Asia-Pacific Data Wrangling 2021 Market Technology Advancements and Research – Teradata Corporation, Oracle Corporation, SAS Institute Inc, Talend Inc.

Market Insights Reports delivers key insights on the Data Wrangling market in its latest report titled, “Asia-Pacific Data Wrangling Market – Growth, Trends, COVID-19 Impact, And Forecasts (2021 – 2025)” provides an in-depth analysis of key players strategies, market growth, product demand, growth factors, technological innovations, regional outlook and Asia-Pacific dynamics which includes drivers, challenges, and opportunities dominant in the industry. This Market study provides comprehensive data that enhances the understanding, scope, and application of this report.

The Asia-Pacific Data Wrangling Market is expected to register a CAGR of 16% over the forecast period from 2020 to 2025.

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Top Key Players in the Asia-Pacific Data Wrangling Market: Trifacta Software Inc., TIBCO Software Inc., Altair Engineering Inc. (Datawatch Corporation), Teradata Corporation, Oracle Corporation, SAS Institute Inc, Talend Inc., Alteryx Inc., Paxata Inc. s and Others.

Industry News:

May 2020 – TIBCO Spotfire 10.5 added new data wrangling, data access, and administration features to their product portfolio. With this inclusion, the company continues to make it easier and shortens the time it takes to add, wrangle, visualize, and gain insights from data.

– April 2020 – Trifacta Software Inc. launched the new version of Dataprep that brings new and enhanced AI-driven features to advance the wrangling experience a step further. The company infused AI-driven functions in many parts of Dataprep so it can suggest the best ways to transform data or figure out automatically how to clean the data, even for complex analytics cases.

Key Market Trends:

Cloud is Expected to Witness Significant Growth

– Many organizations are moving their data to cloud-based environments. Still, it’s a transition that cannot be done in one fell swoop, and for some, a transformation that won’t ever happen ultimately. This means that most organizations manage multiple data environments, including a mix of on-prem, private cloud, and public cloud solutions, also known as a hybrid cloud environment. Data wrangling is considered one of the most challenging parts of the implementation of analytics. On average, organizations generally report that 80% of any data project is spent wrangling data, while only 20% is left for analysis.

– In the modern era of IoT, AI, and cloud computing, architectures for data management have changed dramatically. Instead of recording millions of transactions, organizations in the region are recording billions of interactions. Companies are capturing signals that can inform business opportunities and unlock new sources of value for organizations rather than solely inputting data to support formal business processes. Today’s data-driven organizations have adopted new, agile data management practices. They are moving data into flexible centralized storage structures, such as data lakes and cloud blob storage, and are adopting new data wrangling technologies to assess and transform data for use.

– Data wrangling solutions running on the cloud can help streamline Machine Learning applications so that the teams can focus on the work that matters, such as creating accurate predictions that improve the products, services, and the organization’s efficiency. An automated cloud-based data wrangling solution can perform the bulk of the work for the data science teams automatically such as it could identify profiles and interactive charts, granting immediate visibility into trends and informing on data issues and a final published data set of any size that is fully prepared to be appropriately analyzed by downstream analytics tools.

– As of April 2020, Trifacta has over 100,000 users who have executed more than six million jobs across the major cloud providers and is natively integrated into all three major cloud providers, which include AWS, Microsoft Azure, and Google Cloud as well as fast-growing cloud services such as Snowflake and Databricks. As the demand for data preparation accelerates as organizations move more AI, analytics, and machine learning workloads to the cloud, data wrangling could be used by organizations to take advantage of the market opportunity ahead of the competition.

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China is Expected to Hold Major Share

– China is emerging as one of the significant investors in AI technologies globally. According to the China Money Network, currently, 14 Chinese AI organizations are valued at USD 1 billion, and their worth consolidated comes to USD 40.5 billion. According to Tsinghua University, Chinese AI start-ups raised USD 27.7 billion through 369 VC deals in 2017-2018. Also, according to recent research, in China, venture capital investment in computer vision technology firms has increased four-fold from 2016-2018, surpassing an aggregate of USD 8 billion. Such statistics validate the dominance of China in the adoption of tools such as data wrangling.

– China is doubling down on the digital transformation of its economy with a plan to build industrial big data centers nationwide. This enables massive amounts of information, mostly production data that could be used for developing more efficient industries. That strategy was unveiled in a directive in May 2020 by the Ministry of Industry and Information Technology (MIIT), which called on local authorities in 23 provinces, five autonomous regions, and four municipalities to support the establishment of these new big data centers, which will help bolster efforts to upgrade the country’s manufacturing sector. Such instances are expected to impact the market in the country positively.

– In the past few decades, China’s cities have experienced a period of rapid development. Emerging big data and open data have provided new opportunities for urban studies and observers to observe and understand these changes better. Data wrangling tools are expected to convert big data such that it could provide the analysis, visualization, and applications in the context of China’s urban planning, urban modeling methods, typical models, and emerging trends and potentials revolution of big data in urban planning.

– China’s transformation into a digital economy was already well underway before the coronavirus outbreak, driven by its massive adoption of internet-based technologies, mobile apps, and artificial intelligence applications. Higher data collection has helped prevent the virus from spreading in China because it enables precise reporting of hotspots. Central and provincial governments are pushing to gather and analyze even more data to help contain the spread of the disease where data-wrangling could be deployed to convert the raw data to gain more actionable insights.

This Data Wrangling Market report addresses:

– The Market size from 2016-2021

– Expected market growth until 2025

– Forecast of how market drivers, restraints, and future opportunities will affect

– Segment and regions that will drive or lead the market growth and why

– Comprehensive mapping of the competitive landscape

– In-depth analysis of key sustainability strategies adopted by market place

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Post Covid-19 Analysis:

All the reports that we list have been tracking the impact of COVID-19 on the market. Both upstream and downstream of the entire supply chain has been accounted for while doing this. Also, where possible, we will provide an additional COVID-19 update supplement/report to the report in Q3, please check with the sales team.


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