ModelDR - the tool that untangles complex information
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'Whether upgrading data aggregation processes or improving system governance architecture, ModelDR solves the major problem that prevents financial institutions from earning earning more than their cost of capital; the increasing burden of regulation.’
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ModelDR - the tool that untangles complex information
- 1. ModelDR the tool that untangles complex information
- 2. Many sources Multiple views of multiple sources is the data challenge OpenGamma (Postgress) Reference Data Corep Finrep (XBRL Taxonomy) Spreadsheets View 1 View 2 View 3 View 4 2
- 3. Traditional data architecture uses data warehouse 3
- 4. 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 4 Warehouse duplicates & transforms the data
- 5. ModelDRExisting systems 5 Data Point Model architecture OpenGamma (Postgress) EBA COREP (Access) Corep Finrep (XBRL Taxonomy) Spreadsheets Data point models EBA COREP Spreadsheets Corep Finrep FIBO Open Gamma In place access Semantics View 1 View 2 View 3 View 4
- 6. 6 The Data Point Model Value Value Set Data Point Aspect Context Many Value 1 The data point meta model separates the data points and their values And treats concept relationships as data
- 7. Traditional strategy 7 Example DPM with instance data DoBs Client 1925-10-13 Thatcher 1953-05-06 Blair 1953-05-06 Cameron Data Point Model Aspect Values 1945-01-01 1953-05-06 Aspect Values Thatcher Blair Cameron DPs !6#^ 7f%$ ^rrA FQ!@ ~A¬0 Aspects DoB Client Rules
- 8. Regulator Roles Reporting and analytics Data management tooling 8 How ModelDR fits into the banking ecosystem modelDR Systems Trading systems Calculation Engine Reg Report needs (e.g. XBRL taxonomy) Spreadsheets Risk systems Data warehouses Report writer Data modelling Ontology managementTopbraid Data Architect Manager Data Owner Standards FIBO New viewpoint requirements Report schema & semantics Global language Existing Ontologies Data Models RDF & Sparql Data Schemas Existing Report Designs Semi structured Data designs Inputs & Outputs Cross DB Queries New schemas New Ontologies New Report Designs Meta data in SS Congruent View of 100% Data quality metrics Congruent View
- 9. • Gives a single, congruent viewpoint of all the banks data (with provenance) • Enables queries across multiple database, eliminating new data warehouses • Provides users fingertip access to the data architecture in each system • Speaks a common business language by integrating financial ontologies like FIBO and XBRL 9
- 10. 10 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 curveSelect 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 Steps towards the Model DR data solution
- 11. Use cases with demonstration 1. Untangle existing databases 2. Untangle existing spreadsheets 3. Measure and guarantee data quality 11
- 12. 12 Business Entity Class Level Adaptor Attribute Adaptors Adaptors Class Level Aspect Attribute Level Aspect Attribute Level Value Sets Resources 1. Untangle an existing data base
- 13. 13 2: Untangle a spreadsheet 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 Reportable Aspect Values
- 14. Systems Data management tooling Reporting and analytics Regulator Duplication Coverage Consistency Accuracy completeness conformity 14 3. Measure and guarantee data quality Roles modelDR Trading systems Calculation Engine Reg Report needs (e.g. XBRL taxonomy) Spreadsheets Risk systems Data warehouses Report writer Data modelling Ontology management Data Architect Business Domain Expert Data Owner FIBO Cross DB DQ Queries Data quality metrics Meta data Analysis Definition of Good Data Quality Report Designs
Notas del editor
- modelDR is a tool from ModelDrivers. Its unique technology is the support for the data point model
- Traditional solutions to this challenge create the problems of data quality, slow rates and high costs of change. Left hand side examples of As Is systems are from our demonstration site.
- To run a query in current approaches you use SQL. SQL can only run against one database at a time. Therefore, for complex reg reports across multiple databases the data must be copied out to a data warehouse. This example is from OpenGamma – which is a modern system. Uses a warehouse in the traditional approach.
- The source data is all in different formats – it must also be made congruent, which requires complex transforms during the load –. Expensive! And then the data is in another hard coded format. New business requirement = new data warehouse. Oooh, that hurts!
- In the Data Point Model based approach Data congruence is created in the DPM layer. This is a design layer – no heavy weight technology = agile. The data is used in place by preference, not transformed certainly. SPAQL query language a) allows access across multiple databases and B) has the extra power of semantics and inference.
- Secret sauce: Separating each data point from its value is unique to the DPM approach. Allows data points to be re-purposed without duplication = Agile, Massively reduced data duplication. It shares with rdf and other semantic technologies the idea of making structural and meta data explicit, in data, Enable queries to reason based on “knowledge”, New rules and relationships are introduced without database changes
- Current architecture strategies treat the value as the data point. If there are 1,000,000 clients there are 1,000,000 DoB instances. But between 1915 and 2015 there are only 36500 days to have birthday. A 30 fold duplication of data. This 30 fold inefficiency is necessary as the Client and DoB concepts are hard wired together in the Client table. The DPM breaks the data down into its atomic level – negating this constraint. The rule that all clients must have a DoB is in a Rule, not hard coded into the database structure = agile.
- The Data Point Model enables ModelDR to understand any data schema, untangle it and design solutions into many target technologies.
- The tool can import from, and export, to any data structure, from Oracle to OWL ontologies. The desk top design capability means data structures can be manipulated analysed and synthesised in a visual design approach, making it much faster than other approaches – especially spreadsheets! Access multiple sources (the data panoply) Traditional: Duplication - copy data from multiple places to warehouse Data Point Model: Semantic access - Access at source Data congruence Traditional: Transform - change shape of data during warehouse load Data Point Model: Global language Data access Traditional: SQL - One database at a time- No intelligence Data Point Model: SPARQL – across databases- With inference Visibility of information about data Traditional: Ask manager or technologist of each silos Data Point Model: Global, fingertip access Management Control Traditional: Distributed, by silo Data Point Model: Industrialized Solution Design Traditional: Work with physical database design Data Point Model: Work with logical & semantics
- A simple first step is to take a business need requiring data from 2 or more disparate sources and use ModelDR to wire a solution together without a data warehouse.
- modelDR can easily transform all kinds of data, in this case an Oracle database, into a Data Point Model
- Likewise ModelDR can convert regulatory reports, spreadsheet and any other data format to a Data Point Model
- The seven key dimensions associated with data quality (completeness, coverage, conformity, consistency, accuracy, duplication, timeliness). These dimensions facilitate better communications among and between stakeholders about data quality objectives, challenges and remediation approaches. Input to ModelDR are Schema from database, data models, FIBO, Reg report obligations etc Definition of good from Business Domain experts Output will be Cross database querys Data quality Report designs Meta data analysis Data quality metrics. Definitions: Core data Attributes - a composite set of all critical data attributes for all primary applications (i.e. research, trade, clear, settle, price, value, risk, compliance, books and records) throughout the full transactions lifecycle. Definitions of core attributes will be aligned with FIBO Standard Measurement criteria - working on the development of standard data quality metric categories, standard root cause categories, standard set of data governance criteria and standard set of business rules for both reference and transactional data attributes.