Ventiv Technology

6 Data driven-decision making Data driven-decision making involves a risk manager gathering relevant data and using analysis and evaluation to inform risk management, risk financing and business strategy. Insurers and brokers are beginning to take the leap by exploring new sources of data such as machinery sensors and telematics and using automated decision making when quoting to improve accuracy. Risk managers must follow these trends, e.g. by looking at the numerous sets of data available to them and discovering new relationships between sets if they want to keep up. However, there is no doubt that there are internal challenges. the figure to the right summarises the actions and key questions risk managers should take to combat these obstacles. '' The key to using risk data effectively is to start small and keep it simple and focused. Big data should be used to highlight small changes that could enhance existing business processes, e.g. board risk reporting, resulting in measurable improvements to overall quality and insight. Risk managers will need to demonstrate the cost-benefit of any changes using interactive and visual messaging to get the support of senior leadership. By starting small and building on success, the case for broader adoption and investment into analytics will become easier." PHILIP SONGHURST-THONET HEAD OF RISK CONSULTING, AON RISK SOLUTIONS A DATA DRIVEN-DRIVEN DECISION-MAKING MODEL MONITOR, REVIEW AND IMPROVE TIGHTLY DEFINE THE PROBLEM, IN A BUSINESS CONTEXT DETERMINE THE PERFORMANCE INDICATORS IDENTIFY THE DATA SOURCES AND INTEGRATION METHODS ANALYSE AND MODEL GAIN INSIGHTS AND TAKE ACTIONS » Understand recent business and environmental changes, identifying the risk(s) to review » Break the business into its component parts » Identify the process or area where risk management actions may improve results » Consider how risk management proposals will be reported to internal and external stakeholders » Agree the theory to test and the decisions to be made » Collaborate with teams working in the areas of the business under review » Create a list of KPIs and KRIs that would inform the hypothesis being tested and decisions to be taken » Identify the variables that drive the KPIs and KRIs » Weigh up the relative significance of each variable to inform priorities » Prepare a list of data to be collected and analysed » Review the relevant data already held within the business and where » Identify the new data sources to be collected and how » Decide the common themes, categories and field names to connect multiple data sets » Establish the infrastructure which will hold multiple data pools and » facilitate their interaction » Collect data across multiple areas of the business » Check data quality, identifying missing or inaccurate information » Establish why and how existing data has been collected, ensuring any collection bias is understood » Connect data sets through established common taxonomy and collection timelines » Work with data analytics / data science teams to find trends or patterns in data sources » Overlay data sets onto business processes to spot relationships and correlations » Use descriptive and predicitive analytical tools to establish why an outcome has happened and what will happen in the future » Don't jump to conclusions or take the output from mathmatical models at face value » Evaluate results in workshops with relevant teams, applying common sense to the analysis » Review analysis against business processes to improve risk identification, assessment, management and loss control » Identify potential business process and cultural changes DRIVING THE DATA DIVIDE | 8

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