I have written about various industries using Big Data and today we will go through some pointers for Insurance Industry. Having worked for implementing 2 solutions for insurance industry I am aware of the challenges faced by the industry. Insurance Industry data today
comes from disparate sources that include customer
interactions across channels such as call centers,
telematics devices, social media like Facebook & twitter, agent conversations,
smart phones, emails, faxes, day-to-day
business activities and others sources.
Most of the data processed by organisation today is structured data and it is hardly 10% of the data available. Insurance company can reap real benefits like
1) Increased productivity
2) Improved competitive advantage and
3) Enhanced customer experience
4) Derive business insights and business value from Data Analysis
by capturing, storing, aggregating, and eventually analyzing the data from new age sources. The value comes from harnessing the actionable insights from this data. The strategic objectives of the Insurance Business can be achieved by having a well defines Business Objectives & KPI, clearly defined Business Intelligence requirements and Analytical requirements that help the Data Science team to define the Data Processing for Big Data thus leveraging the 100% data rather than 10 to 20% data that is leveraged today by the industry and achieve actionable insights to achieve the Business Objectives. Clearly just having a Big Data strategy is not enough and we need a well defined custom Analytic Strategy that extracts the true value of the data for the business. In other words the business should have a 2 independent approaches & 2 set of experts to process big Data and to perform analytic on Big Data, these are unique streams of IT , their goals are unique & one should not expert expertise across these 2 technologies ( I must admit there are a handful of people who do have expertise across technologies but the technology is still very new).
Investing in Big Data, like any other technology should be a phased process starting with Business Vision, Strategic Objectives, Technology Vision,Priority Business Cases and Prototyping and finally refinement to the Vision and Strategy. Real value is derived when actionable insights can make a positive difference in achieving the Insurance organization’s strategic objectives. (Is this is too technical for some industry readers I will be happy to simplify it).
Once prototype is successful it is easier to convince the business to invest in Big Data & Analytic Strategy.
Key points for the business to consider
1) By tapping into more than 80% of untapped data business will discover new insights
2) Processing of entire data gives better transparency and accurate perspective to the the business
3) Big Data & Analytic require complete digitization thus enabling 360 degree insight
4) We are also enabling Real time or near real time processing of data that will enable insurer to experiment with products to identify needs of customer which helps in deliver new products and retaining existing customers
Next we will discuss the value creation from Big Data & Analytic & enabling a Real Time Insurance Enterprise.
Most of the data processed by organisation today is structured data and it is hardly 10% of the data available. Insurance company can reap real benefits like
1) Increased productivity
2) Improved competitive advantage and
3) Enhanced customer experience
4) Derive business insights and business value from Data Analysis
by capturing, storing, aggregating, and eventually analyzing the data from new age sources. The value comes from harnessing the actionable insights from this data. The strategic objectives of the Insurance Business can be achieved by having a well defines Business Objectives & KPI, clearly defined Business Intelligence requirements and Analytical requirements that help the Data Science team to define the Data Processing for Big Data thus leveraging the 100% data rather than 10 to 20% data that is leveraged today by the industry and achieve actionable insights to achieve the Business Objectives. Clearly just having a Big Data strategy is not enough and we need a well defined custom Analytic Strategy that extracts the true value of the data for the business. In other words the business should have a 2 independent approaches & 2 set of experts to process big Data and to perform analytic on Big Data, these are unique streams of IT , their goals are unique & one should not expert expertise across these 2 technologies ( I must admit there are a handful of people who do have expertise across technologies but the technology is still very new).
Investing in Big Data, like any other technology should be a phased process starting with Business Vision, Strategic Objectives, Technology Vision,Priority Business Cases and Prototyping and finally refinement to the Vision and Strategy. Real value is derived when actionable insights can make a positive difference in achieving the Insurance organization’s strategic objectives. (Is this is too technical for some industry readers I will be happy to simplify it).
Once prototype is successful it is easier to convince the business to invest in Big Data & Analytic Strategy.
Key points for the business to consider
1) By tapping into more than 80% of untapped data business will discover new insights
2) Processing of entire data gives better transparency and accurate perspective to the the business
3) Big Data & Analytic require complete digitization thus enabling 360 degree insight
4) We are also enabling Real time or near real time processing of data that will enable insurer to experiment with products to identify needs of customer which helps in deliver new products and retaining existing customers
Next we will discuss the value creation from Big Data & Analytic & enabling a Real Time Insurance Enterprise.
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