Businesses reduce IT infrastructure expenses while enhancing accessibility, as cloud solutions enable real-time collaboration, automated updates, and seamless integration with AI-driven analytics tools for smarter decision-making
• Robust encryption, access controls, and compliance frameworks ensure data integrity and protection, helping organizations meet industry regulations while securely managing structured and unstructured data across cloud ecosystems.

•  Cloud-based data wrangling enables instant data transformation, integrating seamlessly with big data, IoT, and AI-powered analytics to deliver faster insights and improve business intelligence capabilities.

For instance,

• In April 2025, according to the blog published by Forbes Media LLC, Google Cloud Next 2025, set for next week in Las Vegas, will highlight advancements in AI-driven data wrangling, cloud computing, and analytics. Expect innovations like Gemini-powered databases and AI-enhanced data management tools, showcasing Google's strategy to integrate cloud, AI, and data solutions across industries. The event will also focus on empowering developers and expanding AI talent, reinforcing Google's competitive edge in cloud technologies

• Additionally, leveraging machine learning and AI, cloud platforms automate data cleansing, deduplication, and transformation, reducing manual errors, enhancing accuracy, and optimizing data workflows for better decision-making.

Data Wrangling  Market Dynamics

Driver

“Growing Adoption of AI and Automation in Data Processing ”

•  The growing adoption of AI and automation in data processing is significantly driving the data wrangling market by enhancing efficiency and accuracy. Traditional data wrangling methods are often time-consuming and prone to human error, making AI-driven automation a game-changer. By leveraging machine learning algorithms, businesses can automate data cleaning, transformation, and integration, reducing manual efforts while improving data quality.
• AI-powered automation enables real-time data wrangling, allowing businesses to extract insights instantly and make data-driven decisions faster. Industries such as finance, healthcare, and retail increasingly rely on real-time analytics for fraud detection, predictive modeling, and personalized customer experiences. Automated data wrangling tools help in continuously refining datasets, ensuring consistency and reliability while integrating with AI-based analytics platforms.

For instance,

In April 2025, Bloomberg's CTO, Shawn Edwards, revealed that AI could streamline 80% of analysts' workload, significantly boosting productivity. In an interview with Financial News, he highlighted how generative AI can enhance research efficiency, especially when processing unstructured data. The market data giant is developing AI-driven tools to revolutionize junior banking roles, potentially increasing productivity tenfold in certain areas, reshaping financial research and analysis.

Opportunity

“Growing Need for Data Governance and Compliance Solutions”

• The rising need for data governance and compliance is driving demand in the data wrangling market. With regulations like GDPR and CCPA, businesses must ensure data accuracy, security, and traceability.
• Sectors such as finance, healthcare, and government rely on advanced data wrangling tools to standardize data, support audits, and prevent unauthorized access. AI-powered automation improves data lineage tracking and compliance with evolving regulations.
• As companies adopt cloud and hybrid environments, built-in governance, encryption, and access controls in data wrangling tools are essential for managing compliance risks..

For instance,

• On February 2025, COMPLY has unveiled its 2025 Innovation Roadmap, emphasizing AI-driven compliance automation and data governance. Its new Employee360 dashboard provides Chief Compliance Officers with real-time oversight of employee risks and regulatory obligations. With growing regulatory complexity, this highlights the rising demand for data governance and compliance solutions—creating a key opportunity for the data wrangling market to streamline regulatory data management, enhance accuracy, and automate compliance processes for financial services firms
• The growing emphasis on data governance and compliance is positioning data wrangling as a critical capability for organizations. Modern data wrangling tools not only streamline data preparation but also ensure regulatory alignment through built-in validation and security features

Restraint/Challenge

“Shortage of skilled experts in data wrangling and automation  ”

• The rapid growth of data-driven decision-making has increased the demand for skilled professionals in data wrangling. However, there is a significant shortage of experts proficient in handling complex data transformation, AI-driven automation, and regulatory compliance. Many organizations struggle to find qualified talent capable of managing, cleaning, and structuring large and unstructured datasets efficiently.
• Data wrangling requires expertise in multiple domains, including data engineering, AI, and machine learning. The complexity of integrating these fields makes it challenging to find professionals with the right skill set.
• compliance with evolving data privacy regulations such as GDPR and CCPA adds another layer of complexity to data wrangling. Companies require professionals who can ensure data governance while maintaining security standards. The shortage of compliance specialists with data wrangling expertise increases the risk of regulatory violations, resulting in legal and financial repercussions.

