Enterprises are adopting analytical solutions to recognize the data pattern, identify the issues related to inconsistent data, and analyze it. Data wrangling tools enable enterprises to in cleaning, enriching, and structuring data. This tools will allow enterprises to have more comprehensive control over their systems.
Turning data into insights has become gradually complex due to the number of enterprise applications that are generating data, which is both structured and unstructured. Due to the rapid increase of data volumes across industry verticals, there is an increase in demand for advanced analytics algorithms by enterprises to gain insights from a significant amount of data generated.
Due to higher adoption of edge analytics, data wrangling tools are widely used by verticals such as BFSI, retail & e-commerce, automotive and many others. These tools would enable enterprises to filter out unwanted data and structure them and would be efficiently used by artificial intelligence and machine learning algorithms to analyze and provide data insights.
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Data wrangling processes include discovering of data features, structuring, cleaning, enriching, validating, enriching and publishing of data. Through this data wrangling process, an extensive variety of data sources are used in different statistics, analytics and data visualization applications.
This process expands the usage of data in the organization and enriches the potential value of data to the business
Data Wrangling Market: Dynamics
The prominence of big data across industry verticals and the integration of artificial intelligence and machine learning technologies with data wrangling tools is expected to fuel the growth of data wrangling market.
Unwillingness to shift from traditional ETL tools to advanced automated tools is expected to hamper the growth of the data wrangling market. Lack of awareness of data wrangling tools among small & medium enterprises is also the major challenge for the growth of this market
Increasing regulations & growth of edge computing is creating growth opportunities for data wrangling market.
Segmentation Overview
The Data Wrangling market is segmented based on business function, component, deployment model, industry vertical and region.
Global Data Wrangling Market: Competition Landscape
Examples of some of the key players in the global Data Wrangling market are Informatica, Oracle Corporation, Teradata, Impetus, Talend, Datawatch, IBM Corporation, Brillio, SAS Institute, Hitachi Vanatara, Infogix, TIBCO, Rapid Insight, Trifacta, etc.
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Data Wrangling Market: Regional Outlook
The data wrangling market is dominated by North America followed by the Asia Pacific and Western Europe. The market in North America is expected to witness higher market share due to the presence of prominent players and broad adoption of data wrangling tools and services.
APAC is expected to grow at a significant rate in the global data wrangling market during the forecast period. Major APAC countries, such as Australia and New Zealand, China, Japan, and Singapore are observing higher growth in big data and analytics startups. With a growing number of smart cities and increase in IoT devices, the region is expected to witness massive growth in the upcoming years.
In March 2017, Trifacta collaborated with Google Inc. Through this collaboration, Trifacta created Google Cloud Dataprep that would enable Google to help enterprises shift their data to the cloud, wherein Trifacta would help them resolve their data wrangling challenges.
The research report presents a comprehensive assessment of the market and contains thoughtful insights, facts, historical data, and statistically supported and industry-validated market data. It also contains projections using a suitable set of assumptions and methodologies. The research report provides analysis and information according to market segments such as geographies, application, and industry.
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Global Data Wrangling Market: Segmentation
By Business Function
- Finance
- Sales & Marketing
- Operations
- HR
- Legal
By Component
- Tools
- Services
By Deployment Model
- On-Premises
- Cloud
By Industry Vertical
- Retail
- BFSI
- Government
- Hospitality
- Healthcare
- Energy & Utilities
- Automotive
- Telecommunications & IT
- Others
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