1. Business understanding
Understand project objectives and requirements from a business perspective.
This phase begins with agreeing the health and safety challenge(s), the approach for integrating the insights from the project into the business, defining the operating environment and determining the available assets and finally setting an “analytics plan” to achieve these outcomes.
2. Data understanding
Collect data and then become familiar with it (quality and initial insights).
The data extracts from the data assets are gathered. This typically includes: safety claims, incidents and observations, operational data, HR information, work site data, production data and other external data sources (e.g. geospatial socio demographics).
3. Data preparation
Construct modelling data by aggregating, manipulating and joining.
We integrate and manipulate your business data with our own external data sources to create an integrated data set that is ready for analysis.
Choose and apply various analytics modelling techniques.
During this phase we apply powerful statistical techniques in order to discover and explain relevant relationships between safety outcomes and operational metrics. Combining traditional safety data with non-traditional sources (e.g. Census data) can lead to predictions about where accidents are most likely to happen, under what circumstances, and to which segments of the workforce—all before they actually happen.
Evaluate analysis in the context of the business issues being addressed.
The model results are validated using statistical validation techniques. This phase typically involves a series of interactive workshops with the business to explore and contextualise the analytical findings. Furthermore, the application of advanced cost optimisation modelling can help facilitate an objective assessment of the relative benefits of different safety spending options.
Organise, present and deliver the insights in a way the business can use it.
The output from all previous phases is of little value if nothing is done with it. This phase covers the change management and best practices to build buy-in for predictive analytics to help bridge the gap between building analytical models and real world outcomes, including the monitoring requirements to drive proactive engagement across the organisation.