Data – Collecting and Analysing Data

Data plays an increasingly important role in training and development. In this article, we explore the significance of data collection and analysis in shaping successful training programs.

In the digital age, training is not immune to the influence of big data. The abundance of information available about each user provides valuable insights for improving training methods.

Training departments can harness machine learning and data analysis to proactively support staff, moving from predictive to prescriptive models.

Three main types of data are vital in this process: personal data (age, gender, job title), company data (values, skills models), and data on learner habits (preferred module type, knowledge levels, etc.). By contextualising and interpreting this data in detail, training departments can create more engaging and personalised content.

Next-generation platforms, driven by AI, store and categorise a wide range of content, making it easily accessible to users.

These platforms also leverage data to suggest additional, relevant content tailored to individual learners, thus enhancing engagement and customisation.

Combining data analysis with the human factor, such as understanding each learner's unique personality, allows for the creation of original and highly personalised training experiences.

By drawing on profiles similar to learners, training programs can build collective momentum and integrate all participants into a comprehensive skills development project.

In summary, data-driven training is a powerful tool for creating more effective, engaging, and personalised learning experiences that meet the current and future needs of employees and organisations alike.