Risk assessment is a system we constructed over the last generation of government and industry working together, to standardize and drive out risks from various activities we do. It involves a combination of risk assessment, risk management, and risk communication, and is all about probabilities and causal pathways. At one level, we do not need anything that is digital to make that system work. However, as the digital systems start to gather more data, different data, and faster data, it complicates and challenges our regulatory systems that are at the heart of these risk analysis enterprises.
It challenges them to figure out how to manage with the data flow, and bring meaning to data - data in and of itself is just noise, and sometimes, more information can just swamp a system, causing us to miss the critical variables. Sometimes, more information can deliver subtle little changes that allow us to find cause and effect relationships or impacts that we had not previously been able to detect. And so the challenge within regulatory systems is that they are fairly blunt instruments, which are created by governments, and populated by people who have standard training, and a lot of experience.
They are also very stylized places where we organize and manage our decisions. Having a sort of firehose of data flooding these systems, scares a lot of the regulators because they are uncertain how to deal with information or data that has no meaning attached to it.
The big data world offers great opportunities for further driving out risks in our economies and societies, but it also runs the risk of swamping the system so that we get paralysis.
Big data, risk assessment and consumer trust
The question here is whether the trust problem is a lack of real time credible data. At one level, more diverse, more timely, and more granular data, could be very useful for building trust with people. However, the big challenge will be the filtering process, when we are inundated with reams of data and multitudes of interpretations of that data. How does an average person make discrete choice of buying or not buying, or choosing between two products to buy or consume, because they need the products as part of their lifestyle and their livelihood? So there is this creative tension between more and better data helping to build confidence, trust, and better relationships between consumer interests and the production system, and the reality that people have scarce attention, and are not quite sure how to deal with information that they have not seen before and that comes from disparate sources and interests.