In high-risk sectors such as electricity transmission and distribution, data science has a growing role to play in preventing harm before it happens. Utilities have long relied on incident reports and injury rates to understand safety performance, but these measures only tell part of the story. They show what has already gone wrong. The bigger opportunity lies in using data to spot weakness earlier, strengthen controls and support better operational decisions.

This shift is particularly important in grid operations, where safety, reliability and system performance are closely connected. High-voltage environments leave little room for error, and small weaknesses in planning, training, inspection or reporting can have serious consequences. For data scientists, this creates a clear challenge: how can different sources of safety and operational data be combined to identify risk before it becomes an incident?

A useful starting point is the distinction between lagging and leading indicators. Lagging indicators, such as injury rates, lost time incidents and days away from work, remain important for compliance and trend analysis. They help organisations understand where harm has occurred and whether performance is improving over time. But on their own, they are retrospective. They are the rear-view mirror.

Leading indicators look further upstream. These might include near-miss reports, safety audits, inspection completion rates, job hazard analyses, training participation, hazard observations and worker engagement in safety processes. When tracked consistently, these measures can help organisations understand whether the behaviours and controls that prevent incidents are actually happening in practice.

This is where data science becomes especially valuable. Safety data becomes far more useful when it is connected to operational context. A near-miss report, for example, may reveal more when viewed alongside crew schedules, weather conditions, asset type, location, training records or workload patterns. Inspection data may show emerging risk when compared across regions, teams or equipment groups. Training records may highlight gaps that would otherwise remain hidden until something goes wrong.

Technology is accelerating this move from manual reporting to earlier intervention. Safety management systems, field reporting tools and operational dashboards can bring together data from inspections, audits, incident reports and workforce activity. With the right analysis, these systems can surface patterns that might be missed in traditional reporting, such as repeated hazards in particular locations, weather-related driving risks, gaps in refresher training or differences in safety reporting between teams.

The value is not simply in producing more dashboards. The real value lies in decision support. Better data can help safety teams target coaching, adjust dispatch rules, prioritise inspections, redesign work processes or make the case for investment in staffing, fatigue management and grid modernisation. In other words, analytics can help move safety from a reporting exercise to an active management discipline.

There is also an important cultural point. Metrics shape behaviour. If organisations focus only on achieving “zero injuries”, they may unintentionally discourage people from reporting hazards or near misses. A stronger approach is to measure and reward the actions that support prevention, such as speaking up about risks, completing safety checks, taking part in safety committees and following through on corrective actions. For data scientists, this is a reminder that measurement design matters. The wrong metric can make a system look safer than it really is.

For utilities, the move towards predictive safety analytics reflects a broader change in how operational risk is managed. Safety is no longer just something to be reviewed after an incident. It can be monitored, modelled and improved continuously. By combining leading and lagging indicators with operational data, grid operators can build a more detailed picture of where risks are emerging and what interventions are likely to make a difference.

For data science professionals, this is a powerful example of analytics being used in a setting where decisions have immediate human consequences. The aim is not prediction for its own sake. It is better prevention, better accountability and safer operations.

References

https://www.powerinfotoday.com/insights/safety-performance-metrics-guiding-grid-operations/

https://www.safetyiq.com/blog/safety-metrics-leading-vs-lagging-indicators