The secrets to tapping into data to automate and streamline hiring of future leaders

Citation

Hipps, C. (2017), "The secrets to tapping into data to automate and streamline hiring of future leaders", Strategic HR Review, Vol. 16 No. 2, pp. 93-95. https://doi.org/10.1108/SHR-12-2016-0107

Recruiting is the perfect shop window for predictive analytics for recruiters who want to ensure they are hiring the best-quality candidates. After all, the market for top talent is highly competitive; hence, the pressure on recruiters to hire quickly and minimise costs per hire is an issue at the top of the minds of recruiting managers.

Getting a hire wrong is not only costly, but poor hiring can also lead to lower productivity, reduced levels of employee morale and engagement and ultimately more attrition. It is a vicious circle.

Predictive analytics are a crucial component of contemporary e-recruitment. They play a core role in helping to reduce reliance on the gut instinct of recruiters and hiring managers by enabling them to effectively utilise the plethora of recruiting data the business has already collected, e.g. data on high-, medium- and low-performing employees; candidate demographics, sources of hire and background data; assessment and psychometric data; structured interview data etc.

Knowing what worked well in the past can help to fine-tune the types of candidates that carry high favour within a firm. The benefits to recruitment include:

• saving recruiters time;

• getting to the top candidates first;

• finding a needle in a hay stack; and

• reducing bias and increasing diversity.

Research shows there is a need for this. Studies by the Social Mobility Commission have shown that numerous industries are failing to hire talented youngsters from less-advantaged backgrounds, because they recruit from a small pool of elite universities and hire those who fit in with the culture – still favouring middle- and higher-income candidates who come from a handful of the country’s top universities.

Furthermore, recent studies from Royal Holloway University of London and the University of Birmingham suggests that managers often select candidates for client-facing jobs who fit the “traditional” image of a role, with many placing as much importance on an individual’s speech, accent, dress and behaviour as on their skills and qualifications.

This introduces disadvantages for candidates whose upbringing and background means they are not aware of “opaque” city dress codes – for example, some senior investment bankers still consider it unacceptable for men to wear brown shoes with a business suit.

Top recruiters might receive over 150,000 applications a year, and rising from a mixture of core and non-core schools, they might not have time to sift fairly. Big Data can ease this pressure helping to instantly and automatically review all applications globally and flag up to 33 per cent of all candidates they will end up extending an offer to. In so doing, it is possible to free up significant amounts of recruiter resource each year – time which could be spent on adapting better engagement techniques to ensure a leading candidate with many offers at their disposal is more likely to buy into the culture, mission and vision of our clients ahead of market competitors with equally tempting offers on the table.

In the recruitment game, closing down top talent ahead of competition is a big challenge, and this technology is helping to offer a solution to this and reduce decline rates to suit corporate objectives.

Inevitably, algorithmic techniques like data mining can help to eliminate human biases from the decision-making process. But, crucially, any algorithm is only as good as the data it works with. To do this well, it is important to be self-critical when testing big data to ensure that you do not inherit the prejudices of prior decision-makers or reflect the widespread biases that persist in society at large.

A blog post by the White House staff captions this perfectly. It cautions: “The era of big data is full of risk. The algorithmic systems that turn data into information are not infallible – they rely on the imperfect inputs, logic, probability, and people who design them. Predictors of success can become barriers to entry; careful marketing can be rooted in stereotype. Without deliberate care, these innovations can easily hardwire discrimination, reinforce bias, and mask opportunity”.

So, how can a recruiter go about making big data a viable alternative for them to be a fairer way of accelerating a recruitment programme? The key is for HR and data/information officers to work together to devise a predictive system that works best for the organisation. At WCN, we advocate “Groupthink”-based algorithms as the best way to naturally reduce bias.

Having such collective thinking means “disparate treatment” or intentional/subconscious bias is removed as no candidate diversity data are fed in to start with, allowing a business to provide evidence that it is not intentionally discriminating against a candidate or applicant – or that it is not motivated by any protected category/associated neutral factors. Instead, the evidence will document the algorithms used to identify and quantify specific features that determine a candidate’s success.

This also has the potential to widen the spread of candidates and open up talent pools to diverse groups of talent, thereby avoiding challenges around elitism. The technology can automatically flag to a recruiter, candidates that have all the key indicators of success they are looking for, but that did not get a qualification from the likes of Oxbridge. All of this is done by simply using digital transformation to replicate collective decision-making, which in turn reduces the influence of bias by individuals or processes.

Harnessing the potential means you are not just dismissing elitism theories but you are also identifying and quantifying any historic bias, reducing the potential for new bias in future decision-making. It means you can mitigate the influence of disparate impact and focus on just winning great hires.

Throughout a continual trial-test-refine process of using big data, you should be able to identify ranges in which there is no significant statistical impact and go on to build better algorithms without disparate impact. By identifying and quantifying the features that determine a candidate’s success, you will be better able to quantify the disparate impact and correct the algorithm.

Reporting wise, it helps with ensuring that you are providing stronger evidence and record-keeping to support hiring decisions and can accept more applications with lower resource implications, and the automated cycle of recruitment means you should have a better talent pool of candidates coming through that, reflecting the future leaders you want joining your organisation. The benefits of this include:

• identifying and quantifying any historic bias, reducing bias in future decision making;

• focusing on what attributes make a great hire will help mitigate the influence of disparate impact;

• providing stronger evidence and record-keeping to support hiring decisions;

• identifying and hiring great candidates outside of your traditional sources; and

• accepting more applications with lower resource implications.

Inputted in smartly, your business will benefit from data insights that are proven to recommend candidates who unequivocally perform better and thereby deliver more revenue, profit and those who are likely to stay longer in the business. It means that a business can go on to use algorithms based on how employees perform in the business rather than what line managers decide at interview.

In summary, predictive recruitment analytics and big data intelligence tools are changing the way organisations view, analyse and harness their talent data. Leveraged efficiently, predictive analytics allows staffing teams to create economic value from their talent data, helping them become more competitive and successful.