This article is the fourth of a four part e-course.
I hope by now you’re becoming convinced that People Analytics offers an exciting wealth of opportunities for both HR and managers when it comes to understanding their employees.
This aspirational data themed destination is not so far away either!
Understanding how your initiatives are affecting your HR and business Key Performance Indicators (KPIs) is critical to evaluating their success – is all of the hard work and effort your team put in to launching that Perks programme really paying off and is it still worth the investment?
Here are 5 simple steps to help you begin making data-driven decisions:
The first step is to understand the objectives of your business - this will ensure HR speaks from the same playbook as the rest of the organisation. When you come to present your business case or investment proposal and eventually, your findings, to the board, alignment to the business objectives will help with positioning and will offer some linkage to why the business should continue to invest
You have two choices here – pick the biggest, toughest mountain to climb or endeavour to make marginal improvements across a number of areas. Both represent valid choices that will follow the same methodology outlined here, while the second option de-risks initially, it does do so at the cost of depth. I always recommend trying to strike a balance between the success rate of change and how much change you aim for.
Pick the metrics that are aligned most closely to the objectives you’ve chosen and the wider business objectives. Also establish the frequency that you wish to review the metrics – I’d suggest much more frequently than once per year – as a guide, compare your metrics to things like sales, quality and customer feedback.
Given the board responsibility of HR, you’ll have access to a myriad of people data points and it is important to try and isolate to 3-5 that are materially related to your objective – this helps to avoid what I like to call paralysis by analysis. These create relevant metrics and performance indicators that will impact directly on the organisational objectives.
Here are some of the HR metrics we have seen correlating with key business objectives in the past:
- Records of Early Turnover: A record of which employees are leaving in their first year.
- Time until Promotion: A measure of how long it takes (on average and per employee) for someone to be promoted.
- Performance Measure: The ‘nine-box grid’ method divides people into under-performers, valued specialists, emerging potentials or top talents.
- Turnover: A core metric for HR, a measure of workers leaving per year.
- Revenue per Employee: This is useful as a measure of the efficiency of the organisation as a whole.
- Engagement: arguably this metric is one of the most important, we’ve found correlations between engagement and most other HR metrics and it’s probably one of the most important for your organisation too.
Picking the metrics you think are most likely to affect a given organisational objective will give you a clear vision for how you are using employee performance to drive the organisation forward.
Hopefully you have in mind a couple of key business objectives for next year and what metrics you think will drive them - now it’s time to make sure they’re worth the cells they’re written in!
I know, a fair test can sound pretty formal but this is data analytics after all! Some of the other areas of the business that are very familiar, perhaps even regulated about the way in which data is used, (Finance for example), will quickly identify when the story has used ‘cherry picked’ data, rather than the insights from the data guiding the story.
For any metric to be a sound measure you need to be sure it is assessed in the same way for each person and is being assessed correctly - this is at the core of assessing performance fairly and accurately.
This is most directly relevant to metrics such as employee performance, potential or competency, as they may not even be measured or set by HR themselves. Operational managers should understand best what makes a high performing employee and the qualities and competencies they look for in their teams. It will also fall to them to regularly assess and report on their team’s performance. If there are discrepancies in how thoroughly these assessments are carried out or inaccuracies how you are assessing performance then it will create misleading data at a high level. It also affects transparency to your employees over what metrics they are being assessed around- which will directly impact engagement.
Investing the time to allow operational managers to influence the data that they collect about their teams will not only build a better relationship and more management buy in to the people analytics process, it will also make for more accurate and actionable data overall.
Finally it’s time for action! The set of initiatives you choose should be designed to impact the employee performance indicators you decided on earlier and should be informed by qualitative research, perhaps from the last engagement survey, from staff voice forums or ‘Innovation Hubs’.
Once they have launched, systematically tracking their adoption and any correlated increase in your performance indicators as a result of it’s launch will help direct your efforts and measure your success. Breaking down these results by business unit and position will help you understand where you’re seeing the best results and where your initiatives are struggling - this kind of demographic based understanding of your workforce is essential to having an effective people analytics strategy.
Diagnosing exactly what is causing the success or failure is a bit more involved, but again, will create a clearer picture of what works and doesn’t in your organisation when it comes to launching initiatives. Thinking in this way, with constant measurement and experimentation, helps ensure continued success in launching initiatives in the future, as well as helping you understand the motivators behind your employee’s behaviour.
The step’s to understanding your success can be broken down as follows, broadly speaking, this methodology will set you right when it comes to assessing any data set:
- Ensure data is clean and its quality can be trusted, and that you have given enough time and possess enough data points in general for this to be a worthwhile check (it’s never going to be perfect, but make sure you feel comfortable that you’ll be happy making a decision based on the information you have – if not, you may need to go in search of more data). The time needed will not always be short - as you may be soliciting a complex behaviour change and the amount of data points will come directly from the success of adoption.
- Assess a correlation between your actions and the intended results, be it positive or negative.
- If you find a correlation, make sure it exists when the data set is isolated over any demographics profile of interest, such as gender, business unit, location or position. If you can’t find a correlation make sure your results match across any demographics anyway. One particularly large business unit could pull your overall results either way.
- Begin to understand why your results show what they do. This is especially important when the data indicates you are right- as you must be sure it is not a circumstantial correlation. Correlation does not always point to causation.