By Megan Schlosser, Associate Director, Risk and David Stebbing, Senior Director, Strategic Risk Consulting at WTW
In this article, based on a webinar from our popular Outsmarting Uncertainty series, we offer practical insights to help risk managers modernize their approach by leading on better data use. We provide guidance and useful checklists on:
What business leaders need to know about bad data strategy
How risk managers can identify good data
The business benefits of supplementing your data
How to evaluate the quality of analytics providers
What business leaders need to know about bad data strategy
Leading the charge on re-wiring your data strategy may need to begin with communicating the drawbacks of sticking with an approach that’s not fit-for-purpose. Poor data strategies can have significant and far-reaching consequences. Bad data can mean you overstate risk. The business may then be unnecessarily cautious. It could also face increased costs through paying too much for insurance; where insurers face gaps in risk data, they tend to make assumptions that err on the side of caution, making for higher premiums.
Conversely, if you understate risk based on inaccurate data, you leave the business more vulnerable to losses and business failure. Let’s say, your flood risk data on a property wasn’t accurate, you based your cyber insurance limits on bad data or your valuations for business interruption purposes were unknowingly low. You could face underinsurance, shortfalls when it comes to funding recovery and having to watch competitors bounce back better from events impacting your industry. Getting a better grip on data is about value.
Flawed risk data means you might retain risks you should have transferred or walk away from emerging opportunities that could have paid off. If you allocate resources based on flawed data, your organization risks wasting resources on low-priority areas while neglecting high-priority risks, moves that could define its enduring success or ultimate failure. AI can play a role here, taking poorly structured data out of spreadsheets and organizing it for ingestion and easier evaluation.
How risk managers can identify good data
Your ability to identify good data and robust data management is crucial for delivering more effective, modernized risk management.
This checklist can help you evaluate the quality of your data, the internal actions you can take to improve it and where you may need to look for external support to fill gaps:
Is your data stored in maintainable formats?
If it hasn’t done so already, your organization should move away from using PDFs or other static, non-editable formats. You need data management systems that allow for easy updating and ready manipulation. Well-structured data sets are what facilitate real-time analytics, continuous improvement in risk management and more informed decision making.
Is your data consistent?
Good data is in a standardized format. The way information is collected and processed is consistent; all data points are comparable and use consistent units of measurement, date formats and data entry protocols. Let’s say you’re collecting data on property values. Taking a consistent approach may mean ensuring all values are in the same currency or that all locations are recorded in terms of longitude and latitude, rather than street address.
Are you capturing detailed information about new losses?
Up-to-date loss data will enable you to spot and respond to risk patterns and trends. You can identify loss drivers more quickly and develop more effective risk mitigation strategies.
Are you continuously improving your data quality?
Good data isn’t a one-time effort but a continuous process. Your organization needs processes for regularly reviewing and updating your data to ensure it remains accurate and relevant. This includes filling in data gaps, correcting errors and incorporating new data sources, such as information on new losses or changes in valuations.
Do you know what you don’t know?
Good data strategy means being able to recognize when you need to go beyond the business to refine your approach. Collaborating with specialist teams.
For example, forensic accountants can connect you to more accurate and nuanced risk data; you can assess the value of a property more accurately by considering details on property condition and historical performance.
The business benefits of supplementing your data
Supplementing your own data is another way to deliver more refined risk management. Integrating external data sources lets you fill gaps where your own data can’t give you the full picture. Let’s say you’re reviewing your cyber insurance but haven’t had a loss for many years. Using external industry data, cyber loss forecasting and scenarios can help you make an informed decision that avoids over or understating the cyber risks most likely to impact your industry and business.
External data sources can also give you fresh perspectives through industry benchmarks or economic indicators. These could provide valuable context for your risk recommendations to business leaders. Supplementing your data with external sources can also enhance the accuracy and reliability of your risk models, particularly important for complex risks best managed with a multifaceted approach, such as amplified and inter-connected exposures due to climate risks.
How to evaluate the quality of analytics providers
If you’re considering working with external data and analytics providers, you’ll need to be confident on the accuracy and efficacy of their own approach to data. Things you’ll want to look out for are:
Evidence of their deep understanding of your industry-specific risks and transparency over how it knows what it knows. You’ll ideally need insight that’s relevant to your industry, specific to your business and capable of offering a holistic view of your risk portfolio. And if an analytics provider offers you this, can the provider also connect you to transparent processes and real-time access to information?
Case studies of businesses the provider has helped to refine data strategy and risk management. A high-quality analytics provider should have a track record of delivering consistent, high-quality data output. Ask them to tell you about the businesses in your industry it’s supported and what was the demonstrable impact of its analytical solution.
Processes that connect your data with theirs and help business leaders see the ‘wood from the trees.’ Ask about systems integration and how your prospective analytics provider plans to make their data insights accessible and actionable. Where there’s a wealth of data and potential takeaways, how will it help you present the insights that matter on generating more value to business leaders?
Want to learn from businesses rewiring their digital strategy to realize more value? Watch the full Outsmarting Uncertainty webinar. Your ability to drive more value from data lies in the quality and structure of the data itself, as well as the tools and processes you use to analyze and act on it. Can we help? Get in touch with our risk and analytics specialists.
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