Data Wrangling Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2021-2031

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 Drivers

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 Challenges

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 Players Profiled in the Data Wrangling Market

• 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:

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
• Europe
• Asia-Pacific
• South America
• Middle East & Africa

Competitive Landscape

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

Available Customization

The analyst offers customization according to your specific needs. The following customization options are available for the report:

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

This product will be delivered within 1-3 business days.