Risk management is quickly becoming a more data-reliant practice. The days of simplistic risk evaluation techniques are coming to an end. Rather, organizations will rely on more data-rich evaluations to drive risk ratings and response procedures. But that doesn’t mean you have to rely on artificial intelligence and machine learning.
An organization that effectively leverages technology to drive informed and timely action will have a better chance of survival, and risk managers and their teams will be looked to as the stewards of this critical initiative. It’s time for risk management functions to embrace this and leverage data to make better decisions.
Traditionally, risk management has always been about impact and velocity, and predictability. For Rick at Ceridian, it was important to know what’s going on in their business models at all times because any combination of risk could have an impact on their organization. They needed a way to report on those risks in real-time, so Ceridian built out the Onspring risk assessment process that factors in their unique risk categories and scoring methods.
The most important element in their risk assessment process is getting down to the exact amounts of data that provide better visibility. If you are unable to present appropriate data analytics to the right people, who can then take the right action, at the right time, then analytics will not help reduce risks. Focus on how you visualize your risk management data based on who you are speaking to and what you want to know.