Managing Data in the Face of a Black Swan Event

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I recently attended an energy trade and risk management conference in London and had the fortune of seeing first-hand the realities of our interconnected world and its effect on managing risk. The Tunisian and Egyptian governments had just fallen in the face of popular uprisings and Libya seemed to be on the verge of a civil war. Blackberries were buzzing full time all around me as companies wanted to know their exposure to this unexpected risk that was unfolding in North Africa and had the potential to spill over to the Middle East. Compiling this uncertainty, we are now faced with the prospect of a nuclear meltdown at several reactors following the catastrophic earthquake in Japan.

Four months ago these events were on nobody’s radar, and yet, as Nassim Taleb predicted in his book Black Swan, these events are becoming more common. This is forcing risk managers to create flexible systems capable of tracking the knock-on effect of these unpredictable and unavoidable events in real time. Even when these events are occurring, their downstream effects are not immediately obvious as witnessed during the Japanese earthquake. Before the earthquake occurred, the Nuclear power industry was enjoying a renaissance with tens of billions of dollars in new investment on the table. In the matter of a few days, and due to an event half way around the world, the viability of a whole industry is put into question.

I’m not sure that anyone could have prepared a company like General Electric for the tsunami that was about to strike it, but from the perspective of managing information flow and understanding the shifting risks tied to this information, there are things that can be done. With respect to data management, the answer is to simplify and automate. In a conglomerate like General Electric, with its huge semi-autonomous divisions ranging from building appliances to developing nuclear reactors, having a data management system that runs across all of these data silos is integral to the company’s ability to react quickly to changing economic conditions. Such systems mean that everyone in the organization works from a common data pool that is timely, correct and accurate. Simplifying the data collection through a single system also means that automated processes throughout the organization that are data intensive can be more easily manipulated to reflect changing circumstances. It also removes the worry that these processes across the organization are out of synch with each other and sending mixed messages to managers who are under pressure to act decisively in the face of one of these Black Swan events.

A company that is still managing its processes using spreadsheets will get crushed by a Black Swan event. There are too many factors to account for and the analysis is simply too complex to manage in a timely fashion. Moreover, the task of providing an audit trail from a pile of adjusted spreadsheets is virtually impossible. Automating these processes makes them much quicker, less prone to human error and more easily auditable should the underlying data or assumptions be changed. The ZEMA Forward Curve Manager provides a good example of how the management of critical information has evolved and will quickly be adopted throughout the industry.

Before the collapse of Lehman Brothers these Black Swan events were considered to be so statistically improbable that they were left out of risk calculations. As we have since learned, effectively dealing with them is a core pillar of corporate risk management and those companies that have simplified and centralized their data management will have risk managers who sleep much better at night.

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