Big Data as a Service BDaaS Market Size is forecasted to grow by 2032

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Market Overview: As per a study by Persistence Market Research, from US$222.6 billion in 2022 to US$247.7 billion in 2032, the global BDaaS (Big Data as a Service) Market is predicted to grow at a 25.7% CAGR from 2023 to 2032. The combination of big data analytics technologies and cloud computing platforms has led to the development of Big Data as a Service or BDaaS. BDaaS offers analyses of large and complex datasets over the Internet or as cloud-hosted services. The rise in the growth in data due to digitization and automation, and rise in the growth for demand in Industries to gain actionable insights from big data, which is driving the market demand for BDaaS. Moreover, the rising number of connected devices globally is one of the factors that is expected to fuel the market growth during the forecast period. End-user industries such as government, media & entertainment, retail, BFSI, and manufacturing are adopting connected devices for their business and manufacturing units. Some other factors that are accelerating the growth of the BDaaS Market include the growing demand in industries, which will gain actionable insights and growing organizational data across industries due to digitalization and automation of business processes. Further, the growing demand of real-time data analytics is another factor that is fueling the market growth during the analysis period. In addition, increase in the adoption of predictive modeling tools, rise in the government initiatives supporting big data infrastructure, rise in the growth in data due to digitization and automation, and rise in the growth for demand in Industries to gain actionable insights from big data. These factors are favoring the market growth over the analysis period. Market Growth Factors: 1. Rising Volume of Big Data: o The exponential growth of data generated by businesses, social media, IoT devices, and other sources has created a need for scalable solutions to store, process, and analyze large volumes of data. 2. Cost-Effective Solutions:


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