The data wrangling global market report 2024 from The Business Research Company provides comprehensive market statistics, including global market size, regional shares, competitor market share, detailed segments, trends, and opportunities. This report offers an in-depth analysis of current and future industry scenarios, delivering a complete perspective for thriving in the industrial automation software market.
Data Wrangling Market, 2024 report by The Business Research Company offers comprehensive insights into the current state of the market and highlights future growth opportunities.
Market Size –
The data wrangling market size has grown rapidly in recent years. It will grow from $2.83 billion in 2023 to $3.28 billion in 2024 at a compound annual growth rate (CAGR) of 15.9%. The growth in the historic period can be attributed to the growing volumes of data, growing demand for advanced analytical algorithm, the rising growth in the healthcare sector, growing volume of data, data integration requirements.
The data wrangling market size is expected to see rapid growth in the next few years. It will grow to $5.90 billion in 2028 at a compound annual growth rate (CAGR) of 15.8%. The growth in the forecast period can be attributed to growing concern about data veracity, the rapid pace of data generation, cost reduction, data complexity, the rise of self-service analytics. Major trends in the forecast period include advancement in AI and big data technologies, time-intensive nature, automated data wrangling, data quality assurance, cloud-based data wrangling.
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Scope Of Data Wrangling Market
The Business Research Company’s reports encompass a wide range of information, including:
1. Market Size (Historic and Forecast): Analysis of the market’s historical performance and projections for future growth.
2. Drivers: Examination of the key factors propelling market growth.
3. Trends: Identification of emerging trends and patterns shaping the market landscape.
4. Key Segments: Breakdown of the market into its primary segments and their respective performance.
5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.
6. Macro Economic Factors: Assessment of broader economic elements impacting the market.
Data Wrangling Market Overview
Market Drivers –
The growing volumes of data are expected to propel the growth of the data wrangling market going forward. Data volume refers to the amount of data generated, collected, processed, or stored within a specific system, organization, or dataset. The volume of data is on the rise due to the proliferation of connected devices, growing internet usage, and increased digitalization. As data volumes grow, data wrangling has become increasingly essential for effectively cleaning, organizing, and structuring large datasets to allow users to make better decisions quickly and gain more accurate insights from the vast amounts of data available. For instance, in December 2022, according to the Linux Foundation, a US-based non-profit organization, an average end-user organization with a storage capacity of up to 20 petabytes experienced annual data growth of 566 terabytes (TB) in 2021 and 1,746 TB in 2022. Similarly, organizations with a storage capacity of up to 25 petabytes saw annual data growth of 2,208 TB in 2022, triple the growth seen in 2021 (700 TB). Therefore, growing volumes of data are driving the growth of the data wrangling market.
Market Trends –
Major companies operating in the data wrangling market are developing innovative technological tools, such as open-source data wrangling tools, to gain a competitive edge in the market. An open-source data wrangling tool is software designed to facilitate the cleaning, transformation, and preparation of raw data for analysis. For instance, in November 2022, Thinking Machines, a Philippines-based technology consulting company, launched GeoWrangler, which is an open-source data wrangling tool that quickly processes geospatial data. This tool aims to improve the efficiency and accessibility of data wrangling by streamlining geospatial projects and enhancing data transformation capabilities. GeoWrangler is a Python library that makes it easier to handle large datasets and create transformation workflows for geospatial data. It is designed to support location intelligence and analytics by providing a tool to speed up the processing of geospatial information.
The data wrangling market covered in this report is segmented –
1) By Component: Tools, Service
2) By Deployment: Cloud-Based, On-Premises
3) By Enterprise Type: Small And Medium Sized, Large
4) By End-User Industry: Information Technology And Telecommunication, Retail, Government, Banking, Financial Services And Insurance (BFSI), Healthcare, Other End-User Industries’
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Regional Insights –
North America was the largest region in the data wrangling market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the data wrangling market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
Key Companies –
Major companies operating in the data wrangling market are International Business Machines Corporation, Oracle Corporation, Cloud Software Group Inc., SAS Institute Inc., Hitachi Vantara, Teradata Corporation, Informatica, Alteryx Inc., Unifi, Altair Engineering Inc., Brillio, Talend, Cloudera Inc., DataRobot Inc., Dataiku, Datawatch Corporation, Datameer Inc., Rapid Insight Inc., Paxata Inc., Zaloni, Trifacta, Onedot, Cambridge Semantics Inc., Impetus Technologies Inc., CoolaData, Ideata Analytics
Table of Contents
1. Executive Summary
2. Data Wrangling Market Characteristics
3. Data Wrangling Market Trends And Strategies
4. Data Wrangling Market – Macro Economic Scenario
5. Global Data Wrangling Market Size and Growth
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32. Global Data Wrangling Market Competitive Benchmarking
33. Global Data Wrangling Market Competitive Dashboard
34. Key Mergers And Acquisitions In The Data Wrangling Market
35. Data Wrangling Market Future Outlook and Potential Analysis
36. Appendix
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