Big data is currently transforming both the public and private sectors by increasing efficiency, transparency and productivity whilst also promoting sustainability. As the ability to utilise intelligent data analytics distinguishes today’s winners, data is fast becoming the oil of the 21st century. Organisations and countries that manage to harness this new commodity will ensure sustainable economic growth in the same way that those with access to cheap fossil fuel resources have been in an advantageous position in the past.
The proliferation of mobile technology, wireless sensors, social media and the Internet of Things, provides a means of monitoring socio-economic activity, consumption of resources, transactions, human mobility and environmental change. Recent advances in data science are now capable of coping with the technical challenges of collecting, managing and developing actionable insights from big data. Much of the exciting research has focused on addressing the technical challenges of dealing with the three V’s that define big data (volume, velocity and variety), which is growing at 40% per year (Figure 1). The sheer size and complexity of the data being created by internet devices (Figure 2) implies a need to move beyond simple linear models and embrace sophisticated modelling approaches. Many organisations sit on a treasure chest of data, which when combined with external data will offer enormous potential.
Measuring and monitoring the UN’s sustainable development goals will require better processes to utilise big data. The UN Statistical Commission has established a global working group to provide strategic vision, direction and coordination of a global programme on Big Data for official statistics. There are numerous challenges ahead that will require multidisciplinary teams to process raw data, extract insights and produce dashboards to enable intelligent decision-making. Fortunately, this revolution has already started in the insurance sector.
There are many contenders when it comes to identifying the most threatening global catastrophic risks. Over the centuries, epidemics, earthquakes, floods and windstorms have competed for the position of deadliest disaster. Those with the highest death tolls include the Black Death of 1348 that wiped out up to 60% of Europe’s population and the Spanish Influenza of 1918 that killed between 40 and 100 million people. The costliest catastrophe, with estimated economic losses now exceeding $235 billion, is the earthquake and tsunami that hit Tōhoku, Japan in 2011, resulting in meltdowns at the Fukushima nuclear power plant.
Reinsurance organizations quantify and compare catastrophic risks in terms of potential financial losses. Since 1987, when AIR Worldwide released the first catastrophe model, reinsurers have benefited from the scientific rigor of catastrophe models to assess risk. The financial losses associated with a particular peril are simulated by combining the hazard, exposure and vulnerability. While impact is clearly important, the frequency of catastrophic events must also be calculated to determine how to develop adequate risk management systems. Big data comprising historical events, crowd-sourced data and computer-simulated output form the ingredients of a CAT model. As the science matures and both practitioners and academics seek to cooperate, the growing need for a collaborative platform has emerged in the form of the Oasis Loss Modelling Framework (www.oasisimf.org).
There are many opportunities to use big data to improve the assessment and management of global catastrophic risks. At present, risk assessment is largely a backward looking exercise where a catalogue of historical extreme events form the basis of the analysis. In many cases, an assumption is made that the risk has not changed during the historical period. This approach is defensible if the hazard, exposure and vulnerability are not changing over time. In reality, all three can vary and both data and advanced modeling techniques are required to understand the complex interactions.
Emerging risks, such as terrorism, lack a historical catalogue and forward-looking predictive models are required. Natural disasters such as windstorm and flood are affected by climate change and overreliance on the past may underestimate future risk. Satellites and drones are helping to collect data to better understand exposure and vulnerability. Crowd-sourcing can also be used effectively to encourage people to build resilience to disasters and develop disaster risk management strategies.