Last month OrgVue 2.22 was released. From a sleeker interface, faster data entry to powerful new dashboards, 2.22 brings better performance and usability. All of these new features were designed to help organisations deep dive into their people data and drive actions from insights faster. You can read the full release note here.
OrgVue 2.22 introduces two new dashboards, Summary Statistics and Numerical Analysis. This blog will focus on the latter and its use cases.
Why is Numerical Analysis so Powerful?
Numerical analysis gives you a flexible and intuitive way of slicing and dicing people data by multiple measures and dimensions, instead of just one at a time. This feature is extremely powerful in workforce planning.
Below are three ways in which OrgVue’s Numerical Analysis can transform the way you do workforce planning. To illustrate these points I have generated some people data in OrgVue for fictional Company X.
- Speed and ease in doing succession planning
Succession planning is a key component of workforce planning, especially since we are experiencing an ageing global workforce. A recent report by Eurostat estimated that the median age in the European Union has increased by 0.3 years per year in the last ten years. Since ageing will affect the whole organisation, it is not sufficient to focus planning only at the top, more so for operational businesses which have many people working on the ground.
OrgVue’s Numerical Analysis has been designed to make holistic modelling of an entire organisation easy. Users can slice and dice data, and visualise it in a creative way to gain fast insights.
For example, Figure 1 visualises the current age distribution of Company X. Using Numerical Dashboard, we can easily model how the distribution will change in the next 10 years based on the ageing trend reported by Eurostat. As shown by Figure 2, on average, the age distribution will shift towards becoming older with ca. 45 % of the employees being potentially older than 40.
How does age demographic shift impact an organisation? It will need to adjust its people recruitment and training programs accordingly. To do this effectively, it needs a way to visualise, track, and analyse the change in workforce distribution, their gaps in skills, experiences, and competencies.
Organisations that can attract and retain a well-balanced, multi-generational workforce will benefit from their diverse skillsets and talents. For example, young employees may be more proficient in using technology and social media, while more mature professionals may share their accumulated years of industry experience. More importantly, knowledge and skills transfer need to bypass seniority and gender differences. This is where Numerical Analysis dashboard becomes powerful. Users can slice people data by average age/gender and depth at once and easily spot if seniority or gender inequality is an issue in the organisation.
For example, the age distribution by depth in Company X is fairly even across the organisation (Figure 3). As visualised, the workforce is relatively young with employees in their 20s present across all levels of the organisation and 30–40 year olds accounting for roughly 40 % of the total headcount. We could say that Company X seem to have a healthy recruitment and promotion process. But of course, we need to be very careful when drawing conclusions from broad statistical measures. Read my colleague’s blog on ‘The Gender Gap and Statistical Bear Traps’.
- Quick insight to gender parity in the workforce
With Numerical Analysis, users can also split age by gender to assess the parity of representation in the workforce in greater detail. For example, in the dashboard created for Company X below, we can see that there are more and on average older male employees than female employees in the workforce (Figure 4). This type of insight should spark questions and advance internal discussions amongst managers.
- Internal benchmarking of workforce data at local and regional levels.
Numerical Analysis dashboard also allows users to breakdown people data by location (see Figure 5). Company X has three main sites in the UK — London (Head Office), Manchester and Birmingham — which make up for 80 % of the workforce. By comparing workforce data across the three locations in a single view, users can perform internal benchmarking at a local and regional level. In this case, the female employees in Manchester are much younger on average compared to the other two sites. Coupled with root cause analysis, this insight can facilitate overall improvement. For example, the CHRO of Company X could look into why the local office in Manchester is better at encouraging young women to join the company.
Those are the three ways in which OrgVue’s Numerical Analysis can help users get an insightful snapshot of their workforce quickly. It makes it easy for analysts to dive into the workforce numbers, categories and test their hypothesis to drive improvements in a more agile way.
Want to see what OrgVue can do for your organisation? Request a demo here!