Advances in technology have made data storage economical and convenient. The vastness of available data may make it useable well beyond the purpose of its original collection. However, its vastness may also make harnessing it a challenge. Robust data sets and data sharing across sectors coupled with collaboration in data mining techniques can yield new insights into risk factors and build solutions for risk management and mitigation.
Clear and specific data use guidelines are needed to assist both the public and the private sectors in establishing and navigating data management processes. Often the private sector has not allocated capital to certain risks simply because there is insufficient data to quantify the risk. While data can provide significant insights into risk and drive solution innovation, there is often a reluctance to share data but that may be overcome if the deliverables and the associated benefits are clearly identified for potential users and the public.
When underlying data is incomplete or incorrect the reasonableness of quantitative solutions becomes more uncertain and consideration should be given about the reasonableness of the output. Another challenge when accessing public entity data is the protection of privacy. Personal information can be easily parsed from “big data” in advance of a quantitative exercise. Understanding the entities’ confidentiality requirements is a fiduciary responsibility before any analytical undertaking.
These considerations should be seen as challenges rather than impediments. The value of combining data inventories is immeasurable when addressing questions of risk mitigation through land use management, potential for pre-loss risk transfer and post-loss funding.