A new market analysis indicates strong growth in the global data wrangling market, projecting an expansion from USD 3,146.7 million in 2023 to USD 7,685.6 million by 2031, exhibiting a CAGR of 11.99% during the forecast period of 2024-2031. The market value is estimated to be USD 3,478.8 million in 2024. This growth is largely propelled by the increasing adoption of big data analytics, artificial intelligence, and machine learning across various industries.
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The comprehensive report analyzes the global data wrangling market, segmenting it by component (tools, services), by deployment model (on-premises, cloud-based), by organization size (small and medium-sized enterprises (SMEs), large enterprises), by industry vertical, and regional landscape. This detailed segmentation provides valuable insights into the market's dynamics and emerging trends.
Key drivers fueling the market's expansion include:
Increasing Adoption of Big Data Analytics: The growing volume and complexity of data are driving the need for effective data wrangling tools and services to prepare data for analysis.
Rising Integration of Artificial Intelligence and Machine Learning: Data wrangling is a critical step in the development and deployment of AI and ML models, fueling market growth.
Growing Need for Data Quality and Governance: Organizations are increasingly focusing on data quality and governance to ensure the reliability and accuracy of their insights, driving the adoption of data wrangling solutions.
Self-Service Data Preparation: The increasing demand for self-service data preparation tools empowers business users to wrangle data without relying solely on IT teams, accelerating the data preparation process.
Cloud Adoption and Scalability: The shift towards cloud-based data warehousing and analytics platforms is driving the demand for scalable and flexible cloud-based data wrangling solutions.
The report also highlights key market trends:
Integration of AI and ML in Data Wrangling Tools: AI-powered features are being integrated into data wrangling tools to automate tasks such as data cleaning, transformation, and enrichment.
Growing Demand for Collaborative Data Wrangling Platforms: Collaborative platforms that enable data analysts, scientists, and business users to work together on data preparation are gaining traction.
Focus on Data Wrangling for Real-Time Analytics: The increasing need for real-time insights is driving the adoption of data wrangling solutions that can process streaming data.
Emphasis on Data Cataloging and Discovery: Data wrangling tools are increasingly being integrated with data catalogs to improve data discovery and understanding.
Rise of Domain-Specific Data Wrangling Solutions: Vendors are developing data wrangling tools tailored to the specific needs and data formats of different industry verticals.
This report offers a strategic overview of the global data wrangling market, providing valuable insights for data scientists, data engineers, business analysts, and organizations seeking to improve their data preparation processes for better analytics and decision-making.
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