Leveraging ALFA to extend Legacy and Strategic Systems
Data models and rules are generated into multiple target platforms and languages allowing integration with existing technology used in the organisation.
A typical financial organization would have multiple sources of risk input data (trade data stores, reference, and market data stores), and multiple traded risk management systems which tend to be arranged by asset class. The aggregation would happen at ‘function’ systems mainly risk and P&L systems.
The challenge is data siloed these systems create and varying degrees of consistency and accuracy.
The data is not standardized within and across clusters and divisions thereby making analyzing, aggregating, and reporting of risk and finance with the organization difficult.
Separate data sourcing process and infrastructure between functions (market risk, credit risk, liquidity, and balance sheet management) causes inefficiencies and complexity.
These processes are supported by numerous data infrastructure solutions, rely on heavy data transformation and manual processes at target- the receiving application.
Consistent data format
Clear definition of published data by multiple sources, and guarantee data quality at source.
Consistency with single model boosts faster, more accurate insights, as well as increased speed and agility to change.
ALFA Advantage for Integration and Analytics
Reusable code libraries used across the cross-asset-class system. Duplication of work. Too often, applications that use the same or similar data require bespoke development effort due to each application-specific approach to handling data
Accelerate IT delivery
The platform accelerates application delivery times by allowing an enterprise to create applications through model configuration rather than by coding those objects and functions. Introducing data attributes or new datasets required by the user (business or regulator) takes minutes not ages.
Low-code, highly flexible, consistent, validated, cost-effective framework for sourcing data from multiple different systems with guaranteed data quality.
Technology agnostic Integration
Technology to provide validated, version-controlled, reusable, canonical, and federated data models while the platform's underlying technology helps firms solve a multitude of bespoke architecture problems.
Organisations utilise this function towards microservices and enhanced data governance.
Use same data model in Analytics
Support innovation. A significant number of business users rely on AI/ML or Excel to view, edit, manipulate their data before feeding the data into other business applications - enable them to benefit from the same guarantees of data quality and adherence to canonical modelled data enabled by ALFA.
Interoperability to support innovation. Changes incrementally introduced to existing applications.
Support for new/updated feeds and data can be made available in a matter of days, not months.
ALFA is designed for Service oriented Architecture, and allows Architect to review the model and extend some features such as adding APIs and model metadata.