There are multiple sources of risk data available covering the full spectrum of economic, environmental, social, geopolitical and operational risk categories. These can take the form of raw data points, or can be data points already transformed by other analytical tools designed for specific functions, industries or categories of risk.
Each data point may also have been individually analysed against a set of criteria to determine that there is a risk, but these are merely indicators of risk. They do not determine that a risk has manifested, or even that it may do so. A single indicator by itself can be proven benign or confirmed as having reached a failure state, only when all other dependencies are considered.
It’s a data problem. It’s a big data problem.
The problem has been that very few data points look connected. Only by making the connections and interpreting them, do we see the likelihood of risk realization. To put them into context requires analysis. To analyze the volume of data to determine business risk requires automation.
MeasuredRisk harvests both raw and transformed data points from multiple disparate sources. These data points are connected by networking key risk indicators and mapping them to business specific criteria. By distilling this data using statistical modelling and presenting it in context, business leaders can [see risk] in a way that enables key business decisions to be made.