The global data wrangling market is expected to gain significant traction and is projected to grow at a Compound Annual Growth Rate (CAGR) of 19.7% during the forecast period to reach a market size of USD 3.18 billion by 2023.
The major factors for the growth of the data wrangling market are the emergence of big data across industry verticals and the advancements in the field of data science to incorporate AI and ML algorithms to seek advanced analytics by the corporates.
Data wrangling tools organize raw or unstructured data into a structured format through various steps, which include discovering, structuring, cleaning, enriching, and validating the data sets generated across organizations. These tools run a statistical algorithm on high-performance data engines to combine data from numerous sources and arrange it in a user comprehensible format to derive downstream analysis.
Data wrangling has traditionally been a task of business analysts and data scientists, wherein the analyst would manually dig into big data and arrange it in a proper format to simplify analytical activities. The manual process is time consuming and tedious, and if the size of data is large, then it becomes more complex impacting the performance leading to operational inefficiencies. With the rise in AI and ML potential for data wrangling, it has now become easier to code a model to learn and predict. Moreover, ML can help organizations to reduce their data manipulation and learning and development activities from 3–4 weeks to a few days.
The on-premises deployment model is expected to be adopted at a high rate as compared to on-demand deployment model. The on-premises deployment model is expected to gain traction across the globe during the forecast period, as it helps users have control over the platform, software, and data.
Small and Medium-sized Enterprises (SMEs) are organizations with less than 1,000 employees. The SMEs are increasingly adopting the data wrangling solutions as they do not require any additional skills or infrastructure to deploy and manage. Moreover, their capability for handling voluminous data enables SMEs to prepare data and arrange it in a proper format.
Asia Pacific (APAC) is expected to grow at the highest CAGR during the forecast period. The most important growth factor is the manufacturing prowess of the emerging economies, such as China and India, to manufacture goods and export cost-efficient products across the globe.
China is expected to be at the forefront in the adoption of data wrangling tools and services. Enterprises in China are increasingly adopting the data wrangling solutions to gain insights from the data generated through various industry verticals, such as manufacturing, and retail and eCommerce. According to Mintel Group’s estimation, online spending in China was 45.7% of total per capita income in 2017 and it is expected to grow at a steady pace in between 2017 and 2019. Hence, the rise in eCommerce spending is expected to complement the growth of data wrangling solutions during the forecast period. Moreover, China is gradually investing in new technologies, such as AI and ML. The country is progressing toward automating the production activities, which could be a significant opportunity for data wrangling solution providers.