According to an Airmic report from summer 2017, “More than a third of risk managers (35%) say that their use of analytics tools today is limited, but within three years, more than half of risk managers (56%) expect to be using data analytics extensively in their roles.” Ventiv is committed to helping risk managers move from the status quo—limited use of analytics tools—into an analytics-centric future. That’s why we partnered with Aon Risk Solutions to produce a white paper called “Driving the Data Dividend. Making Use of Analytics in Risk Management.” Produced specially for Airmic members, we’re delighted to make this paper available to a wider audience.
“Driving the Data Dividend” addresses the main challenges to making the best use of data in a risk management setting and proposes a model for data-driven decision making. By no means is it the only resource you’ll want to consult as you chart an analytics-oriented future; however, we think this paper is a useful contribution to a crucial discussion.
Following are summaries of the eight chapters in “Driving the Data Dividend.” Have a read and, if you like what you see, we invite you to read the whole paper and learn how leading organizations are using data to drive transformation.
Chapter 1: Introduction
Risk management has, from its inception, been a data-driven enterprise. Yet we’re in unique historical moment: First, the volume of data has exploded in recent years. Second, the cost and capabilities of analytics tools have never been more attractive for businesses of virtually any size. So why are risk managers not making the most of the opportunities now before them? This chapter sets the stage for challenges and solutions addressed in the rest of “Driving the Data Dividend. Making Use of Analytics in Risk Management.”
Chapter 2: Data and analytics—an overview
This chapter covers, in brief, what big data is as well as the challenges in utilizing it. We also offer a definition of data science and the challenges in practicing it. With these ubiquitous terms defined, we’re ready to move on to the practice and potential of analytics.
Chapter 3: Data analytics—a maturing discipline
No doubt, you and your organization are well versed in reporting on claims and business trends. But what else is possible? This chapter covers the range of analytics possibilities, from descriptive analytics to prescriptive analytics.
Chapter 4: Risk data and the business
It’s not always easy to visualize how risk data can be used across an organization, from operational management to senior management all the way up to the board level. This chapter provides a useful graphic model of the kinds of risk data captured (the “input”), who uses them and how, and the purpose (or “output”).
Chapter 5: Barriers to data use
Without doubt, there’s widespread excitement over the emerging uses of analytics. Yet, barriers to making the best use of data remain. This chapter covers some of the key barriers and presents ways in which organizations have successfully overcome them.
Chapter 6: Data-driven decision making
What is data-driven decision making? We define it as gathering relevant data and using analysis and evaluation to inform risk management, risk financing and business strategy. This chapter covers the concept of looking at numerous sets of data and how to discover new relationships between those data sets. See page 11 especially for a graphic representation of a data-driven decision-making model.
Chapter 7: The risks
From GDPR to cyber risk, there are risks to embracing data-driven decision making. This chapter introduces you to the thinking behind managing those risks.
Chapter 8: The future
Risk managers will need to educate themselves not only on how to approach data-driven decision-making but also the technological developments that will increasingly dictate where data comes from and how it is used. Read and study “Driving the Data Dividend. Making Use of Analytics in Risk Management.” Then keep reading and studying other resources. You’ll be on the right path.
Sep 12, 2018
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