Use and Misuse of Data in Workforce Science

January 29, 20211:43 pm541 views
Use and Misuse of Data in Workforce Science
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Data governance, privacy, and ethics are envisioned to become more pressing topics for companies in 2021. While the rewards of using data are great, so too are the risks connected with abusing, losing, and misusing data. 

The marriage of HR and analytics  

There is a widely-held notion that the best salespeople were extroverted because extroverts are typically able to communicate better. However, research shows that the biggest predictor for sales success is persistence – the courage to actually keep going despite initially being told no. This finding highlights a pivotal point in HR, where managers should no longer rely solely on gut instincts and common belief but turn to Big Data and analytics to hire, fire, and promote employees. 

The marriage of HR and analytics presents unprecedented opportunities for employers to gain a better understanding of how employees can function and operate to the fullest. Technology allows a cheaper approach for businesses to normalise HR teams relying on data, hence helping them to observe the right and wrong approaches to workforce science. 

See also: 3 Best Data Literacy Practices for HR

Starting points of workforce science 

Workforce science, or commonly known as people analytics, is referred to as hiring source analytics. HR teams recruit using a variety of different methods, such as referrals, web ads, career fairs, online job sites, etc. They also use various metrics to go back and analyse whether employees hired from certain web ads tend to perform better than peers who came from referrals. 

According to an article from SMB Group, workforce science helps businesses solve and improve talent ROI as it combines behavioural science with normative data, analytics, consulting and processes to determine systems of engagement. Workforce science also helps ensure that talent investments will pay off throughout the talent management life cycle that has been developed within organisations. 

Beware of the caveats  

Just like all technology and automation, data analytics also has its own flaws. Before managers jump on every shiny data science model for hiring, firing, and promoting, Peter Cappelli warned that hiring managers should have a full picture before implementing science into their recruitment process. Capelli said there is a case where software rejected every one of many good applicants for a job. This happened due to firms in question having specified that these candidates must have held a particular job title – one that existed at no other company. Consequently, overreliance on algorithms means that employers risk overlooking some factors in their decisions. 

There might also be collection errors which will cost employers good talents. Inaccurate algorithms can result in a company bringing in data it never meant to gather, endangering leaving businesses outside of compliance regulations. Moreover, depending on how each system is used by an employer, some datasets might be stored in locations where they are accessible to the wrong teams or users. In the case of selection tests, when HR intended to hire workers outside countries and the regulations in the intended country differs from regulation in the home country, the selection of workers might not be maximised due to misinterpretation of data. 

Workforce science is more relevant in some industries than others. A company that designs nuclear power plants, as an instance, is looking for incredibly specialised people and will get more value out of testing to find the right match than a firm where people join and leave daily. 

The danger of mis-assessing talents  

A film entitled Moneyball could teach us a lot about talent management. The film is about a famed General Manager of the Oakland Athletics baseball team, Billy Beane (played by Brad Pitt) and his literally game-changing approach to team management and strategy. 

Beane’s most significant struggle throughout Moneyball is dealing with the team’s coach, who overvalued traditional baseball skill sets and undervalue the synergistic potential of the budget players. The coach insisted each player play according to their designated role, rather than to the overall abilities they have. This approach eventually drives the team to the ground, and unfortunately, the approach affected more than just the play itself. 

Now, let’s assume HR managers assess individuals within their companies based on job descriptions – only bringing people that are matched with it, without taking into account abilities that candidates have that can be highly relevant to the organisation’s overall needs and goals. If this happens, there will be a greater likelihood that HR managers will bring candidates who are only good on their resumes but might not be good in the practices.  

While HR has put away the preconceived notions, it was only when HR managers embraced their new data-driven approach accompanied by a real test that the team was able to break forward. 

Read also: Looking for Job Change? Here are 5 HR Job Themes of the Future