PUBLISHER: Grand View Research | PRODUCT CODE: 1554241
PUBLISHER: Grand View Research | PRODUCT CODE: 1554241
The global workforce analytics market size is expected to reach USD 5.53 billion by 2030, registering a CAGR of 15.3% during the forecast period, according to a new report by Grand View Research, Inc. The growing concern by the majority of the large enterprises to deal with the humongous volume of data pertinent to human capital is anticipated to spur the demand for the workforce analytics software application platform over the forecast period. The widespread adoption of the human capital information system to enhance the profitability of the industry by reducing operational cost also stimulated market growth.
The retail industry is anticipated to be the fastest-growing application sector owing to the rising implementation of the workforce information system to automate the key human resource-related processes. Retail industries are subjected to exhibit multiple issues related to inventory management, in-store product distribution, customer handling, and deployment of a sufficient number of resources to the store locations.
The incidence of the public and private cloud storage systems by several business entities with respect to various data applications scalability, tool capabilities, and optimum implementation is anticipated to bolster the popularity of cloud storage platforms in the field of data analytics. The effective combination of data analytics tools and cloud computing platforms to enhance execution flexibility and agility of the data management system aggravated the demand for the incorporation of a cloud management system by the majority of the business enterprises.
Additionally, the cloud storage platform also enables the data analyst to optimize linear scalability, database virtualization, work management, and storage optimization. Cloud computing platforms categorically act as a complementary technological paradigm by enabling flexible network access to a shared pool of configurable computing resources with minimal cost to the management.