What is “resilience”?
The dictionary defines “Resilience” as “an ability to recover from, or adjust easily to, misfortune or change.” Based on many years of clinical research, a team of neuroscientists developed an innovative code to deliver insights into human resilience and how it can drive high performance teams within the corporate realm. The client approached Matogen Applied Insights (MAI) to engineer an integrated data system to process detailed information about subjects’ behaviours, attitudes, emotional and cognitive states. This data was previously captured and analysed in a disconnected and manual fashion.
Analysis and recommendations
MAI analysed historical data, and devised a data-driven expert-validated model utilising the client domain expertise, i.e. “the neuroscience expert system”. This model not only measures resilience, but also identifies the most impactful actions towards improvement. The model was used to build a software application that provides users with commendations on areas in which they are already performing well and recommendations for most impactful improvement.
Model stability and segmentation
In order to ensure that the population on which the model was built matches the observed population, the monitoring phase of the project includes calculating the Population Stability Index (PSI) which measures the discrepancy between the expected and actual populations.
In addition, the MAI data scientist applied collaborative filtering to examine the relationships between the variables, including the Resilience Index. Collaborative filtering is an advanced statistical technique that simultaneously assesses similarities between respondents and constructs to detect relationships. It is a method often used in recommender systems such as Netflix. MAI has also successfully incorporated this method for a major listed South African telecoms company for its campaigns. In this project, collaborative filtering clearly identified four clear segments within the data. Finally, linear regression was conducted which delivered the outstanding insight that five constructs could explain almost 70% of the outcome under scrutiny!
Model monitoring
In this ongoing project, the information captured in the neuroscience expert system and analytics engine are updated according to the ever-widening academic knowledge base, which, in addition to on-going monitoring, ensures the accuracy of the predictive resilience model.
