It's the age of big data and the internet of things (IoT), but how will that change things for insurance companies? Do insurers still need to consider classic data warehouse concepts based on a relational data model? Or will all relevant data be stored in big data structures and thus
Auditability and data quality are two of the most important demands on a data warehouse. Why? Because reliable data processes ensure the accuracy of your analytical applications and statistical reports. Using a standard data model enhances auditability and data quality of your data warehouse implementation for business analytics.
Using a standardized data model is an essential condition to achieve data governance in an enterprise. A standard data model supports data governance processes by implementing industry standards wherever possible: standards for contract and claims representation, mapping of data content with standard definitions (glossary function), use of code attributes
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?
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