For instance,

•  On August 2024, according to the news by PRNewswire, a Multiverse report reveals that data skill gaps cost businesses 26 working days per employee annually due to inefficiencies in data handling. Analyzing 12,000 employees across 18 industries in the U.S. and U.K., the study found that workers spend 36% of their week on data tasks, with 4.34 hours lost to inefficiencies. The findings highlight the urgent need for improved data literacy, automation, and predictive modeling skills in the workforce
• The shortage of skilled experts in data wrangling and automation poses a challenge for organizations aiming to manage complex data efficiently. This gap drives the need for user-friendly, AI-powered tools that reduce manual effort

Data wrangling Market Scope

The market is segmented into five notable segments based on business function, component, deployment model, organization size and industry vertical.

Data wrangling Market Country Analysis

“North America Is A Dominant Region In The Global Data Wrangling Market”

•  North America leads the global data wrangling market due to early adoption of AI, machine learning, and automation tools, enabling businesses to streamline data processing and analytics.
• The region is home to global tech leaders such as IBM, Microsoft, Google, and Amazon, which continuously innovate and expand data management solutions. Venture capital funding and corporate investments in AI-powered data processing startups are also fueling market growth.
• Additionally, collaborations between enterprises and AI research institutions enable the development of more sophisticated data wrangling tools tailored to industry-specific needs.

“Asia-Pacific is Projected to Register the Highest Growth Rate”

• The Asia-Pacific region is undergoing rapid digital transformation, with industries adopting AI-driven analytics and automation. Surging investments in cloud infrastructure and data solutions are boosting demand for efficient data wrangling tools.
• The growth of e-commerce, fintech, and smart cities is generating large volumes of unstructured data, driving the need for advanced wrangling capabilities. Countries like China, India, and Japan are prioritizing real-time data processing to gain competitive insights.
• Stricter data protection laws, including China’s PIPL and India’s DPDP Act, are pushing enterprises to adopt data wrangling tools that ensure compliance, accuracy, and streamlined regulatory reporting.

Data Wrangling Market Share

The market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to market.

The Major Market Leaders Operating in the Market Are:

• Trifacta (U.S.)
• Datawatch Systems Inc. (U.S.)
• Dataiku (France)
• IBM (U.S.)
• SAS Institute Inc. (U.S.)
• Oracle (U.S.)
• Talend (France)
• Alteryx Inc. (U.S.)
• TIBCO Software Inc. (U.S.)
• Paxata Inc. (U.S.)
• Informatica (U.S.)
• Hitachi Vantara Corporation (Japan)
• Teradata (U.S.)
• Datameer (U.S.)
• Cooladata (Israel)
• Ubiquiti Inc. (U.S.)
• Rapid Insight (U.S.)
• Infogix Inc. (U.S.)
• Zaloni (U.S.)
• Impetus Technologies Inc. (U.S.)
• Ideata Analytics (India)
• Onedot AG (Switzerland)
• IRI (U.S.)
• Brillio (U.S.)
• TMMData (U.S.) 

Latest Developments in data wrangling Market

In October 2024, DataPelago has launched a Universal Data Processing Engine to accelerate any engine on any hardware for GenAI and analytics workloads. Backed by $47 million in funding, it tackles growing data complexity and unstructured data challenges. The engine redefines data processing efficiency, overcoming cost and scalability limits. CEO Rajan Goyal highlights its ability to unlock breakthrough intelligence by processing massive, complex datasets across various formats in the accelerated computing era.

In April 2025, Deutsche Telekom has expanded its partnership with Google Cloud, making it the backbone of its 'One Data Ecosystem' to streamline data systems, improve processing speed, and ensure regulatory compliance. The collaboration supports Deutsche Telekom's AI-first transformation, enhancing operations and customer experience through AI-driven solutions like the Gemini assistant in the MyMagenta app. Google Cloud will also power Deutsche Telekom’s new AI platform, driving innovation and flexibility for better user experiences.

In February 2025, the Netherlands' privacy watchdog, AP, announced an investigation into Chinese AI firm DeepSeek over concerns about its data collection practices and privacy policies. The investigation follows Italy's ban of DeepSeek's app, and other EU nations like Ireland and France are seeking information on its data handling. This raises critical concerns for the data wrangling market, as strict data privacy regulations in the EU emphasize the importance of secure and compliant data processing practices, impacting global AI and data analytics firms.

• In February 2025, COMPLY has unveiled its 2025 Innovation Roadmap, emphasizing AI-driven compliance automation and data governance. Its new Employee360 dashboard provides Chief Compliance Officers with real-time oversight of employee risks and regulatory obligations. With growing regulatory complexity, this highlights the rising demand for data governance and compliance solutions—creating a key opportunity for the data wrangling market to streamline regulatory data management, enhance accuracy, and automate compliance processes for financial services firms.
• In June 2024 Cloudera introduced three AI-powered assistants to help customers speed up the development of data, analytics, and AI applications. One assistant, Cloudera Copilot for Cloudera Machine Learning, leverages pre-trained LLMs to assist with challenges such as data preparation and model deployment. 

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