Competing With Data & Analytics
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If you see the phrase “black swan” and immediately think of either a large bird or the psychological thriller starring Natalie Portman, you probably are not in the business of managing organizational risk.
To a risk manager, “black swan” phenomena are highly unlikely events that have massive impacts on a business or society on the rare occasions they occur. The Fukushima disaster in March 2011, brought on by a deadly combination of a powerful earthquake at sea and resultant tsunami, is a sterling example of such a phenomenon. The reactor meltdown that followed was an outcome that some argue could (and should) have been prevented with better planning.
New research suggests that by exploiting many types of data, managers can help prevent (or at least contain) the damage related to black swan events and other risky blind spots. The caveat: organizations should rely less on management experience and intuition and rely more on integrated data to point to potential risks.
How data can help risk managers is the topic of a recent paper, Managing Risks with Data, by Ron Kenett, who is a visiting professor at the University of Ljubljana, Slovenia and holds research positions at the University of Turin, Italy, and and NYU’s Polytechnic School of Engineering.
Kenett suggests that the proper exploitation of organizational data can help prevent some of those hugely disruptive, largely unexpected events. In practical terms, that involves acquiring and merging data, as well as building data-driven risk management decision-support systems that complement and reinforce the more traditional methods used today. According to Kenett:
Risk management is traditionally practiced using subjective assessments and scenario based impact analysis. This common approach is based on experts providing their opinions and is relatively easy to implement …. Modern evidence based management relies however on data, and not only opinions, for achieving effectiveness and efficiency. In that context, risk management can exploit information from structured quantitative sources (numerical data) and semantic unstructured sources (e.g. text, voice or video recordings) for driving risk assessment and risk mitigation strategies.
In his research, Kenett lays out a maturity ladder of risk-management practices:
- Intuitive – no formal methods used.
- Qualitative – risk assessments are based on expert opinions
- Quantitative – some data is collected and used to derive Key Risk Indicators.