Despite significant investments in cutting-edge analytics capabilities within the talent management and acquisition disciplines, many analytics functions aren’t providing the returns and advocacy expected. According to a recent Gartner survey on the state of talent analytics, only 40 per cent of senior leaders seek out HR data when making business decisions. Furthermore, according to the same survey, less than one-quarter (23 per cent) of heads of talent analytics believe leaders are effective at using talent data to inform their business decisions.
Given the potential confusion towards talent analytics, there is a growing need to re-evaluate how to deliver value in this area. To help you prepare, Gartner have made the following four predictions as to how talent analytics might evolve over the next three to five years.
Understanding employee experience will top the analytics agenda
Organisations are becoming increasingly attentive to more holistic notions of employee experience: Gartner data shows that 70 per cent of employees expect their workplace to provide systems and communications that better anticipate and understand their needs. As such, employee experience assessments are likely to rise to the top of the talent analytics’ agenda. Talent analytics teams are uniquely positioned to help HR interpret what experiences employees value most, given their unrivalled ability to compile and sift through diverse sets of data, run extensive analyses, leverage new technologies and draw out insights.
There are numerous opportunities for analytics teams to gather such data, ranging from surveys and internal forums such as town halls and focus groups, to social media and behaviour-monitoring technologies their organisations employ. Spotting patterns in this “employee voice data” is where analytics functions can thrive, by identifying recurring moments of employee frustration, or common topics of discussion among workers.
The delivery of data will matter just as much as the analysis itself
Talent analytics clients are often key decision-makers, who have little time or attention to spare, and who expect technologies to offer the same effortless experience at work, as they do in their personal lives away from the desk. It is vital that talent analytics teams refine their data delivery in order to match these demands and enable decision-makers at every level. A recent Gartner survey of analytics leaders suggests that the majority of teams recognise a growing need to invest in easy-to-use and accessible products – 94 per cent of talent analytics teams say they are currently investing or plan to invest in self-service platforms. However, innovations such as real-time dashboards do not necessarily make the data easier to consume or understand. Relevance and timeliness are the hallmarks of effortless delivery. In order to maximise the relevance of the information they provide, talent analytics’ must recognise that decision-makers often struggle to identify the most relevant points among vast quantities of data. To this end, analytics function must “constrain” what they provide, delivering insights – not essays – to business leaders.
Talent analytics will play a vital role in balancing risk aversion
Talent analytics teams must be acutely aware of the decision-making and management contexts in which they are received. Leaders are often called upon to make decisions with little information and uncertain outcomes – decisions which may significantly impact the short- and long-term prospects of their business. Within this context, decision makers may over-rely on what they perceive as “safe bets.” However, avoiding risk entirely can hamstring businesses when faced with disruptive innovation. Talent analytics can help leaders mitigate their risk aversion, by providing data-oriented recommendations to help highlight opportunities and disperse this uncertainty. Aiding a client’s decision-making process requires some intelligent parsing of their needs and wants. Data and insights leads must ascertain what kinds of information would provide the biggest boost in confidence for decision-makers and their shareholders; vital, too, is defining what approval or buy-in clients require before moving forward with big decisions. Only by delivering data against these specifications can analytics teams secure trust and generate recommendations of real, tangible use. Of course, leaders may still resist recommendations, instead deciding to follow data more aligned to their “gut feeling”. In such cases, data and insights leads must probe why their client mistrust their recommendations, identifying their underlying assumptions and misconceptions. Clients may be won over if you are able to provide comparable data from other business units that dispels these concerns. Furthermore, such data may even be able to quantify the impact of leaders’ resistance to insights, evidencing how much money or talent the organisation might lose if leaders rely solely on their instinct.
Experimentation will be the sustainable path to value
The business environment of today is changing at an unprecedented pace. As such, Gartner predicts that rapid experimentation with new data and projects will be required in order to create a more sustainable path to driving business value. Small and frequent periods of experimentation provide talent analytics teams with the means to identify value-generating projects at an early-stage. This not only boosts their competitive value, but enables effective resourcing by only backing projects proven to have measurable impact. To get the most out of experimentation, talent analytics teams must prioritise results, designing experiments that reveal value earlier — and being prepared to abort experiments more quickly if they do not yield good results. Furthermore, data and insights teams must recognise that failure will be a part of this process. Setting clear thresholds for an experiment’s success and calculating aggressive but attainable short-term targets that represent a meaningful business impact are critical. As is, holding the team accountable to these targets, which ensures that when experiments do fail, they fail fast and early in the process. It is vital that analytics leaders recognise such experiments are to determine whether an initiative will create value – not whether it will be a panacea for the entire suite of client woes. The most effective instances of experimentation will be targeted at yielding solutions to specific problems in specific areas of the business, where value waits to be generated.
Interested in HR Analytics? We recommend the Mission Critical HR Analytics Summit 2019.