Artificial intelligence, machine learning and process automation are accelerating the development of customised and adaptive training frameworks. 96% of large and medium-sized companies already integrate learning management systems into their skills management policies.
The abundance of data and clear indicators, however, raises a critical challenge. L&D departments struggle to anticipate latent needs, trigger the right measures, and adjust training actions in real time. What do we mean by predictive analysis, and what is its impact on managing skills development?
What is predictive analysis?
Often associated with data science and Big Data, predictive analysis is based on the growing volume of your company’s digital data. It uses information processing algorithms to develop scenarios for change and assess the likelihood of them occurring. You then have several levers for forward planning to support informed decision-making, including for the continuous training of talent.
Definition and basic principles
Predictive analysis refers to the in-depth study of data to predict certain events. It is based on:
- Data mining;
- Machine learning;
- Statistical data analysis.
This predictive approach identifies relationships between several variables through modelling. In the context of human resources, predictive analysis can help extract and classify information related to human capital. You can then identify patterns, inconsistencies and correlations in order to make informed decisions.
Technologies and methods used
HR analytics tools provide a solid foundation for developing predictability across the various aspects of skills development.
They make it possible to map skills, anticipate employee needs, evaluate training, and adjust training actions in real time… 56% of HR managers also highlight the urgency of adopting these technological solutions to better manage human capital.
There are several types of predictive models:
- Decision trees to better understand an employee’s performance;
- The linear model, which links, for example, job performance to experience or training;
- The classification model to link an observation to a specific category in the context of recruitment;
- Artificial neural networks for deep learning of complex data…
The analysis of predictive models also relies on data visualisation solutions. In the context of continuous team improvement, you can use a skills tracking tool to better represent the results of the analyses.
Why use predictive analysis in business?
Human resources departments use predictive modelling to quickly identify trends emerging from their company data. This may relate to talent satisfaction and engagement, salary expectations, the quality of communication between teams…
The learning and development department identifies risks and opportunities in advance, enabling it to stay one step ahead in skills development.
According to DARES, 439,600 resignations from permanent contracts were recorded in mainland France in the fourth quarter of 2024. Employees often show subtle warning signs before they decide to leave, signs that should never be ignored.
With predictive analysis, your managers can anticipate this situation and act as quickly as possible to retain employees. Certain data may, for example, reflect dissatisfaction or difficulties in carrying out tasks. Using the right predictive model then enables the team member concerned to develop their skills or benefit from an internal mobility programme.
React quickly, decide better: smart alerts serving skills development
The data processed by predictive models supports decision-making through smart alerts. Integrating them into your skills management policy enables you to respond in real time to signs of employee disengagement.
Identifying weak signals through data
In predictive analysis, a weak signal refers to early information that is difficult to detect and signals an emerging issue or trend.
A slight increase in absenteeism in an industrial company can, for example, indicate a deteriorating social climate that could lead to strike action. Such a situation hampers knowledge transfer, especially in the absence of a repository of unrecorded skills.
Smart alerts highlight these signals, enabling managers to take targeted action.
Triggering the right measures to support skills development
Even with the right data in hand, predictive analysis presents two major challenges: identifying the right profiles to use it, and acting quickly before the information becomes obsolete. This is where smart alerts come into their own. Generated through upstream automated processing, they provide clear visualisations that enable intervention at the right time, with the right levers:
- Adjusting the balance between working hours and financial compensation;
- Tailoring the training plan;
- Supporting career progression with development opportunities…
Alerts, when combined with HR indicators, become powerful levers for managing and accelerating your employees’ skills development.
Adjust training actions in real time to maximise effectiveness
Skills development takes time, and therefore requires regular monitoring. Every talent in the company should feel supported in this process. Smart alerts leverage each predictive model in your analysis system to adjust your teams’ development plans: coaching, mentoring, additional training…
They are based on precise and relevant indicators to guide your talents’ learning and ensure effective follow-up after training.