Many say People Analytics, but in reality, they are “only” doing Reporting.
The difference lies in the level of maturity of the data-driven solutions developed by HR functions.
Often, these differences are not related to the quality and availability of data or the tools used, but rather to the HR professional’s ability to ask valuable questions.
In our approach, we emphasize that the use of People Analytics and Reporting solutions aims to bring a significant improvement in the quality of HR decisions, in the value of HR services, and in the value HR can deliver to the business.
What do we mean by Reporting?
By People Analytics we mean the concise and graphical representation of how a phenomenon may evolve over time.
The reference database and statistical processing determine different levels of complexity and the overall value of Analytics.
Once again, both the database and the statistical processing are determined by the question HR asks.
We can distinguish two types of People Analytics:
- predictive
- prescriptive
Predictive analytics is based on a stable predictive model with high confidence. In other words, it requires working with a historical dataset across multiple dimensions, statistically processed to provide a forecast of how the phenomenon under analysis will evolve. This prediction must then be validated.
An example of predictive people analytics is the development of an analytical model capable of predicting engagement levels in relation to variations in other variables linked to a change management action, a modification in total reward policies, or welfare services.
Prescriptive analytics is based on a predictive model but also indicates which dimensions should be acted upon to achieve a desired state described by the model’s values.
An example of prescriptive people analytics is identifying how to adjust total reward values to reach a target engagement level within a specific population.
In both cases, HR asks a question about the future, aiming to anticipate the evolution of a phenomenon.
In predictive analytics, the question seeks insights into what may happen.
In prescriptive analytics, the question aims to identify the actions most likely to make the desired outcome happen.
In our example, the two questions could be:
- What will be the engagement level of our population if we introduce a new smart working framework?
- Which smart working model can generate the highest level of engagement?
Conclusions
In addition to avoiding common mistakes, there are two key points to highlight:
- the importance of the question HR asks in developing a Data-Driven solution; if the question has a strategic perspective, the greater the value of the solution developed — especially for business stakeholders
- do not underestimate reporting; the value it can generate lies in improving decision quality through data-driven evidence, reducing biases linked to personal experience or other organizational dynamics