New York, July 27, 2022 (GLOBE NEWSWIRE) -- The Insight Partners published latest research study on “Data Wrangling Market Forecast to 2027 - COVID-19 Impact and Global Analysis By Component (Tools and Services); Business Function (Finance, Marketing and Sales, Operations, Human Resources and, Legal); Organization Size (SMEs and Large Enterprise); and Industry Vertical (BFSI, Government, Healthcare, IT and Telecom, Manufacturing, Retail, and Others)” The global data wrangling market growth is driven by continuous technological advancements. North America dominated the data wrangling market in 2019 with a share of 37.9%.
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| Market Size Value in | US$ 1.37 billion in 2019 |
| Market Size Value by | US$ 6.03 billion by 2027 |
| Growth rate | CAGR of 20.9% from 2020-2027 |
| Forecast Period | 2020-2027 |
| Base Year | 2020 |
| No. of Pages | 191 |
| No. Tables | 110 |
| No. of Charts & Figures | 95 |
| Historical data available | Yes |
| Segments covered | Component ; Business Function ; Organization Size ; and Industry Vertical |
| Regional scope | North America; Europe; Asia Pacific; Latin America; MEA |
| Country scope | US, UK, Canada, Germany, France, Italy, Australia, Russia, China, Japan, South Korea, Saudi Arabia, Brazil, Argentina |
| Report coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
Data wrangling is receiving high momentum across the world. The roll-out of data wrangling tools to combat cyber threats is paving the path for market growth. Growing emphasis on data protection laws by government authorities in countries, such as the UK, the US, Germany, and Singapore, is propelling the adoption rate of data wrangling. Moreover, integrating sophisticated technologies, such as machine learning (ML) and artificial intelligence (AI), into data wrangling tools accelerates the entire business's output and productivity. The companies incorporate AI and ML to automatically analyze sensitive data behavior while improving security measures to prevent a data breach and data loss. The managers can rapidly measure the outcomes and develop a quick feedback loop with near-real-time data collection. Benefit of integrating AI and ML will help in boosting the penetration of data wrangling across the world.
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APAC Data Wrangling Market to Grow at Highest CAGR during Forecast Period
The presence of developing economies such as China, India, South Korea, Malaysia, the Philippines, and Taiwan make Asia a developing continent. Growing economic structure and technological advancements support the growth of a diverse range of industries in the region. The region has the potential to invest massive amounts in data wrangling tools to manage complex industrial data in huge volumes.
APAC is has a rising banking sector, and there has been a surge in the adoption of a data-driven approach using automation tools by the regional enterprises. Automating complex data engineering tasks through data wrangling tools can help manage the collective intelligence of enterprises. Further, there is an increasing number of start-ups in countries such as Japan, China, India, Australia, and Singapore, engaged in the business of offering analytics and big data platforms. The emergence of new enterprises providing data analytics solutions, growing banking industry, expanding conceptualization of smart cities in India, China, and other APAC countries help fuel the penetration of data wrangling tools in the region.
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Key Findings of Study:
Big data requires data wrangling and performs its task of delivering quality information for management decision making. The large volume and high speed propel the need to classify data. Similarly, due to its ability to learn the tags for every kind of information set in the system, machine learning is likely to boost the market for data wrangling. Also, various companies are integrating technologies such as AI and ML, to automatically analyze sensitive data behavior and enhance security measures to prevent incidents involving data breach. These technologies help companies rate of reduce errors introduced during manual processes or user interference, while classifying sensitive data and empowering data wrangling solutions, to leverage continuous learning processes to automatically categorize data in a more refined and precise way, based on user-defined rules and policies. By presenting better and more complete data, third-party organizations and government units can significantly add value to the quality of management systems and MDM platforms, contributing to precise decision-making. AI makes suggestions on what to get from a given set of data and the build's data connections. Having detailed and clean data at one place allows companies to make informed decisions effectively.
Data-driven decision-making (DDDM) is the practice of data collection, analysis, and decision-making based on information-derived insights. This process contradicts the process of making decisions based on gut feeling, instinct, tradition, or theory. Data-driven decisions are more objective, and their impact on metrics can be quickly evaluated. A manufacturer can increase the production rate by timing the assembly line and noting delays, if any. The managers can time the process again after introducing bottleneck solutions to see if the change would result in time savings. They could also try another solution to get the best production acceleration results. With DDDM, organizations are becoming more agile, detecting new business opportunities sooner and responding faster to market changes. Managers can quickly measure outcomes and create a fast feedback loop with near-real-time data collection. These capabilities make data-driven enterprises extremely customer-focused and more competitive. The same applies to organizations and service providers, including public educational agencies. Data-driven decision-making leads to greater transparency and accountability, which can enhance teamwork and employee engagement.
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