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Using IBM Insurance Information Warehouse & Big Data to Augment IBM Software Information Management White paper Using IBM Insurance Information Warehouse & Big Data to Augment a Data Warehouse 2 Using IBM Insurance Information Warehouse and Big Data to Augment a Data Warehouse Big Data and Insurance Big data sources The ability to use big data is a differentiating factor for enterprises Big data sources in the insurance sector. The adoption of big data technologies has triggered an evolution in the way existing information management Transactions 88 % challenges are tackled. Log data 73 % The growth in availability of new information sources such as sensor data (for example vehicle telematics) and social media is providing Events 59 % new opportunities for insurers to gain new insights. Text analysis unlocks the hidden meaning and context found in insurance docu- Emails 57 % mentation and communication (email, web, voice). This same infor- mation when aggregated and combined with external sources such as Social media 43 % fraudster registries, shared information databases, and geo spatial data supports new levels of customer insight and transform how insurers Sensors understand and serve their customers. 42 % IBM Insurance Information Warehouse plays a significant role in ad- External feeds 42 % dressing these challenges by providing both big data specific content and by accelerating the creation of the logical data warehouse that RFID scans or POS data 41 % encompasses both new and traditional data structures. Free-form text 41 % Insurers can now use the comprehensive traditional data warehouse Geospatial coverage in IBM Insurance Information Warehouse alongside new 40 % information sources for big data to develop new customer insights, Audio support new information needs, innovate new products and manage 38 % ever increasing data volumes. Still images/videos 34 % The IBM IBV study “Analytics: the real world use of big data”1 found Respondents with active big data eorts were asked which data sources that organizations are being practical about engaging with big data they currently collect and analyze. Each data point was collected as they work to understand how it can be of value to their business. independently. Total respondents for each data point range from Most are educating themselves on the key use cases, defining a big 557 to 867. data roadmap or are conducting pilot implementation activities. Figure 1. Organizations are mainly using internal sources for big data efforts 1 This finding is echoed by IBM Industry Model customers who are 7% looking to extend their existing investments in data architecture tools Respondents were asked which data sources they currently collect and analyze, and processes to help harness the opportunities of big data. A com- based on those respondents whose organizations are already engaged in big data eorts. mon theme is the use of big data to enhance and augment existing business intelligence solutions by increasing the volume and variety of data available for analysis. Information Management 3 Information Management for Big Data Business and Technical Priorities and Analytics The business and technical priorities for big data projects include: There is an emerging challenge for information management that is being encountered by enterprises that are seeking to combine big Customer centered analytics data and analytics. As enterprise data warehouses are expanded and Understanding what information you hold on a customer enables the augmented with a variety of new data such as unstructured, geospa- development of a 360 degree view of the customer. IBM Insurance tial, weather and claims transaction event data there is a relentless Information Warehouse is used as a reference point for the develop- business demand for data analytics and exploration capabilities that ment of an enterprise catalogue of often disparate data models and work across all data. business vocabularies. There is critical need for the analytical results and insights from Developing strong analytical capabilities predictive and explorative analysis to be safely integrated with existing The ability to generate business value and insight from big data trusted information used by decision makers. For example, while the requires the fusion of business expertise, data analysis skills and robust combination of publicly available geospatial and claims transaction data management. IBM Insurance Information Warehouse provides data makes it possible to extract insight about an insurers customers, a combination of integrated business, analytical and data warehouse third parties or competitors – care is required to ensure that the data models that help business and technical analysts plan the exploitation is combined accurately and in accordance with both the institution’s of data assets. and regulator’s data and privacy guidelines. Unlocking the value in existing data The data models and business vocabularies provided IBM Insurance There is untapped potential locked away in both internal operations Information Warehouse provide finical institutions with a blueprint and business intelligence systems. Initial efforts are focused on using for combining, describing and governing big data for analytics. big data to gain insights from internal data sources, both new and existing. IBM Insurance Information Warehouse provides structure for the storage and analysis of transaction data such as claims and website logs. Enhancing information governance The business and technical priorities for big data projects include: The existing challenges of managing and connecting data across the organization become more complex with the addition of new internal Customer centered analytics and external sources. Data architectures must be able to support a Understanding what information you hold on a customer enables the combination of relational database systems (RDBMS), analytical ap- development of a 360 degree view of the customer. IBM Insurance pliances, Hadoop and NoSQL databases. IBM Insurance Information Information Warehouse is used as a reference point for the develop- Warehouse can be used to deploy a logical data warehouse that spans ment of an enterprise catalogue of often disparate data models and various data technologies. business vocabularies. Figure 2. Combination of data 4 Using IBM Insurance Information Warehouse and Big Data to Augment a Data Warehouse Using IBM Insurance Information Warehouse and Big Data to Transform Insurance IBM Insurance Information Warehouse provides a flexible and scala- Creating the logical data warehouse ble data warehouse design, enabling organizations to build a com- The data required for usage-based insurance requires data archi- prehensive data warehouse solution through phased development. tectures that span the big data platform (Hadoop) and traditional This allows for rapid delivery of high-business-value deliverables by data warehouse. This combined data resource is called the logical initially focusing on the business areas offering the greatest returns data warehouse. As with all complex databases it is vital that a single and feasibility, while building within a proven technical warehousing data model is used to understand, access and govern. IBM Insurance architecture. Information Warehouse is ideally suited to this task of data warehouse augmentation as it is implementation neutral and can be deployed on Creating new insurance products with vehicle telematics a range of platforms. data The potential of big data to transform the insurance product land- Business Example Diagrams scape can be illustrated with a vehicle telematics and usage-based IBM Insurance Information Warehouse includes new big data busi- insurance use case. ness overview diagrams and associated entity-relationship scenario diagrams covering three uses cases: Customer Analytics and Insight, Analysis of data such as customer demographics, vehicle attributes Vehicle Telematics, and Catastrophe Modelling. and claims history stored in the traditional data warehouse provides a basis for underwriting of vehicle insurance policies. Business users The business overview diagrams provide a design-level business view and data scientists are curious to explore how this existing analysis that enables users to get started with Big Data. The view shows how could be combined with information from big data sources such as organizations can augment traditional data with data from new Big vehicle telematics. This approach is illustrative of the type of proto- Data sources, and allows users to visualize the main data concepts be- typing that is being undertaken by early adopters. ing addressed and shows how data items from the use case align with the overall model structure Telematics data provides second-by-second information on vehicles and how they are being driven. Within Hadoop, the information The entity-relationship scenario diagrams provide a technical view could remain unstructured but also integrated with more structured and represent a subset of the model that can support a specific scenar- modeled information. This can be analyzed to provide a detailed io described in the documentation. The diagrams feature a color-cod- picture of how, when and where a vehicle is being driven, by aggre- ed background showing which entities are suggested Apache Hadoop gating the data and deciphering the driver patterns and driving rating. or similar deployments. Ultimately this driver scoring is be used to determine the usage-based premium. Information Management 5 Vehicle Telematics Use Case Business context Telematics
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