Market Trends of APAC Data Wrangling Industry
This section covers the major market trends shaping the APAC Data Wrangling Market according to our research experts:
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.
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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.
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