Slidesharedeck feb19
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The document discusses the Model DR and data point model market's significance in improving data governance and regulatory compliance for banks. It highlights the need for banks to evolve their data architectures to facilitate better risk data aggregation and reporting. The document outlines the objectives and next steps for implementing a Model DR strategic data aggregation solution while emphasizing the importance of a universal language in big data technology.
Slidesharedeck feb19
- 2. 2 • Why Model DR & data point model market exists • Regulatory environment • Model DR solution • What value we offer to our customers • What differentiate s Model DR • Questions
- 3. ModelDR – a data design & analysis solution • providing a single, enterprise view of portfolio risk and exposure • resolving data dependency on inflexible systems architectures • providing a congruent dataset ready for reconciled & validated report generation • enabling transition towards larger system architecture decisions 3
- 4. 4 Financial regulation demands improvements in bank risk data aggregation and reporting Banks must establish strong data governance, architecture & processes Regulators want to know if banks prioritise compliance as highly as other infrastructure projects
- 5. Data warehouse Front office system Client ID Name Client type 1 Thatcher Gold 2 Blair Silver 3 Cameron Bronze Risk system Client ID LEI Name Client type Risk Rating 1 987 Thatcher Gold High 2 654 Blair Silver Medium 3 654 Cameron Bronze Medium 321 Obama LowLEI Name Risk rating 987 Thatcher High 654 Blair Medium 321 Obama Low Current Data Management 5
- 6. Data Point ModelFront office system Client ID Name Client type 1 Thatcher Gold 2 Blair Silver 3 Cameron Bronze Risk system LEI Name Risk rating 987 Thatcher High 654 Blair Medium 321 Obama Low Name Client ID Front Office Client Client Type LEI Risk Client Risk Rating aggregates Big data and sematic technology 6
- 7. Objective Historical Data Point Model Data panoply Duplication Semantic access Data congruence Transforms Global language Data access SQL - One database at a time - No intelligence SPARQL - Across databases - With inference Visibility Silo by silo Global Control & lineage Distributed Industrial Design Physical Logic & semantics Let’s compare & consider 7
- 8. The Data Point Model 8 Value Value Set Data Point Aspect Context Many Value 1 The data point meta model separates the data point and the values
- 9. Reverse Engineer a Report into a DPM 9 Report NameY Axis Aspect Y Axis Value Set Name Y Axis Coordinate Aspect Values Reportable Aspect X Axis Aspect X Axis Value Set X Axis Coordinate Aspect Values Package Name Reportable Aspect Values
- 10. 10 Business Entity Reverse Engineer a Data Base into a DPM Class Level Adaptor Attribute Adaptors Adaptors Class Level Aspect Attribute Level Aspect Attribute Level Value Sets Resources
- 11. Designing a new viewpoint DPM to DPM 11 A third DPM wired together from 2 existing DPM Regulation may be the driver but efficiencies will evolve from a universal language across the big data technology curve Next Steps towards the Model DR strategic data aggregation solution Select legacy systems for ModelDR to reverse engineer into DPM format ModelDR ‘wires up the new DPM viewpoint to an existing or new database Model DR forward engineers a query drawn from the new viewpoint New reports are drawn from old system architec ture Assessment of tactical transition to the ModelDR strategic solution