A Data-Driven Analysis of the Paradigm Shift From Permanent to Contractual Recruitment

1. Introduction

The continuous growth of digitization and smart devices at workplace made work flexible. The human workforce is still required for creative working and doing manual activities. The organizations and industries are employing contract workers and permanent workers for getting work done as soon as possible to achieve the benchmarks. The inclination of organizations towards contract workers makes it necessary to made research in this area. The several industries and employment hubs hold different viewpoints with respect to employment of workers as contract workers generate maximum outcomes within stipulated time period as compare to permanent workers and the contract workers are more reliable and trustworthy. The dilemma among the different opinions and requirement of workers introduces big data as a methodology for analyzing the benefits obtained by different types of workers.

Big data is used for resolving the problem of handling large volumes of data and complex structure that is assembled from different data sources as streaming data, transactional data, mobile generated data and web data records. (Gupta et al., 2018) found that data is compiled from various sources are accumulated in databases which increases the size of data. Big data analysis is the process of knowledge pattern discovery to handle huge amount of data and use it for relevant information delivery. Big data plays a vital role for noncommercial use of knowledge discovery in research area for commercials. Pattern and relationship discovery finding is crucial for market basket analysis and customer segmentation to find the relationship between organizations, commercials and customers for balancing demand and supply chain. The big data is potentially required in healthcare, public sector, retail, manufacturing, personal location data and smart routing technologies.

Big data stores data in a different manner from traditional warehouses. (Latib et al., 2018) found that big data holds the ability to deal with both emerging data and historic data for transformation and analysis of data using MapReduce. The big data stores data in the form of log files and sensored storage modules. (Gupta et al., 2017) found that big data is essential for business and enterprises that always look up for generating more revenues by building customer experiences by improving their business models using big data analysis. The esteemed properties of big data allow author to find patterns and analyses data for finding the merits of employing contract workers in organizations. The solution to the problem of skill shortage is contract employees that provides experienced skilled employees in particular field without the requirement of on job training. The frequent market shifts provide volatile job markets which give rise to contract workers rather than committing new employees. In the rapidly changing market, there are number of benefits for hiring contract workers such as help employers find workers with the right skills faster, assess and respond to changing business needs, finding the right employee for a role, providing shorter notice periods and maximize cost savings and efficiencies.

There are 10 v’s in big data which is necessary for information analysis that is velocity, variety, volume, value, variability, veracity, validity, vulnerability, volatility and visualization. (Rana et al., 2018) found that the data is extracted in the form of structured, semi-structured and unstructured which defines the Variety aspect of big data and Velocity manages the time constraint and time variant processes that can be for real time data, near real time data, streaming data, and batch processes. The size of data can be in terabytes, petabytes exabytes and zettabytes which is managed by Volume. (Wiktorski., 2019) found that Value refers to the process of finding in depth information. Variability manages inconsistent data while veracity deals with reliability. Validity manages system accuracy and its discreteness. Vulnerability deals with security concerns, Volatility deals with current and historic data and visualization refers to big data analysis.