The Asia-Pacific data wrangling market is expected to register a CAGR of 16% during the forecast period (2021 - 2026). The growing volumes of data, advancement in AI and big data technologies, growing concern about data integrity, and increasing need for more useful insights are some of the major factors driving the data wrangling solution and services across Asia-Pacific.
- Rapid growth in the volume and integrity of data generated over various industry verticals in the region has led to the adoption of advanced analytics algorithms to choose insights that could transform a business entity. The regional advancement in data analytics technology is opening new opportunities for business development that deals with assembling and transforming newly acquired data into an appropriate and usable form. Therefore, data-wrangling technology is witnessing increasing adoption in the region, across many consumer-centric businesses.
- In the studied market, the region is also seeing a trend where regional enterprises have been trying to adopt a more data-driven approach with the help of automation tools. By automating complex data engineering tasks, data wrangling solutions can harness the collective intelligence of the organizations. Major APAC countries, such as China, Australia, Japan, and Singapore, have a boom of start-ups with big data and analytics as their platforms. With the rapid adoption of IoT devices, the APAC region is expected to witness tremendous growth during the forecast period.
- The emergence of Big Data across many end-user industry verticals and the increasing need to incorporate AI and ML technologies to gain the ultra-competitive advantage are fuelling the demand for the data Wrangling market in the Asia Pacific over the forecast period. The adoption of data wrangling solutions in the Asia Pacific is not as high as in developed regions like North America. However, the recent developments in the region are attracting many global and US-based high-end technologies vendors of the studied market. US-based data-wrangling vendor, Trifacta, is one such example.
- In September 2019, Trifacta secured USD 100 million in financing from its new investors, including Telstra Ventures, Energy Impact Partners, NTT DOCOMO Ventures, BMW iVentures, and ABN AMRO Digital Impact Fund which also includes additional investment from existing investors, including Accel Partners, Cathay Innovation, Google, Greylock Partners, Ignition Partners, and Infosys. According to the company, the new capital will fuel the adoption of the company’s data wrangling platform. It will also accelerate the company’s continued expansion into new geographies, especially the Asia Pacific, as telecom vendors Telstra and NTT DOCOMO are increasingly trying to capture the regional market.
- The COVID-19 pandemic has created a tidal wave of data. As countries and cities in the region are struggling to grab hold of the scope and scale of the problem, tech corporations and data aggregators have stepped up, filling the gap with dashboards measuring social distancing with the help of the on-location data from mobile phone apps and cell towers, contact-tracing apps using geolocation services and Bluetooth, and modeling efforts to predict epidemic burden and hospital needs. Bad data could produce serious missteps with consequences for millions. Data-wrangling could be deployed for cleaning, structuring, and enriching raw data into the desired format for better decision making in less time and more accurate insights.
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’re 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.
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.
The Asia-Pacific data wrangling market is quite consolidated as few companies hold the majority of the market share in the region. Technological advancements in the market are also bringing sustainable competitive advantage to the companies, and the companies are forming various partnerships to increase their presence in the region.
- 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.
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Table of Contents
1.2 Scope of the Study
4.2 Industry Attractiveness - Porter's Five Forces Analysis
4.2.1 Bargaining Power of Suppliers
4.2.2 Bargaining Power of Consumers
4.2.3 Threat of New Entrants
4.2.4 Threat of Substitute Products
4.2.5 Intensity of Competitive Rivalry
4.3 Industry Value Chain Analysis
4.4 Industry Policies
4.5 Assessment Of COVID-19 Impact On The Industry
5.2 Market Restraints
6.2 By Deployment
6.3 By Enterprise Type
6.3.1 Large Enterprise
6.3.2 Small and Medium Enterprise
6.4 By End-User Industry
6.4.1 IT and Telecommunication
6.4.6 Other End-user Industries
6.5 By Country
6.5.5 Rest of Asia-Pacific
7.1.1 Trifacta Software Inc.
7.1.2 TIBCO Software Inc.
7.1.3 Altair Engineering Inc. (Datawatch Corporation)
7.1.4 Teradata Corporation
7.1.5 Oracle Corporation
7.1.6 SAS Institute Inc.
7.1.7 Talend Inc.
7.1.8 Alteryx Inc.
7.1.9 Paxata Inc.
A selection of companies mentioned in this report includes:
- Trifacta Software Inc.
- TIBCO Software Inc.
- Altair Engineering Inc. (Datawatch Corporation)
- Teradata Corporation
- Oracle Corporation
- SAS Institute Inc.
- Talend Inc.
- Alteryx Inc.
- Paxata Inc.