Data Wrangling Market | Brillio, Paxata, Trifacta, SAS Institute, Altair Engineering

The data wrangling market is expected to reach US$ 6,034.4 million by 2027, rising from US$ 1,377.8 million in 2019. The growth rate for revenue curve is estimated to be 20.9% during the forecast period.

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Data wrangling tools and services offer numerous benefits, such as delivering better and faster decision making and providing a competitive advantage by promptly analysing & acting upon information. Companies are also adopting data wrangling for real-time forecasting and monitoring various occasions that may affect the performance of the organizations. The ability to mitigate risks by optimizing complex decisions about unplanned events, such as cyberattacks and other crises, propels the growth of the data wrangling market. Further, the demand for data wrangling is increasing due to growing instances of cyberattacks as the wrangling process makes data easier to locate and retrieve. When it comes to managing risk, compliance, and data security, data wrangling is of particular importance. The wrangling of data involves tagging the data to make it easy to search and track. It also eliminates data duplications, reducing storage and backup costs while accelerating the search process. Data wrangling provides a clear view of all data within the control of an organization and a clear understanding of where the collected data is stored, how to access it quickly, and the best way to protect it against potential security risks. Once implemented, data wrangling provides an organized framework that facilitates more appropriate data protection measures and promotes employee compliance with security policies.

Integration of AI and ML into Data Wrangling

In the new era of fueling data, the advent of big data and machine learning would prove to be a game-changer for industries. Big data requires data wrangling and performs its task of delivering quality information for management decision making. The need to classify data is characterized by its large volume and speed. Similarly, due to its ability to learn the tags for every kind of information set in the system, machine learning is likely to boost the market for data wrangling. Also, companies are integrating technologies, such as AI and ML, to automatically analyze sensitive data behavior and enhance security measures to prevent incidents involving data breach. These technologies help companies reduce error rates through manual processes or user interference while classifying sensitive data and empowering data wrangling solutions to leverage continuous learning processes to automatically categorize data in a more refined and precise way, based on user-defined rules and policies. By presenting better and more complete data, third-party organizations and government units can significantly add value to the quality of a management system and MDM platforms, contributing to precise decision making. AI makes suggestions on what to get from a given set of data and the build's data connections. When a company has detailed and clean data in one place, the chances of making informed decisions are higher.

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This release was published on openPR.