As the entire insurance industry is in the turbulent times of saturated markets and new regulations, the importance of the availability of the right information at the right time, on the basis of which one can make the right decision and take appropriate actions, is increasingly highlighted. Our Insurance DWH model solution simplifies these tasks as well as entire decision making process.
Our Insurance Data Warehouse Data Model (PI Insurance DWH Model) is a standard industry data warehouse model applicable for both life and non-life insurances. Based on data represented in the model, all standard insurance reporting and analysis Data Marts can be delivered.
Insurance DWH Model is based on industry best practices, developed and applied during data warehouse system implementations in Basler osiguranje Zagreb. Also, it is open for necessary alterations and modifications required for each insurance customer.
PI Insurance DWH Model is based on strong Primary Key – Foreign Key relationships that will assure consistency in the model itself and in content of implementation Data Warehouse System. Physical model implementations can be in every standard RDBMS.
PI Insurance DWH Model is developed with ERWin and will require ERWin license to view or change. Model can be exported to any other standard database modeling tool format.
Visual representation of Main Subject Area, which consists of all PI Insurance DWH Model Entities:
In PI Insurance DWH Model, semantic data model is actually integrated with logical data model in several ways: relationships between the various entities are named and they describe type of relationships, naming conventions for entities and attributes is respected throughout the model, and all attributes have a domain. Domains are not only generic (String, Number, Date), but also context-specific and descriptive of attribute role (Name, Address, ID, Telephone Number).
PI Insurance DWH Model is customizable per customer specific requests. There are several types of customizations, for instance customizing of existing Entities including changing attribute properties (name, type, and description), adding new attributes, adding indexes etc., creating of new Entities if there is a requirement for a new dimension or new aggregation, adding new Subject areas with multiple entities that will cover new business areas and customizing of physical model – defining partitions, block sizes etc.