Advantages of a standard insurance data model


"BEST PRACTICE" Tag Cloud Globe (business process improvement)

In my first blog article I explained that many insurance companies have implemented a standard data model as base for their business analytics data warehouse (DWH) solutions.

But why should a standard data model be more appropriate than an individual one designed especially for a certain insurance company?

Besides faster implementation the important advantages of a standard data model for insurance are:

  • better support for data governance,
  • standardization of business processes,
  • verification of auditability,
  • improvement in data quality,
  • and higher maturity of the model as well as release capability.

Let’s consider these advantages in more detail.

Data Governance

A standardized data model is an essential pre-condition to achieve data governance in the enterprise. For example because it supports data governance processes by using available industry standards and coded expressions wherever possible.

Standardized representation of business processes e.g.  insurance contracts

There are standard structures available that have been defined by study groups of the insurance industry and also have demonstrated their successful implementation capabilities for all lines of insurance business.


Using such a standard structure considerably simplifies the implementation of different lines of business in the data warehouse by enabling the development of ETL code following a common logic for all lines of business.

Code expressions instead of free text

Standardized code expressions will be used in a standard data model wherever possible. The code values may be individually defined by each division or operational entity (OE) of the insurance company and then be mapped to company-wide valid values on a meta-data level after populating the DWH.

I will explain further details of this standard code concept in a follow-up blog article.

Auditing acceptability

Auditing acceptability is crucial if data content and state of the data warehouse must be tracked at any time of the data warehouse lifecycle. This includes defective data content that may have been used as entry data for reports or analytical results. This transparency is demanded by several regulations for insurance e.g. Solvency 2 or U.S. Solvency Regulation. An auditable history management concept is therefore a very essential requirement for a DWH data model. This is typically part of a standard data model concept as a result from best practices of successful DWH implementations.

How this may look in detail will be illustrated in another future blog.

Data quality

Data quality is linked very tightly to data governance: a data governance concept always has the objective to improve the quality of data sources throughout the enterprise. The demand on a DWH data model is not to simply copy the defects of operative data sources but to implement a consistent and cleansed relational model. The standard data model ‘Detail Datastore for Insurance’ (DDS) for example implements a business party model that identifies any party uniquely and allocates individually defined – but unique – insurance business party roles. Data cleansing is supported by data quality tools including master data management.

High maturity and release capability

Last but not least a big advantage of a mature DWH standard data model for insurance is that it has gone through several release cycles and been thoroughly tested and therefore reached a degree of maturity that an individual model development could only reach after a long period of time. Numerous changes of an individual data model development are expectable and may significantly slow down the data warehouse implementation.

An extension management concept together with customization guidelines ensure upwards compatibility between individual model extensions of customer projects and future standard developments. This concept has also been applied to produce an extended insurance data model ‘DDS Central Europe’ which supports some specific requirements of central European insurance companies. More on this in another upcoming blog article.

Learn more about successful insurance business analytics solutions at SAS Global Forum 2016.

Hartmut Schroth, Business Advisor data strategies for insurance at SAS Germany. For further discussions, connect with me on LinkedIn.


About Author

Hartmut Schroth

Business Advisor

After finishing his master study of mathematics in 1981 Hartmut has been working in insurance and IT business in several positions. Since 2006 he is employee of SAS Institute in Germany. As business advisor in the presales team he is responsible for DACH region (Germany, Austria and Switzerland). His main focus is advising customers of financial services industry in strategies for data models of SAS Business Analytics solutions. Moreover he is regional product manager for the SAS Insurance Analytics Architecture.

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