Enterprise Risk Management (ERM) software manages the processes and the risk related data that drive risk behaviour, including Risks, Controls, Issues and Actions, Incidents, Key Risk Indicators (KRIs), Audit Findings, Compliance Obligations, Risk Control Self-Assessment (RCSA), Compliance Questions, and Compliance Attestations to name a few.
Tracking this data helps an organisation manage risk through improved knowledge and understanding. A good ERM framework will provide Business Intelligence (BI) analytics to visualise this data to enable business users to make better decisions.The analytics should cover all levels of the organisation vertically from strategic, through to tactical and operational-level decision making.
The BI capability should also integrate the concepts horizontally into visualisations that clearly show the relationships between the different risk management drivers, for example, the number of controls for each risk.
Visualising how these drivers change with time is crucial to a better understanding of risk, which is now possible with the introduction of “Historical Models” for Protecht.ERM.
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Matthew joined Protecht in 2017, as the Manager of Business Intelligence and Analytics having extensive experience in senior roles within government and private organisations delivering large transformation projects, providing hands-on expertise and thought leadership in data and software. He graduated with a PhD in Applied Mathematics in 2001 from the University of Wollongong and more recently with an Executive MBA in 2008 from RMIT.