Big data includes a combination of structured, semi-structured, and unstructured data collected by organizations, which can be mined for information and used with Artificial Intelligence (AI) and Machine Learning (ML) projects, predictive modeling, and other advanced analytics applications.
Big data is a term that has been popping up in almost every sphere of corporate or organizational setting and discussion, besides a variety of other sectors and industries in recent times. Big data is considered a gamechanger in almost every industry and organizational department, and this is especially true for the Human Resources (HR) industry. In any end-use or application area, big data serves to uncover trends, patterns, and correlations from among large volumes of raw data to help make data-informed decisions.
Big data includes a combination of structured, semi-structured, and unstructured data collected by organizations, which can be mined for information and used with Artificial Intelligence (AI) and Machine Learning (ML) projects, predictive modeling, and other advanced analytics applications. Big data in recruitment entails the process of deploying more than just traditional approaches or screening resumes, and includes use of keywords or mining social media data to filter out candidates with high potential for an opening or job. Put simply, people analytics or big data in recruitment enables filtering through large volumes of data to zero in on the best candidate for a particular job, and this is done in a relatively short span of time than it would take to be done manually. Overall, big data helps to line-up candidates ready to interview, thereby making the hiring process significantly more streamlined and faster. Besides playing a major role in recruitment, big data is helping to streamline processes such as training, development, performance, and compensation, and enables HR managers to make smarter decisions and help an organization meet its goals more efficiently.
In recruitment, recruiters use tools to analyze a candidates' online presence so as to build a profile on the candidate based on their online presence to determine if can be a good fit for the job. This also allows recruiters to track the recruitment process, and establish how long it takes a candidate to complete various stages of the recruitment process. Using this approach recruiters are also able to identify any bottlenecks in the recruitment process and amend to make it more efficient. Sorting data is also an advantage with big data in recruitment, and data based on key performance metrics to identify candidates who best match requirements serve well to speed up the recruitment process.
Big data in recruitment also offers other advantages including improving quality of new hires, creating a better hiring strategy, streamlining the hiring process, and forecasting future hiring needs. Besides, other major advantages to recruiters is the ability to predict future market trends and patterns to foresee what skills a company would require from future talent, and this can even be used to delve into insights such as estimated average tenure by role, understand hiring needs, and work on retention strategies. Big data also gives HR managers the ability to monitor and track efficiency of recruitment efforts and identify most efficient strategies to attract ideal candidates.
Currently, Human Resource Management (HRM) professionals are using recruitment data to attract, identify, and hire top talent, but an increasing number are getting accustomed to leveraging the various other functionalities this concept has to offer, and this is providing a competitive advantage in recruitment. HR organizations also use big data analytics to understand and motivate employees, plan, and use resources efficiently. Big data also aids with real-time prediction of hiring needs, improves quality and retention of new employees, and connects recruiting performance with business performance.
In addition, big data is being used to optimize talent acquisition pipelines with automated recruitment tools that retrieve the best matches and perform candidate analysis. Furthermore, recruiters are currently more equipped to narrow down searches, accurately target ideal or potential candidates for jobs, and also ensure that no biases, such as gender or age factors, have any impacts across the process.
Going by statistics, adoption of big data analytics has boosted job creation at city level by 1.4% and resulted in reduced unemployment for men/males under 25 years and over 45 years and for women/females under 45 years of age. Adoption has also led to more indefinite jobs and fewer short-term engagements, as well as resulted in increased employment for women, youth, and older workers.
Also, an increasing number of companies and organizations are adopting more advanced technologies and approaches to streamline various aspects, processes, and functions in order to enhance productivity and drive revenues. As of July 2023, AI adoption in recruitment indicated a steep incline, with between 35% to 45% of companies utilizing AI recruitment tools. Majority of fortune 500 companies (around 98%) are currently incorporating AI practices, while 65% of recruiters use AI in their hiring processes.
However, while big data is proving to be rather useful for recruiting, it carries along some inherent risks, ranging from violating privacy laws to misconstruing results returned post analytics. Big data can be so useful for recruiting, but beware it comes with its own inherent risks – everything from violating privacy laws to misconstruing the returned results.
One primary issue is related to how a company or HRM stores and manages personal data of candidates. The General Data Protection Regulation (GDPR) is a European Union regulation on information privacy in the European Union and the European Economic Area, and is an important component of EU privacy law and human rights law. As per the new provision, companies holding an individual’s data require explicit consent related to use of that data and information, as well as right to access and greater control over the data in possession of the HR or company. Also, data gathered without knowledge or consent of specific candidate and algorithms used for data analysis could lead to potential bias and profiling, and this could result in legal hurdles. Hence, companies need to ensure GDPR compliance when collecting data about job applicants.
In addition, some level of concern arises as a result of social media profiles and information being accessed and used to make hiring decisions based on data. Currently, this approach is being associated with ethical issues surrounding data-driven hiring, and questions are being raised as to whether a privacy line is being crossed by using an individual’s Facebook profile to make a hiring decision. This also raises questions about the suitability and professionalism of making a hiring or job-related negative or positive decision based on an individual’s online personality and presence, and through the use of non-professional details and information. This is just the beginning, and as various trends in HR and other sectors continue to evolve and much more is known and understood among the masses, more unseen challenges and issues can definitely be expected to come to the fore. These could be with regard to ethical aspects, biases, misinterpretation of analytics results, or related to understanding various principles of the concept.
Despite the many risks and grey areas, algorithm-driven approaches to recruiting seem to be here to stay. AI and ML integration and big data in recruitment may be adding momentum to slogans like ‘recruit smarter, not harder’ or ‘rise of the machine HRs,’ but a sizable number of individuals and forums insist that while big data drives hiring decision making, it should not be done without some amount of human engagement and intelligence.
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