We all need to find our black swans because they may find you first.
The theory of the black swan dates back to 16th century London as a statement of impossibility. Back in the 16th century there was an assumption that all swans must be white because all swans identified by the known world at that time were white. Then the famous Dutch explorer, Willem de Vlamingh discovered black swans in Australia in 1697. All of a sudden what seemed impossible was now a reality. Yet, in reality it was purely a lack of information that meant that everyone thought there were only white swans.
Black swans are now synonymous with the significant economic and business events of the last 10 years. Bubble bursts in technology, subprime mortgages, global financial crisis and sovereign debt events were all once regarded as impossible. The processes and systems in place in organisations meant these events could never happen. People associated their risk management with an inability to fail and regarded their controls as beyond failure. Growth charts were considered the risk management charts for all organisations, and nothing would ever occur to change this view. Now, in a world full of uncertainty and an understanding that the black swan of tomorrow is just around the corner, organisations are facing into the key dilema. How do we find these “black swans”?
Here at the Innovation of Risk we have discussed black swans and the tools that could assist in identifying and then managing these potential disasters in past posts. One of our most read articles ever on the Innovation of Risk was on “Finding operational risk data and having it work for you“. In this article we specifically discussed the digital technologies that could be utilised to search for the “black swans”.
Since writing that article we are starting to better understand the value of internal loss data and external loss data. In an risk context, internal loss data refers to the events that have occurred in your organisation that you have recorded in a respective risk system. These events could be events that have resulted in a financial loss or they could be near misses that did not actually result in a loss but had the potential to cause a financial and/or reputation loss. External loss data is the broader piece of events, but rather than only being internal events, they are events that have impacted organisations within your industry or across the globe.
But what is even more important is not just finding this data but considering the data in the context of organisational design, the external environment, business strategy, business processes, people, technology and regulatory environment. Organisations across the globe already record both internal and external event loss data. And many organisations use this information in management and board reporting. Sections in reporting on the external environment are typical. But the key question that needs to be answered is “how does this help us find these black swans?”.
In order to answer this question we must first consider the DIKW hierarchy. The DIKW hierarchy is a model for considering the data within your organisation and how that is translated to information. From there information is in turn translated into knowledge. But knowledge has no value unless you can translate that into wisdom. This is ultimately how organisations will identify those black swans and turn them into opportunities, rather than events that adversely impact the organisation.
In the words of Albert Einstein, “Wisdom is not a product of schooling but of the lifelong attempt to acquire it.” And just like the intelligent formulas and physics principles that Einstein uncovered, this statement is the key to unlocking what you have in the DIKW hierarchy and finding those black swans.
Organisations need to continue to reassess, change, evolve and grow in how they not only capture internal and external loss data, but most importantly in how they use this data within the organisation.
Today it may be reading an article on cyberespionage as highlighted in Risknewstand and realising that you have always thought that could never happen to you. Realising this is the first step in identifying one of your black swans, and this is an early warning sign that you need to take that information and assess it against your current risks and controls. From here, you can convert that information to knowledge about your options, and then find individuals within your organisation that will convert that to wisdom which will result in you inventing a new technology or closing a gap in your existing processes.
From a Risk Managers perspective this is dream come true! However, interestingly from a risk management perspective, it also highlights the difficulty in quantifying the value you bring to an organisation, as although you may have reduced a potential risk and then loss, you can never really quantify that loss! But that is another post altogether!
There is no silver bullet for finding and utilising internal and external loss event data. However there is no excuse in 2013 to not take the time to consider what you are doing within your organisation to take the wealth of data, both internally and externally (both at an industry level and globally) that exists, and to then convert that through the DIKW hierarchy from data, to information, to knowledge and then to wisdom.
There is no-one out there who wants to be the organisation or person that everyone refers to in the future as missing their black swan. The consequences of being in such a situation can result in tragic and organisation ending consequences. As Albert Einstein said, “Wisdom is not a product of schooling but of the lifelong attempt to acquire it.” Never for one moment should anyone ever think they have achieved that lifelong goal, because the moment you reach what you think is the pinnacle the world throws another curve ball from somewhere unexpected.
It is the unexpected that made the English realise that white swans were not the only colour of swans on this planet and we all need to find our black swans because they may find us first.
