Sound infrastructure techniques, data management methods, rich functional content and an implementation roadmap help to reduce data warehouse development costs and minimize project risks.
The solution helps address regulatory compliance issues associated with reporting by providing the right level of data granularity. Examples include the GDPR, the CCPA and Solvency II.
Consolidating financial and actuarial data gives you more control, while reducing the time it takes to scope requirements, perform subsequent customization and carry out data warehouse extensions.
With no modeling or abstraction involved, business terms define in plain language the industry concepts involved in the insurance industry. Clearly defined business terms help support standardization and communication within an organization. Mapping to the data models makes it possible to create a common, enterprise-wide picture of the data requirements and transform IT data structures based on those requirements.
The dimensional warehouse model provides the data design support needed to transform enterprise-level business requirements into efficient, business-specific structures dedicated to the design of a dimensional data repository. The comprehensive logical data models contain the predefined data warehouse structures required to store all financial services data in an efficient layout for analytics.
Supportive content captures non-reporting requirements in a particular domain and relates them to the data warehouse model entities, relationships and attributes. It provides a method of mapping both external and internal terms, from business standards and other requirements to business terms and atomic and dimensional warehouse models.
The IBM solution provides an industry-specific vocabulary that can help discover and govern privacy data. It includes KPI templates for regulatory reporting and a hierarchy of General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) terminology. The glossary and underlying data warehouse models help organizations ensure that their enterprise data architecture is able to provide the necessary data artifacts to report on data protection issues.
Analytical requirements reflect the most common queries and analyses for business performance measurement and reporting, while supporting other analytical functions such as ad hoc reporting and decision support. Over 140 predefined business reporting requirement templates are provided, addressing the common business reporting and analysis requests from risk, finance, compliance, CRM and line-of-business users.
IBM Insurance Information Warehouse provides the necessary modeling tools and support for requirements gathering to accelerate Solvency II implementations and build a flexible, fit-for purpose risk management warehouse. The models make up a flexible, scalable solution and provide a unified view of critical business data for risk management. Coverage for Solvency II includes support for asset management; balance sheet; premiums, claims and expenses; and reinsurance, life and non-life technical provisions. Aligned to data point model 2.3.0.
This is the first point where various business requirements are brought together and modeled in an entity relationship format. This component includes common design constructs that can transform into separate models for dedicated purposes, such as operational data stores, warehouses and data marts. Designed for the insurance industry, the business data model contains thousands of business definitions and provides an enterprise-wide view of data common to all insurers.
The atomic warehouse model is a logical model consisting of the data structures typically needed by an insurer for a data warehouse. The comprehensive logical data models contain the predefined data warehouse structures required to store all financial services data in an efficient layout for historical and atomic data.
Accelerates the design of an enterprise data warehouse or business intelligence solution, based on financial services business requirements.
Predefined energy and utilities-specific vocabularies, KPIs and data structures, which can help accelerate enterprise governance and analytics projects.
Predefined healthcare-specific vocabularies, KPIs and data structures, which can help accelerate enterprise governance and analytics projects.