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Data Wrangling - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts 2019 - 2029

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    Report

  • 100 Pages
  • February 2024
  • Region: Global
  • Mordor Intelligence
  • ID: 4622690
The Data Wrangling Market size is estimated at USD 3.41 billion in 2024, and is expected to reach USD 5.75 billion by 2029, growing at a CAGR of 11.03% during the forecast period (2024-2029).

The creation of various automated technologies has already enhanced and improved the data-wrangling procedure. The industry would create more complex AI solutions during the forecast period to assist the processes of data wrangling and data analysis.

Key Highlights

  • The adoption of sophisticated analytics algorithms to choose insights that might revolutionize a business entity results from the rapid development in the quantity and reliability of data collected throughout many industrial verticals. Massive amounts of unstructured data have also been produced due to the surge in Big Data usage. Applications for iterative and interactive data wrangling may identify distributions and inconsistencies and suggest process improvement.
  • Data manipulation can offer statistical insights into the metadata by making the information more consistent. Increased metadata consistency makes it possible for automated technologies to examine the data more quickly and precisely, frequently leading to these findings. Data wrangling would clean the information to enable a model to operate without problems, mainly in developing a model about expected market performance.
  • Businesses are increasingly using data wrangling for real-time forecasting and monitoring of numerous events that may impact their performance. The market for data wrangling is expanding due to the potential to mitigate risks by executing complicated judgments concerning unplanned occurrences, such as cyberattacks and other emergencies. Also, as more cyberattacks occur, there is a growing demand for data wrangling since it makes data simpler to find and recover.
  • Growing concerns about information loss and theft, expanding Bring Your Own Device (BYOD) trends, and business mobility are just a few factors that are significantly accelerating the growth of the data wrangling market.The industry of data wrangling is predicted to benefit significantly from advances in edge computing.
  • However, issues with data quality are limiting the market's ability to expand.The data-wrangling industry is anticipated to face challenges due to a lack of readiness to switch from conventional ETL tools to cutting-edge automated technologies. Further, one of the key obstacles to this market's expansion is the lack of knowledge about data-wrangling tools among small and medium-sized businesses.
  • The COVID-19 epidemic brought on a considerable data influx. Technological firms and data aggregators exploited local data from cell towers and mobile applications to impose social segregation and close the gaps using dashboards that monitored and tracked contacts. Applications predicted hospital requirements and epidemic burden using Bluetooth, modeling efforts, and geolocation services. As a result of the flawed data produced throughout this procedure, millions of people were expected to be negatively impacted. Data wrangling is used to clean, arrange, and enhance raw data into the appropriate format for users to make better decisions more quickly and accurately. As a result, COVID-19's requirement for data wrangling provided market potential for expansion.

Data Wrangling Market Trends

Large Enterprises are Analyzed to Hold Significant Market Share

  • Large enterprises are expected to hold significant market share in the data wrangling market primarly due to increasing adoption of AI and ML, growing volume of data owing to the substantial adoption of advanced technologies. Furthermore, increasing regulatory pressure among the large enterprises is expected to present major growth opportunities for the expansion of the market in future.
  • Additionally, the ability of data-wrangling solutions to deliver better and faster decision-making and to offer a competitive advantage by analyzing and acting upon information promptly further boosts the demand among large enterprises. Furthermore, large enterprises are adopting data wrangling for real-time monitoring and forecasting of various occasions that may affect the performance of large organizations.
  • Moreover, according to IBM, the adoption of AI varies amongst businesses, countries, and sectors. While larger firms are twice as likely to have actively used AI as part of their company operations, smaller businesses are less likely. Companies are more likely to investigate AI than actively pursue it. As of 2022, a majority of IT workers in China and India, compared to markets like South Korea (22%), Australia (24%), the United States (25%), and the United Kingdom (26%), believe their organization is already actively employing AI.
  • Further, large businesses are constantly discovering new data kinds as big data continues to progress. Data management, however, keeps becoming a more significant challenge for firms as technology produces more and more data sources. Such companies significantly recognize the importance of data wrangling in the large businesses, thereby driving market growth.


