The phrase ‘forewarned is forearmed’ came from the 16th century – a little bit before HR departments existed – still, it’s excellent advice for progressive leaders, who must try to predict the future and anticipate business needs. Planning and forecasting are valuable tools in the HR armoury, as failure to anticipate upcoming peaks in demand inevitably results in missed opportunities, dissatisfied customers and an excessive burden on valuable team members.

A failure to predict changing customer requirements can result in costly excess capacity, or lost business as the organisation does not have the skills to meet new needs. Spotting those trends and changes early buys valuable time, enabling the business to move faster than it could have done on a purely reactive basis. Historically, forecasting the future could be achieved through formal planning processes, research and dialogue with colleagues and customers. Historic trends could broadly be trusted to anticipate the future, supplemented with discussion at board level and beyond, about how those trends might evolve and how the business should change to keep up. The pace of change is one of the significant changes companies of any scale have had to adapt to over the last decade or so. In today’s global, connected world, where new technologies and competitors can emerge from anywhere and change the game fast, HR leaders must increasingly be able to see around corners and be prepared to adapt faster than ever before. In this environment, predictive analytics – a branch of data science that uses historical data, statistical algorithms and machine learning techniques to forecast future events or outcomes – is becoming an increasingly important tool. It gives progressive organisations and HR leaders an even sharper edge in today’s rapidly changing world. In many ways, predictive analytics mimics best-practice approaches used by experts for decades. The enhancement here is in the speed and timeliness of the analysis, the ability to trawl through vast amounts of data for the most predictive signals and the ability to replicate that approach time and again.

HR is a data-rich environment – perhaps second only to finance – and the ability to blend multiple data sources adds a wealth of insight to help predict the future. There are three key areas where predictive analysis consistently delivering value in HR and the first of these is workforce planning. By drawing on multiple data sources across a business, organisations successfully use predictive analytics to forecast their growth trajectory and ensure sufficient headcount and skills to meet demand. Fundamentally, this comes down to having a clear and consistent understanding of the people and skills required to meet future commitments and this helps HR leaders understand what is needed from a supply perspective. Hiring risk is a significant consideration here and so by understanding which positions are currently open, how long they have been open, how long it typically takes to fill such a role and the availability of suitable talent in the pipeline, hiring teams can predict how likely they are to be able to onboard enough of the right kind of talent in the appropriate timeframe and whether alternative approaches need to be considered. Suppose an organisation has always been able to recruit sufficient people with a specific skillset – within the required timeframe, using tried-and trusted approaches – and that there are plenty of good candidates in the market. In that case, resourcing specialists can focus on the more challenging recruitment tasks.

The power of predictive analytics here lies not in replacing humans, but in augmenting their ability to understand what is happening in their organisation and what is most likely to occur in the future. Therefore, it is important for humans to assist AI and machine learning, when making significant decisions, rather than allowing the technologies to do it alone. Keeping humans in the loop at key moments is crucial for the ethical use of predictive analytics. Analytics can do the heavy lifting of sifting through vast amounts of data and understanding which signals are most relevant. This then provides the most accurate forecast and a clear and compelling picture of the different patterns and trends in play. A second area where progressive HR organisations gain tremendous value from predictive analytics, is understanding and mitigating potential disruptions to a business created by employee attrition. Predictive analytics are now widely used to forecast employee turnover and uncover its underlying drivers. Part of the challenge here is understanding the problem areas. Analytical models can identify and surface the departments or cost centres in which attrition is a problem and point to the most likely contributing or correlating factors, such as hiring methods, department performance, compensation or diversity issues. Predictive models draw leaders’ attention to particular teams, departments or locations in which the number of leavers is higher than expected and identify employees who are at an increased risk of leaving the business. With this insight, HR leaders can have conversations to uncover further information and explore how to remediate the situation. The power of prediction can go further still, after understanding the most common reasons people leave and the factors that correlate most strongly with churn, some HR teams map that understanding on to the existing employee base, to identify talent at significant risk of leaving. An understanding of the top drivers of retention, can inform action points, such as an alternative assignment or upskilling, to pre-empt people leaving the business.

One of the significant challenges in understanding skills at scale is identifying them in the available datasets. Humans are good at identifying skills on a CV and matching them to a job description, but there is a limit to the number of resumes that even a highly-skilled practitioner can review. At any scale, it is costly and time consuming and processes are prone to bias and other errors. Natural language processing, a branch of AI that gives computers the ability to understand text and speech similarly to how humans do, can identify skills in a résumé or a job description and also filter those skills based on context. This provides valuable insights automatically, saves a huge amount of time and enables HR leaders to focus on more strategic tasks such as identifying relevant skills. This makes it possible for organisations to quickly understand the skills portfolio they have in their workforce and identify where skills gaps exist. It makes it much easier to predict the individuals with skills best suited to a specific job, or to find the right training course or mentor to help an employee close the skills gap for their next career opportunity. Integrating predictive analytics into HR practices is revolutionising how organisations manage their workforce, by providing a more comprehensive understanding of what has happened and what might happen in the future. Human beings can then respond faster and more appropriately to the accelerated pace of change, making better-informed decisions.

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