Data Wrangling Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

Market Overview

The data wrangling market was valued at USD 1.31 billion in 2020 and is expected to reach USD 2.28 billion by 2026, at a CAGR of 9.65% over the forecast period 2021 - 2026.

• Rapid growth in the volume and veracity of data generated over various industry verticals has led to the adoption of advanced analytics algorithms to choose insights which could transform a business entity. Domo Inc estimated that 1.7MB of data will be created every second for every person on earth by 2020 end.
• Some of the prominent players in the industry are increasingly investing in order to further the technology which is expected to boost the market growth over the forecast period. For instance, in September 2019, Trifacta, the global leader in data wrangling, announced that it had raised USD 100 Million to Support Explosive Growth of Data Wrangling for AI and the Cloud.
• Also in September 2019, Mindtech Global Ltd, announced the availability of Mindtech Chameleon Simulator, creating synthetic vision datasets for training neural networks, and Mindtech Chameleon AI Tools, that provide end to end data management for deep learning systems. The Chameleon AI Tools simplify data wrangling tasks.
• Further, the explosion in Big Data adoption has also created huge sets of unstructured data. By using iterative and interactive data wrangling applications, it is possible to visualize distributions and inconsistencies as well as provide recommendations to improve the processes.
• The increase in the number of active users on social media web sites has resulted in the adoption of data wrangling tools by the social media web sites that help them structure the huge amounts of data generated by the users on these sites. For instance, by the end of the fourth quarter, 2018 Facebook had 2.320 billion active monthly facebook users.

Scope of the Report

The process of transformation and mapping of data from one "raw" data form into another format with the purpose of making it more appropriate and valuable for a variety of downstream purposes such as analytics is known as data wrangling. This report segments the market by Component (Tool, Service) Deployment (Cloud-based, On-premise) Enterprise Type (Large, Small- and Medium-sized), End-user Industry, and Geography.

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Key Market Trends

BFSI is Expected to hold a Significant Share

• The banking and financial institutions have been increasingly using data wranglers that enables them to optimize processes and inform agents on how to best interact with customers online, reducing the time spent on data preparation by 15x. This enables such organizations to have a 360-degree view of their clients.
• The Royal Bank of Scotland that serves over 30 million customers worldwide, and ensures that they receive top-notch service have adopted big data particularly unstructured and semistructured data from online customer web chats and is making use of data wranglers to easily extract insights from unstructured data. Such adoption is expected to fuel the demand for the data wranglers over the forecast period.
• Due to the rising trend of digitization, the banking and the financial institutions have started digitizing the banking services through online portals which have allowed them to better serve their consumers. This has resulted in the generation of big data making it imperative for such institutions to have solutions like data wrangling that would allow them to systematically structure the data generated. 
• For instance, JPMorgan Chase in its 2018 annual report stated that the company had hit average mobile customers mark of 33.3 million, a number that was by 11% with Total active digital customers being 49.3 million. This in itself is indicative of the fact of banking institutions could need solutions like Data Wrangling to manage the data systematically. This is expected to boost market growth over the forecast period.
• Also, the data generated by such institutions are so massive that if not managed properly there lies a risk of data being exposed leading to unwanted events of data breaches or leakage. This is propelling the institutions to have solutions like data wrangling that enables them to properly manage the data.

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