Data Wrangling

Data Wrangling

Global Data Wrangling Market to Reach US$7.9 Billion by 2030

The global market for Data Wrangling estimated at US$2.2 Billion in the year 2023, is expected to reach US$7.9 Billion by 2030, growing at a CAGR of 19.9% over the analysis period 2023-2030. Data Wrangling Services, one of the segments analyzed in the report, is expected to record a 17.6% CAGR and reach US$2.1 Billion by the end of the analysis period. Growth in the Data Wrangling Tools segment is estimated at 20.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$592.0 Million While China is Forecast to Grow at 18.4% CAGR

The Data Wrangling market in the U.S. is estimated at US$592.0 Million in the year 2023. China, the world`s second largest economy, is forecast to reach a projected market size of US$1.2 Billion by the year 2030 trailing a CAGR of 18.4% over the analysis period 2023-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 18.2% and 16.9% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 14.1% CAGR.

Global Data Wrangling Market - Key Trends and Drivers Summarized

What Is Data Wrangling and Why Is It Fundamental to Big Data Success?

Data wrangling, often referred to as data munging, is the process of transforming and mapping raw data from one form into another to prepare it for analysis. This essential phase of data preparation involves cleaning, structuring, and enriching raw data into a more digestible and useful format. The importance of data wrangling has escalated with the explosion of big data, as businesses and organizations accumulate vast amounts of unstructured data from various sources including IoT devices, social media, and transactional systems. Effective data wrangling ensures that this data can be accurately analyzed to provide actionable insights, drive strategic decision-making, and enhance operational efficiency. Without this critical step, much of the data collected would remain untapped, as raw data is often incomplete, inconsistent, or imprecise.

How Does Data Wrangling Enhance Data Quality and Analysis?

Data wrangling enhances the quality of data by addressing issues such as missing values, errors, and inconsistencies during the cleaning phase. This process also involves transforming and enriching data, which may include normalizing data formats, correcting values, and integrating datasets to enrich the data`s context. For instance, data from different departments within a company can be amalgamated to provide a comprehensive view of operations. Furthermore, the structuring phase of data wrangling helps in organizing the data into a more appropriate format for analysis, such as converting data into model-ready formats for machine learning algorithms. By improving data quality and structure, data wrangling facilitates more sophisticated and accurate analyses, allowing businesses to unlock predictive insights and strategic intelligence from their data assets.

What Are the Challenges Associated with Data Wrangling?

Despite its importance, data wrangling can be a complex, time-consuming, and often challenging process. One of the primary challenges is the sheer volume and variety of data, which can be overwhelming for data professionals to manage manually. Each data source may require a different approach due to varying structures, quality levels, and the types of information contained. Additionally, data wrangling tools and techniques must continuously evolve to keep up with the rapid pace of data creation and the emerging varieties of unstructured data. Another significant challenge is ensuring data privacy and security during the wrangling process, especially when handling sensitive or personal information in compliance with regulations such as GDPR or HIPAA. These challenges necessitate not only skilled data professionals but also advanced tools that can automate many aspects of data wrangling to improve efficiency and accuracy.

What Drives the Growth in the Data Wrangling Market?

The growth in the data wrangling market is driven by several factors, reflecting the increasing importance of data analytics in business strategy. The exponential growth of data generated by digital technologies is a major catalyst, as organizations seek to harness this data for competitive advantage. As analytics technologies become more sophisticated, the demand for high-quality, well-prepared data is higher than ever. Innovations in artificial intelligence and machine learning are also significant drivers, as these technologies require large volumes of curated datasets to train accurate models. Furthermore, the push towards data-driven decision-making across industries has made data wrangling an essential capability for companies looking to extract valuable insights from complex data landscapes. Additionally, regulatory pressures for higher data quality and privacy are pushing businesses to invest in advanced data wrangling solutions that ensure compliance and security. Together, these drivers ensure that data wrangling remains a critical and growing field within the broader landscape of data management and analytics.

Select Competitors (Total 44 Featured) -

• Alteryx, Inc.
• Brillio
• Cooladata Ltd.
• Dataiku, Inc
• Datameer, Inc.
• Hitachi Vantara Corporation
• IBM Corporation
• Ideata Analytics
• Impetus Technologies
• Infogix
• Informatica, LLC
• Innovative Routines International (IRI)
• Onedot AG
• Oracle Corporation
• Paxata
• Rapid Insight.
• SAS Institute, Inc.
• Talend SA
• Teradata Corporation
• TIBCO Software, Inc.
• TMMData
• Trifacta
• Unifi Software

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