Unlocking The Power Of Actionable Data: Moving From Reactive To Predictive

We all want it.  We all say we need it.  And we all think we have it.  But what is “actionable data”?

For most in the risk management community, “actionable data” is what we get from analyzing the claims data received from the claims system of our carrier or administrator. The data is often used for underwriting and pricing rather than learning and trending. 

As Risk Managers, we are experts in the implications of claims. We use financial and statistical data to identify the frequency and severity of different types of claims, therefore recognizing which types of claims should be our focus in addressing. What we get is a scorecard of lagging indicators and results – a report of “what happened?” and “what are the consequences?” We analyze this data and react. 

But what about the “why” and “how” of each incident? What caused the incident? And how could it have been prevented

The ability to tally our missteps (and near-missteps) with losses is valuable. However, simply reporting our results to operations and management yields no guarantee of reduced claims and an improved bottom line.

We need to move away from lagging data and toward leading and predictive data. This data is “actionable” because it not only identifies our weaknesses, but also specific steps to improve in those areas.  It enables us to influence the data, rather than reacting to results. 

Even the best data analytics cannot help identify causes and predict future issues if the raw data of each incident is not collected. Risk managers have access to this data – the behaviors and circumstances associated with each event – when the claims system is integrated with the incident management system. The incident management system records the event, collecting all relevant data, not just claims handling data, and reports it to the claims system. The claims system is then used for measuring, evaluating, and reporting both specific and aggregate events. 

For instance, to know our trucks are hitting parked cars tells us what happened.  However, unless we capture the reason the cars are being hit (e.g. narrow streets or driver distraction), we cannot take the steps necessary to prevent such future losses. 

Actionable data results when we correlate root cause analysis with claims experience. 

Using the right integrated incident management and claims system, you will get actionable data and help you to predict and prevent future incidents, ultimately improving your company’s health, safety and risk performance as well as your bottom line.

Richard Rabs

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