North America is Expected to Hold the Significant Share

  • North America is expected to dominate data wrangling during the forecast period, as the region remains one of the most significant contributors to the adoption of data wrangling tools and services. Further, the presence of major market vendors coupled witg growing adoption among end-user industries is analyzed to boost the market growth in the region over the forecast period.
  • The region is expected to witness massive growth along with the application of big data due to the emergence of Industry 4.0 services. Moreover, big data is an enormous phenomenon in the United States, and companies from various industries benefit from collecting, analyzing, and manipulating vast amounts of data from multiple sources.
  • The significant shareholding firms are considerably based in the North America region, which significantly drives the market with considerable investments and developments in the region. Companies such as Trifacta, Altair Engineering, Inc., TIBCO Software Inc., Oracle Corporation, SAS Institute Inc., etc., are based in the United States and are actively engaged in the operation of data wrangling in the region.
  • The rising technological trends in terms of investments, adoption, and integration of various technologies in the region would significantly create opportunities for data wrangling technology in assisting firms to work effectively in handling huge amounts of data. Further, the increased trends of cloud adoption in the region post-pandemic boosted market growth in the region.


Data Wrangling Industry Overview

The data wrangling market is consolidated owing to the presence of a few key players, such as Alteryx, Inc., Oracle Corporation, and Teradata Corporation, amongst others. Their ability to continually innovate their offerings has allowed them to gain a competitive advantage over others. Through research and development, strategic partnerships, and mergers and acquisitions, these players have gained a stronger footprint in the market.

In March 2023, Simplebim released version 10 of its BIM data wrangling software used by construction firms, BIM managers, architects, and structural and design engineers. According to the company, the latest release by the company opens up new ways to use data in IFC files to enable enhanced production planning and scheduling, procurement, tendering, cost estimation, monitoring, installation work, and other downstream BIM data usage.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support


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Table of Contents

1 INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY3 EXECUTIVE SUMMARY
4 MARKET INSIGHTS
4.1 Market Overview
4.2 Industry Value Chain Analysis
4.3 Industry Attractiveness - Porter's Five Forces Analysis
4.3.1 Bargaining Power of Suppliers
4.3.2 Bargaining Power of Buyers/Consumers
4.3.3 Threat of New Entrants
4.3.4 Threat of Substitute Products
4.3.5 Intensity of Competitive Rivalry
4.4 Assessment of the Impact of COVID-19 on the Market
5 MARKET DYNAMICS
5.1 Market Drivers
5.1.1 Growing Volumes of Data
5.1.2 Advancement in AI And Big Data Technologies
5.1.3 Growing Concern about Data Veracity
5.2 Market Restraints
5.2.1 Lack Of Awareness Of Data Wrangling Tools Among Enterprises
5.2.2 Explicit Data Access Permission
6 MARKET SEGMENTATION
6.1 By Component
6.1.1 Tools
6.1.2 Service
6.2 By Deployment
6.2.1 Cloud-Based
6.2.2 On-premises
6.3 By Enterprise Type
6.3.1 Small and Medium Sized
6.3.2 Large
6.4 By End-user Industry
6.4.1 IT and Telecommunication
6.4.2 Retail
6.4.3 Government
6.4.4 BFSI
6.4.5 Healthcare
6.4.6 Other End-user Industries
6.5 Geography
6.5.1 North America
6.5.1.1 United States
6.5.1.2 Canada
6.5.2 Europe
6.5.2.1 United Kingdom
6.5.2.2 Germany
6.5.2.3 France
6.5.2.4 Rest of Europe
6.5.3 Asia-Pacific
6.5.3.1 China
6.5.3.2 Japan
6.5.3.3 Singapore
6.5.3.4 Rest of Asia-Pacific
6.5.4 Latin America
6.5.4.1 Mexico
6.5.4.2 Brazil
6.5.4.3 Rest of Latin America
6.5.5 Middle East and Africa
6.5.5.1 United Arab Emirates
6.5.5.2 Saudi Arabia
6.5.5.3 Rest of Middle-East & Africa
7 COMPETITIVE LANDSCAPE
7.1 Company Profiles
7.1.1 Alteryx, Inc.
7.1.2 TIBCO Software Inc. (Cloud Software Group, Inc.)
7.1.3 Altair Engineering Inc.
7.1.4 Teradata Corporation
7.1.5 Oracle Corporation
7.1.6 SAS Institute Inc.
7.1.7 Datameer, Inc.
7.1.8 DataRobot, Inc.
7.1.9 Cloudera, Inc.
7.1.10 Cambridge Semantics, Inc.
8 INVESTMENT ANALYSIS9 MARKET OPPORTUNITIES AND FUTURE TRENDS

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • Alteryx, Inc.
  • TIBCO Software Inc. (Cloud Software Group, Inc.)
  • Altair Engineering Inc.
  • Teradata Corporation
  • Oracle Corporation
  • SAS Institute Inc.
  • Datameer, Inc.
  • DataRobot, Inc.
  • Cloudera, Inc.
  • Cambridge Semantics, Inc.

Methodology

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