90% of the world's data didn't exist two years ago (Mckinsey Analytics, How to win in the age of analytics, 2017). Technological advances and the lowering cost of computing makes data available to businesses like never before. Successful businesses are using this data to review business models and drive transformation.
However, risk managers confirm that they aren't making the most of the information surge. Many use the same information sources and statistical techniques to assess risk and purchase insurance that they have adopted for several years. Over 50% of Airmic members state that their use of data is limited, but an equal amount recognise that analytical literacy is a key competency for the modern risk manager, believing they will make more of data in the future (Airmic, A Profession in transformation, 2017).
Data is vital for effective risk management. There is an unprecedented level of uncertainty in business, meaning that 50% risk managers expect their risks to change significantly in the next two years (Aon, Global risk management survey, 2017). New sources of intelligence must be manipulated to understand and manage these changing risks. Indeed, Aon report that analytical techniques would give new insights into all ten of the 'top risks' highlighted by the same risk managers, from supply chain modelling, to predictive analytics of profit streams.
So why the lack of use? Data seems to be placed in the 'too big to handle' box. Risk managers cite a number of challenges from poor quality to a lack of understanding on their own part.
This paper will address each of the challenges and propose a model for data-driven decision making for enterprise risk management purposes.