Big data is undoubtedly one of the hottest topics in business and technology today. Everywhere you turn there are discussions about the potential of big data and analytics to transform businesses and whole industries.
Big data is undoubtedly one of the hottest topics in business and technology today. Everywhere you turn there are discussions about the potential of big data and analytics to transform businesses and whole industries. SMA’s new research report, Big Data in Insurance: Insurer Priorities, Projects, and Investment Plans for 2014 and Beyond, confirms that big data is really taking off in the insurance industry. From a big picture view, big data is indeed gaining momentum in insurance, with both P&C and L&A insurers investing more and expecting major benefits. Twenty-five percent of insurers are now investing in big data, up from just 9% in 2012.
Opportunities and Plans
What is the result of all this activity? What kind of business problems and projects are insurers pursuing with big data approaches? There are no quick and easy answers to these questions because there are all sorts of projects underway in many parts of the business. It is not just a few uses cases, like pricing or claims fraud. There are at least 40-50 different types of problems that are being addressed with big data – or at least insurers have high expectations that big data will address these problems in a new way. There are the projects that you would expect – for catastrophe modeling and improving risk selection, underwriting, and pricing. But there are also many new projects that are designed to understand more about customers and producers in order to improve sales and service. Gaining new insights that enable segmentation of the distribution channels? – Check. Understanding what kinds of coverages and product features customers value? – Check. Determining how billing patterns affect renewals? – Check. There are dozens of these kinds of issues that are critical to business results. So critical, in fact, that it is worth seeking new ways to mine internal and external data to uncover trends, patterns, and opportunities.
Challenges and Success Factors
As a relatively new set of capabilities, big data comes with big challenges for insurers. The top business challenge identified by insurers is determining the business value. There are a wide variety of potential business cases across the enterprise, but building a business case is difficult in many situations since the whole approach is new. The top two technology challenges related to big data are managing the variety of data and maintaining the veracity of the data. Surprisingly, the volume of data is not considered to be a top issue. You’d think that big data is all about huge volumes of data – and it is. But insurers believe that identifying, managing, and combining all the different sources of structured and unstructured data is the most challenging technology issue. Veracity is high on the list too. The garbage-in, garbage-out rule still applies here. For some big data uses cases (especially regarding social media data), the data can be less precise and still be useful in spotting big trends. However, for applications in insurance, the data must still represent the truth. Most insurance uses of big data require that the data be reasonably accurate and complete, and some, such as pricing, require a high level of precision.
The primary factors for big data success are not related to the data, or the technology platforms, although both are vitally important. For all the gee whiz technology and the amazing stories in the press about game-changing results, success still comes down to people. The opportunities and potential for big data are vast, and we are just at the dawning of the big data era. There are so many traditional and new data sources to leverage, and so many high powered technology platforms to choose from. But success comes down to having talented people, who can ask the right questions, determine the business value, design the model or analytic approach, and interpret the results. And last but not least, determine how to put the results into action. For more on the organizational, people, and technology implications of big data, read SMA’s companion report, Big Data in Insurance: Emerging Trends in Organization and Technology.
For a complete review of big data projects and investments through 2016, read SMA’s new research report, Big Data in Insurance: Insurer Priorities, Projects, and Investment Plans for 2014 and Beyond.
Mark Breading, a Partner and Chief Research Officer at Strategy Meets Action, is a recognized expert in advanced technologies and their implications for the insurance industry. His specialty areas include the customer experience and analytics. He has exceptional knowledge and experience in many dimensions of the customer/producer experience, including omni-channel, customer communications, mobile technologies, CRM, digital content management, and contact centers. He has broad experience and insights in data and analytics, including business intelligence, advanced analytics, and big data.
Opportunities and Plans
What is the result of all this activity? What kind of business problems and projects are insurers pursuing with big data approaches? There are no quick and easy answers to these questions because there are all sorts of projects underway in many parts of the business. It is not just a few uses cases, like pricing or claims fraud. There are at least 40-50 different types of problems that are being addressed with big data – or at least insurers have high expectations that big data will address these problems in a new way. There are the projects that you would expect – for catastrophe modeling and improving risk selection, underwriting, and pricing. But there are also many new projects that are designed to understand more about customers and producers in order to improve sales and service. Gaining new insights that enable segmentation of the distribution channels? – Check. Understanding what kinds of coverages and product features customers value? – Check. Determining how billing patterns affect renewals? – Check. There are dozens of these kinds of issues that are critical to business results. So critical, in fact, that it is worth seeking new ways to mine internal and external data to uncover trends, patterns, and opportunities.
Challenges and Success Factors
As a relatively new set of capabilities, big data comes with big challenges for insurers. The top business challenge identified by insurers is determining the business value. There are a wide variety of potential business cases across the enterprise, but building a business case is difficult in many situations since the whole approach is new. The top two technology challenges related to big data are managing the variety of data and maintaining the veracity of the data. Surprisingly, the volume of data is not considered to be a top issue. You’d think that big data is all about huge volumes of data – and it is. But insurers believe that identifying, managing, and combining all the different sources of structured and unstructured data is the most challenging technology issue. Veracity is high on the list too. The garbage-in, garbage-out rule still applies here. For some big data uses cases (especially regarding social media data), the data can be less precise and still be useful in spotting big trends. However, for applications in insurance, the data must still represent the truth. Most insurance uses of big data require that the data be reasonably accurate and complete, and some, such as pricing, require a high level of precision.
The primary factors for big data success are not related to the data, or the technology platforms, although both are vitally important. For all the gee whiz technology and the amazing stories in the press about game-changing results, success still comes down to people. The opportunities and potential for big data are vast, and we are just at the dawning of the big data era. There are so many traditional and new data sources to leverage, and so many high powered technology platforms to choose from. But success comes down to having talented people, who can ask the right questions, determine the business value, design the model or analytic approach, and interpret the results. And last but not least, determine how to put the results into action. For more on the organizational, people, and technology implications of big data, read SMA’s companion report, Big Data in Insurance: Emerging Trends in Organization and Technology.
For a complete review of big data projects and investments through 2016, read SMA’s new research report, Big Data in Insurance: Insurer Priorities, Projects, and Investment Plans for 2014 and Beyond.
Mark Breading, a Partner and Chief Research Officer at Strategy Meets Action, is a recognized expert in advanced technologies and their implications for the insurance industry. His specialty areas include the customer experience and analytics. He has exceptional knowledge and experience in many dimensions of the customer/producer experience, including omni-channel, customer communications, mobile technologies, CRM, digital content management, and contact centers. He has broad experience and insights in data and analytics, including business intelligence, advanced analytics, and big data.
