Employees are a company’s greatest asset, but if the company gets hiring choices incorrect, employees might likewise be the company’s greatest expenditure. Appropriately, recruiting the right people and keeping and promoting the best, while identifying and resolving under-achievers, is crucial. Lots of companies invest a great deal of time and effort on personnels issues but do not have sufficiently in-depth data to help them fully comprehend their employees and the challenges that can affect workforce planning, development and productivity.
Big data analytics can help to address these challenges, which explains why a growing number of HR departments are turning to them for a variety of purposes, for example, to: (i) identify prospective recruits; (ii) measure expenses per hire and return on financial investment; (iii) measure employee efficiency; (iv) determine the impact of HR programs on performance; (v) identify (and forecast) attrition possible leaders. Supporters also argue that big data analytics can help to supply evidence to de-bunk commonly held assumptions about employees that are wrong and based on biases.
Appropriately, the use of analytics assures many potential benefits for organizations, not only in terms of making improvements in talent identification and recruitment, but also in terms of workforce management. However, the use of data analytics in the HR sphere likewise raises some particular threats and obstacles that companies have to think about, including increased direct exposure to discrimination claims, breaches of privacy law and reputational/brand damage. In this article, we will discuss some of the key factors companies need to bear in mind.
What Is Big Data?
Organizations have always accumulated information but, in this digital age, the amount of data being generated and kept is growing exponentially. IBM has calculated that 90 per cent of the digital data that exists today was created in the last two years. In addition, traditionally, organizations may not have had the ability to draw value from the data that they held, particularly where such data were unstructured (and Gartner Inc. estimates that roughly 80 per cent of all business data is disorganized). However, new technologies now enable the analysis of large, complex and rapidly changing data sets comprised of structured, semi-structured or unstructured data. In short,” big data” is just data. It’s simply that we have more of it and we can do more with it.
I. Recruitment
Organizations are using big data analytics, for example, to identify candidates with the right skills and experience. New talent management systems can help organizations quickly search and analyze huge volumes of applicant data, e.g., using principles, not just key words. Organizations are also using analytics to analyze working with data to help make changes in hiring strategy and recruitment collateral to attract more candidates and minimize attrition. There are two key stages that need to be considered in handling legal compliance with respect to these activities. First, there is the collection of data, and second, there is the analysis of the data and the formulation of resulting choices.
A. Collecting and Processing Personal Data for Big Data Analytics
In regards to the collection of data, companies are increasingly mining candidate data from online sources, including job sites and social media sites, for the purpose of talent identification and recruitment. Privacy concerns loom large because information collected about a proposed candidate will be considered individual data and may even contain sensitive personal information (e.g., health data, ethnic origin and sexual preference).
B. Preventing Discriminatory Impact
Of course, as with all talent identification and recruitment activities, organizations also need to ensure that they do not act in a manner that could be considered discriminatory. In Europe, Directive 2000/78/EC establishes a general framework for equal treatment in employment and occupation, forbidding discrimination based on religion, belief, disability, age and sexual preference. Different directives also forbid discrimination on the grounds of sex and race. The concept of equivalent treatment implies that there should be no direct or indirect discrimination on any of these grounds.
In terms of an organization’s analysis of the data collected, again it will need to ensure that its analysis and the decisions that it makes as a result of such analysis are not deemed discriminatory– in particular decisions that are based on predictive decision-making about candidates. Of course, it is essential that organizations do not blindly accept data without challenge. Given the size of the possible data pool, conclusions may well be based upon correlations, rather than being determinative. Proper interpretation and assessment of the results of a big data exercise is essential. For example, organizations should be wary of any predictive decision-making that gives results that appear skewed in favor of certain types of candidates. For example, if a big data analytics work out brings up a short list of potential prospects that have the same race, gender or other characteristic, that might recommend that there has actually been a discriminatory input eventually in the big data process. Although it may be tough for a candidate to develop that a big data analytical exercise has actually been discriminatory, particularly provided the possibly complicated algorithmic calculations involved and lack of transparency about those algorithms, organizations have to bear in mind the dangers. In some cases, if a practice is identified to have a discriminatory effect, the concern might move back to the employer to safeguard its methodology. Employers might also be needed to disclose comprehensive information about their big data methodologies in case of employment litigation or a government examination. As a result, employers will want to be prepared to explain and, if necessary, justify their big data analytics methods.
C. Third-Party Rights
However, it is not just a question of compliance with privacy and HR issues because mining data from 3rd party sites, such as online task websites, could be a breach of their regards to use and, potentially, an infringement of copyright rights. Web scraping might also be thought about a breach of applicable local cybersecurity laws that prohibit unauthorized access to computer system systems (e.g., the U.K. Computer Abuse Act 1990 and the united state Computer Fraud and Abuse Act). Accordingly, organizations need to ensure that they have adequately resolved all possible legal dangers prior to starting any data collection activities.
II. Workforce Management
The 2nd area where analytics are being increasingly harnessed by HR departments includes the monitoring and analysis of data relating to workers. Again, this use of analytics throws up some particular problems that business have to understand.
Lots of organizations already use analytics to get insights into their customers and target customers. Organizations are now seeking to acquire the same understandings into their staff members, which they can use to improve organizational effectiveness and drive performance. This can help organizations to objectively evaluate their existing individuals management practices. Of course, if HR is going to end up being a more data-driven department, it will have to recognize what data it hangs on its employees and whether such data merely have to be joined up or more data have to be collected.
The collection of more data is most likely to include increased monitoring of workers. The applicable policies relating to such monitoring vary across the world and, for that reason, if a company is rolling out an HR analytics project, it will certainly need to deal with monitoring and data collection on a country-by-country basis.
Conclusion
Big data analytics may provide HR departments the capability to make better, more unbiased, data-driven decisions about recruitment and staff members. Nevertheless, the value of a big data project will certainly depend quite on the quality of the inputs and project specifications and the mindful analysis of the results. HR departments will certainly have to have appropriate analysis internally or hire appropriate service providers to help them design the proper big data program and interpret the resulting data. Of course, if a company makes use of a third-party company for the provision of HR big data innovation and analytics services, there will certainly be other legal issues it will certainly need to consider, in particular in respect to commercial plans (e.g., many HR analytics suppliers made available analytics on the basis of cloud-based Software as a Service) and copyright rights and data ownership.
