Nothing works today without an efficient data management – also in insurance business. A standard data model can be an important component of it. This article explains why.
“Make or Buy”? This question has been raised very often by insurance companies planning to introduce a consistent data structure – a data warehouse (DWH) - for different business analytics applications . This data structure should integrate all lines of insurance business and provide a 360 degree view on all information of related business parties. The first task is to make a decision on developing an individual data model or buying a standard data model from a software vendor.
But what can be the ‘standard’ of a DWH data model for insurance business?
A standard data model for business analytics must comply at least with the following requirements:
- It has to support the data requirements of many different business analytics applications.
- It must comply with available standards and support all important and common insurance business processes.
- It has to support the data governance processes and data quality management.
- It must comply with regulatory rules - especially complete auditability.
- It has to support project specific extensions and must be release capable.
The ‘Detail Datastore for Insurance’ (DDS) as core element of the SAS Insurance Analytics Architecture was released for the first time in 2004 in cooperation with a major insurance company. Since then it has been consequently enhanced and refined. Today it has been licensed by more than 60 insurance companies all over the world.
SAS has been working on business analytics data warehouse implementations together with insurance companies all around the globe for the past 30 years now. In the beginning the goal was always to implement an individual data structure for an insurance company within the given project requirements. Time by time SAS identified a lot of common structures within the different DWH projects. This powered the motivation for the development of a standard business analytics data warehouse with focus on providing data for different insurance business analytics applications. The application scope covered classical business intelligence applications like performance reporting as well as analytical scoring solutions to calculate 'customer value' components, customer intelligence solutions for campaign management and rate making applications for actuary departments of insurance companies. In the last years risk management for Solvency II and fraud detection completed the application focus.
It is important to mention that the DDS was not developed on the drawing board but out of field projects and has been validated in many DWH projects for insurance. The main focus was set to fasten the implementation of business analytics applications and increase their value. Available standards of the insurance industry with relevance for business analytics aspects are supported by the structures of the DDS. Many concepts of ACORD and GDV have been implemented in the data model. Besides one of the key aspects of development was to support data governance processes, data quality aspects and auditability.
Requirements of insurers in the Central European region have been used by SAS to enhance the data model and supply an extended Central European (CE) version of the DDS in addition to the 'core' global data model. This could be done very easily due to the powerful extension management concept and customization guidelines of the DDS that ensure release capability.
Important features of the SAS standard data model and related DWH concepts will be presented in following blog articles. E.g. explicit advantages of the SAS standard data model for insurance, data governance and data quality aspects, auditability, release capability and extensions for Central Europe (‘DDS Central Europe’).
Read more on SAS data model for insurance business: http://www.sas.com/en_us/whitepapers/data-is-king-105398.html.
Hartmut Schroth, Business Advisor data strategies for insurance at SAS Germany. For further discussions, connect with me on LinkedIn.
 We talk about a data integration layer for feeding different business analytics applications and not about 'enterprise data warehouse' nor ‘operational data store’.