云端型巨量数据的全球市场 - 各引进类型(内部部署，云端)、各终端用户行业(通讯、IT，能源、电力，银行、金融服务、保险，医疗，零售)、各地区的成长，趋势，市场预测
Global Big Data as a Service Market - Segmented by Deployment Type (On-premise, Cloud), End-User (Telecom and IT, Energy and Power, BFSI, Healthcare, Retail), and Region - Growth, Trends, and Forecast (2018 - 2023)
|出版商||Mordor Intelligence LLP||商品编码||646739|
|出版日期||内容信息||英文 108 Pages
|云端型巨量数据的全球市场 - 各引进类型(内部部署，云端)、各终端用户行业(通讯、IT，能源、电力，银行、金融服务、保险，医疗，零售)、各地区的成长，趋势，市场预测 Global Big Data as a Service Market - Segmented by Deployment Type (On-premise, Cloud), End-User (Telecom and IT, Energy and Power, BFSI, Healthcare, Retail), and Region - Growth, Trends, and Forecast (2018 - 2023)|
|出版日期: 2018年04月26日||内容信息: 英文 108 Pages||
The big data as a service market is expected to witness a CAGR of 15.73% during the forecast period (2018 - 2023) and is estimated to the reach the value of USD 38.06 billion by 2023. The growing amount of structured and unstructured data, driven by the increased penetration of internet and the advent of IoT, has led to companies across various industries seeking solutions to help make data-driven decisions. With the ability to store, process, and analyze large volumes of data, Big-Data-as-a-service (BDaaS) is transforming businesses across the world. The implementation of BDaaS enables companies to focus on the outputs and analyses of their business, rather than technical aspects. These solutions reduce upfront infrastructure, data storage, and management costs significantly. Also, it provides increased flexibility and customization to analyze and manipulate data, thus offering improved data management and accessibility to complex data analysis, making it a viable solution for large businesses that handle enormous amounts of data. It is estimated that global business spending on cloud-based data analytics solutions is expected to grow from approximately 15% in 2016 to 38% in 2023, thus presenting greater growth prospects for the BDaaS market.
There has been an unprecedented explosion of data in the BFSI industry, which can be turned into actionable insights if analysed. Additional factors, such as depleting operational efficiency and the increasing operational costs, along with the ability to predict the outcome of a particular event have all emphasized on the need for big data analytics. The use of analytics by many banks are already underway such data driven decisions are further expected to improve the customer relationship and experience in turn contribution to sales for the banks. Such analytics insights obtained are useful for various applications, such as Fraud detection, application screening, which emphasizes the need for accurate variables in case of predictive analytics. This further helps in optimizing the target selection various activities, such as loanee selection and business to invest in etc. One of the most primary function of such analytics tools is fraud detection as many of the banking industries primary concern is fraud, big data analytics helps in judging the chances of fraud for a particular person.
Rapid technological developments in the information technology sector and increasing business operations mark Asia-Pacific (APAC) as the most important market for Big Data in banking during the forecast period. The biggest contributors to this market are China and India that account for most of the revenue in the APAC region. Many organizations in the APAC region are increasingly depending on digital systems to realize their goals. Several vendors such as SAP and IBM provide wide-range of Big Data analytics services to banks in the region. Some of the core capabilities of these services include real-time monitoring, big cloud services, and other customized dashboards for easier retrieval of data to ease the workflows. These tools enable organizations for on-the-resource planning and offer modified plans to aid decision-making.
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