Understanding Cesium: A Data Framework Comparison
Explore the comparison between Cesium, a reference data framework at Bank of America, and FIBO (Financial Industry Business Ontology) to optimize data management, compliance, and sustainability. Discover the solutions implemented for sustainable extensibility, data integration, and platform functionality driven by Cesium's ontology. Learn how Cesium provides a single model for client data, firm data, and instrument data, enhancing data quality, security, and efficiency.
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Presentation Transcript
FIBO and Cesium Dean Allemang Working Ontologist, LLC dallemang@workingontologist.com
Two parts: Cesium a reference data framework at Bank of America FIBO Financial Industry Business Ontology What can one learn from the other?
Cesium Reference Data Ontology August 2015
Introduction Cesium is . A Platform for Reference Data at Bank of America / Merrill Lynch A Single source for all client data in markets Integrates and normalizes various systems of records Regulatory attributes What do we need to know about our clients and affiliates to comply to regulations? Consistent linkages between clients, accounts and other aspects Provides a Global consistent footprint Cesium went live in Q1 2014
Sustainable Extensibility The problem of Sustainable Extensibilty Bank of America / Merrill Lynch has several systems of record for clients, accounts, affiliates, etc. How do you get a single view of all that data especially when there are more databases around the corner? ?
Sustainable Extensibility The Cesium solution to Sustainable Extensibilty Build a model of the data Virtualize legacy data as graphs Map the graphs and datasets Include more data sets as time goes on.
Data Integration Cesium provides a Single model for Client data Firm data Instrument Data Primitives aka controlled vocabularies, code lists, data points, value sets, etc. Uses W3C SKOS for controlled vocabularies Tracks provenance (where the data came from) Uses W3C Prov-O Displays information about the data source to end users
Model-driven Platform Data Quality (testing and reconciliation) Security (who can read and write) Cesium Ontology drives all platform functionality Indexing (optimization) Cesium Platform User Interface Ingestion One of the key things that has driven the success of our platform is the ability to use the ontology to drive the platform end to end. Starting with ingestion which governs how legacy formats are converted to RDF, data quality checks which attest to the correctness and consistency of the data, security which governs who can publish and see the data, how the data is indexed for efficient retrieval to how the data is actually rendered in the end user UI these are all driven from a single model. A large part of this is engineering but the engineering would not have been possible without adopting RDF as a strategic choice.
Cesium Ontology Browser Detail Show History (only appears if there is history) Bi-temporal Data Unified id Linked to Metadata Search across 100+ ids Search across names Filtering Faceting History Navigation Dev mode Linked Data Aspects 9
Platform Features RDF-based open model Based on W3C standards including RDF, SKOS and Prov-O Real-time and Bi-temporal Real-time end users Current view or bi-temporal snapshot Extensions, Overrides and Defaults The model can be extended to cover new data sets Extensions include certain non-monotonic logic like defaults and overrides Workflow and Data Quality control are integrated into the platform
Questions 11
FIBO Motivation Financial Industry Business Ontology Spearheaded by the Enterprise Data Management Council (EDMC) Response to BCBS 239 Ontology that describes data for the financial industry
FIBO Technology FIBO is an Ontology published in OWL/RDF Provides a sharable model of data that can be mapped to other data models
How could Cesium benefit from FIBO? Basic entities Parties Accounts Contacts Restrictions Ratings Hierarchies
Modeling Decisions Are contacts separate from parties? Are there different kinds of contacts?
Modeling Decisions (cont) What kinds of hierarchies are there? How do we represent restrictions? How do these things change with Jurisdiction? What are our reporting requirements for various regulations? What kinds of codes can help us identify the lines of business of an affiliate? I d like to have a model that someone has already thought through, so that I don t have to repeat this work
Many Masters RegW RegM APRA CFTC SEC FINRA Regulations Federal Reserve Bank of England Regulators MISMO LIXI ISO 20022 known FIBO can even help predict data needs of unseen datasets And business use cases In-house data sets SWIFT FBRL Messaging Standards An extensible system serves many masters Vocabularies NAICS upcoming SEC UNSPSC Business users
Why is Cesium in RDF? RDF is extensible Cesium can incorporate new data sets and new business units RDF is built for mapping Cesium connects (meta-)data back to its source using Prov-O RDF is format-agnostic Cesium has to deal with data from source systems, some internal, some external RDF is Scalable Cesium is enterprise-wide in a large firm
Why is FIBO in RDF? RDF is extensible We can incorporate new standards, models, etc. into FIBO RDF is built for mapping Connect (meta-)data from FIBO to original sources RDF is format-agnostic RDF can express data from XML, XSD, spreadsheets, databases, CSV, RDF is Scalable Cesium is an example
What can FIBO learn from Cesium? What are the needs of a production-level reference data system in a Strategic Bank? What does it mean to operationalize an ontology at production scale? Specifically, what are the distinctions we need to track for various regulations (RegW, Dodd-Frank, etc.)?
Division of Labor Operational Constraints Requirements Priorities Cesium Model decisions Links to Regulations and Standards Use Cases
