HR teams are under pressure to produce insights from employee data, but if they’re not clear about where that data comes from and why, they could end up alienating staff and cause projects to fail. Alistair Shepherd explains.
HR analytics – the collection and analysis of data on employees – offers a genuine opportunity to understand our workforces better.
It helps us to see not only trends and sentiment but also get a better understanding of how work actually gets done and where productivity comes from in the messy day to day.
This can make HR more effective than ever at improving the way work gets done and, hopefully, gives it a credible role in the business aside from “just a cost function”.
However, it’s likely that most HR analytics projects will fail, either because they are too invasive of individual‘s privacy or more likely they’re purely voyeuristic exercises and don’t provide a “so what”.
In both cases the solution to making HR analytics succeed is in being clear about what the aim of the project is, how success will be measured and clearly communicating the benefit of the exercise to the workforce – it is their data after all.
What about privacy?
Let’s start by addressing the privacy concern. The misuse of personal data is becoming a regular headline that means employees are increasingly aware of how their digital footprints can be used.
Most of the time the use of our personal data by corporations or governments is in our interest so we’re amenable to the idea. But occasionally it is used to limit our freedoms.
The South China Morning Post recently reported that over 17 million people in China have been restricted from purchasing a plane ticket because their social credit score (maintained by the Chinese government) is too low.
Even if you’re a model citizen your data can be used to subconsciously influence your opinions without you even realising it, as demonstrated in 2016 during the US election and the UK referendum on Brexit.
Because of these high-profile stories, it becomes easy to see how employees might fear that data held by employers could be used to make decisions about career progression, development opportunities or even salary.
Fearing this, many employees will be reluctant to engage in data collection activities or analytics projects. Since analytics relies on data, HR analytics often relies on the willingness of the employee base to be measured or monitored.
The good news is that a good number of HR departments already understand the risks associated with collecting employee data.
Deloitte’s 2018 Human Capital Trends Report found that more than 50% of their survey respondents (senior HR leaders) are “actively managing the risk of employee perceptions of personal data use and a similar proportion is managing the risk of legal liability, yet only a quarter are managing the impact on their consumer brand”.
This means that organisations considering broad scale HR analytics projects need to develop a set of well-defined policies, security safeguards, transparency measures and ongoing communication around the use of people data or risk employee, customer and societal backlash.
People analytics opportunities on Personnel Today
The second and arguably more common reason that HR analytics projects fail is that they’re voyeuristic exercises with no clear objective or reason to exist beyond just being able to see data. And if there’s no objective, how can we succeed?
In 2016 Deloitte reported that most (77%) of HR professionals felt that people analytics were important, but only 32% felt ready or somewhat ready to implement them.
The real question is, important for what? Being “insight-led” doesn’t count as a reason. Being “data driven” shouldn’t be the primary purpose to launch an HR analytics centre of excellence.
The question should really be, what are our (HR’s) current objectives? What big questions are we trying to answer? Analytics projects shouldn’t be carried out for their own purposes, they should be a prerequisite for achieving a bigger objective.
Once you have a clear objective (for example, to understand why some managers get high performance out of their team while others don’t) then you need to construct a hypothesis.
A hypothesis is your guess at the reason(s) behind the phenomenon. Maybe your guess is that the high performing managers have regular one-to-ones with their team and the low performing managers don’t.
The goal of your analytics project should be to collect data that can prove or disprove this hypothesis.
Business and employee needs
To be most useful, analytics projects should connect to a business need. Even better, connect to an employee need.
Then, HR must clearly communicate the benefit to the end user – in the example above that benefit might be communicated as helping managers develop their people skills. Even better than communicating the benefit to employees, is to put them in control of realising that benefit.
Given that most HR analytics projects will be looking at people problems, very often you’ll be looking at changing the way people behave.
So one of the questions that needs to be answered in order to avoid the voyeur trap is:
If the data confirms my hypothesis, am I in a position to act on it?
At Saberr we worked with an international technology firm who were going through a big change programme after a change in senior leadership.
It identified that one of the barriers to change was the fact that not all managers were communicating strategy to their direct reports – which had a knock-on effect of teams lower in the business being unclear on their objectives. Identifying where the issues were enabled managers to get more than 20% improvement in team performance after six months.
Giving employees not just the accountability but also the autonomy to solve problems themselves makes analytics projects far more likely to succeed – and less of a burden for HR.
Does it really benefit employees?
Of course, not all projects will directly benefit employees. An example of this is one study that suggested a link between commute distance and retention whereby the further you have to commute to work, the more likely you are to leave.
Acting on this data might mean prioritising recruitment activities in the local area or offering relocation benefits. Neither of which clearly benefit employees directly.
However, the principles of connecting to a business need and clearly and transparently communicating why this data is helpful to the company should remain as key principles.
It’s not uncommon to hear that implementing HR data analytics is a daunting prospect.
To avoid the issues with both privacy and voyeurism the easiest way to ensure the success of your HR analytics project is to clearly define the benefit and communicate this to all stakeholders involved.
If the benefit is solely for the HR function – that is fine – but employees will engage in the process far more readily if they understand why it’s important and how it will help improve the company.