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behaviour, data points need to be extremely multi- faceted. This is because there are so many factors that influence human behaviour and individuals will also be influenced to varying degrees by different factors. Simply put, the scope of human intricacies is enormous. HR teams are therefore right to tread warily and wait for early adopters to find appropriate solutions before they dip their toe in the water. When - and if - we can build models that are sophisticated enough, it’s also the case that most companies don’t have HR systems that are joined up, to allow them to collect the volume or scope of data needed to assure statistical relevance across more than just a few data points. It’s also an issue of size of business - obviously, the largest firms do have access to mountains of data from tens of thousands of employees, which can be used to identify behaviour patterns and inform predictions around staff churn, talent development and performance management. These patterns can then act as a highly useful pointer for HR teams in future planning terms. Even so, that data has to take employees’ specific personal circumstances into account, as well as the extrinsic and intrinsic factors that influence people behaviour. For example, research shows that, once you join a company, the relationship you have with your manager is crucial - but how do you plug that into the big-picture model? Predictive analytics is an evolving area and one that HR needs to watch, and, it’s potential is clear to see. But should HR use machine-based workforce predictions in isolation? No, not yet.
The need for humans in HR - now increasingly and paradoxically referred to as the ‘People Department’, as digital encroaches - is especially pertinent. People are complex beings - I’m sure readers will concur - and technology cannot yet claim to compute the many feelings, frustrations, goals, concerns and time-demands etc. that make up the modern, multi-dimensional human. HR professionals can figuratively put themselves in another person’s shoes though to gauge - at least to a certain extent - how that person might be feeling, or what they might want in terms of career progression, but machines do not possess that same ability, which is why humans will always remain central to people management. The importance of HR being led by humans is also amplified by GDPR, which has safeguards in place to protect EU nationals from “automated individual decision-making.” Therefore, ensuring that experienced HR professionals can work with data scientists, to both fact check and sanity check data results, is essential to promoting HR accuracy and relevance, and as HR Director Justine Brown corroborates: “Digital data cannot compute human complexities in their entirety and so it’s essential that we do not de-humanise the process”. Where HR can start to make a real difference is with personalisation technologies. These are now widely-used in marketing, across platforms such as, LinkedIn, and increasingly within Learning Management Systems. Here, the focus is on the
IT’S IMPORTANT TO QUESTION WHETHER
TRENDS IN THE PAST CAN BE TRUSTED AS A GUIDE TO THE FUTURE, ESPECIALLY WHEN YOU’LL NEVER HAVE THE COMPLETE PICTURE IN DATA TERMS. THERE ARE JUST TOO MANY VARIABLES TO GUARANTEE ACCURATE RESULTS
employee, looking at their past behaviour, current role or even future career plans to suggest activities that will enhance their personal life or promote their professional development. In this instance, all data points are specific to the individual employee, meaning that well-informed suggestions can be made. Examples include: Suggesting relevant training courses based on an interest expressed as part of a performance review, update to a personal profile, or planned or actual career move and automatically reconfiguring self-service screens based on what HR activities have the highest priority, or which are most likely to have the most positive impact for the business and the individual. Using predictive people analytics in this context is a much safer bet. It is far less risky than a company decision being made off the back of workforce-wide analytics, a scenario where it is far easier to miss the mark. In contrast, when basing predictions off one specific individual’s past behaviour and other key data points, the worst that can happen is that the employee is presented with an irrelevant suggestion, while the best is improved employee engagement and productivity. People will always be unpredictable so, with that in mind, the widespread adoption - and reliability of - predictive HR analytics is still a way off, none more so than in the SME environment. In the short term though, businesses can - and should - look to benefit from personalised predictive analytics that are tailored to the individual employee via historic data and career focus. Most often these will provide a useful platform from which to empower employees to learn and develop - a factor that will go a long way to driving business productivity and overall performance. In these instances where predictive HR analytics can currently be successfully applied, it is important that organisations first recognise what it is that they want to predict. After all, there is no point in investing huge amounts in taking on and developing staff, if you cannot tie this in with your business plans and five-to-ten year goals. Keep an open mind on the future potential of predictive HR analytics, view them through a pragmatic lens and never stop asking questions. As Dawn Klinghoffer, Microsoft’s HR Business Insights lead says in an article on rework: “It’s rare that we are ever “done” solving a problem - analyses can bring up new research questions, variables, and follow-ups that are important to understand”.
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MAY 2019 | thehrdirector | 45
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