The financial industry is facing a perfect storm of disruptive drivers for data management. While regulators seek accuracy and transparency, institutions are struggling with fragmented data and IT infrastructures. The path forward is “data engineering” – applying consistent semantics with scalable infrastructure to harmonize data and enable traceable and dynamic analytics. In this webinar, we hear from industry practitioners and thought leaders on how this vision is being deployed and also see it in action.

1. Applying Data Engineering and Semantic Standards to Tame
the "Perfect Storm" of Data Management
March 2nd, 2017
Marty Loughlin
Vice President
Cambridge Semantics
500 Boylston St., Suite 1700, Boston, MA
www.cambridgesemantics.com
marty@cambridgesemantics.com
(o) 617.855.9565

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Introduction to Cambridge Semantics (CSI)
Agenda
• Introduction
Marty Loughlin, Vice President, Cambridge Semantics
• Financial Industry Data Challenges & Solution Overview
Carl Reed, Adviser, Cambridge Semantics
• Regulatory Perspective & FIBO Update
Mike Atkin, Managing Director, Enterprise Data Management Council
• State Street - FIBO Interest Rate Swap Demo
Arthur Keen, Managing Director, Cambridge Semantics
• Q&A

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The Anzo Smart Data Lake
Smart Data Discovery, Analytics & Management
Company:
 Founded in 2007 by senior team from IBM’s Advanced Internet Technology Group
 Privately Funded
 Select customers:
Software:
 Market leading Anzo software suite is built on open Semantic Web standards
 3rd generation of Anzo in production
Introduction to Cambridge Semantics (CSI)
MIT Innovation Showcase
Business Intelligence /
Analytics Solutions

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Financial Industry Data Challenges & Solution Overview
Carl Reed, Adviser, Cambridge Semantics

5. Trading
Settl&Clear
Risk
Operations
OrderMgmt
Compliance
Treasury
RegReporting
RefData
...
Enterprise Data Governance, Architecture & Execution
The World Most of Us Grew Up In
• Process Driven Architecture
• Vertically Alligned Implementations
DataCenterMgmt
DisruptionTension
Carl Reed February 24th 2017
Can We Turn Tension and Disruption into Opportunity?

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Three Key Ingredients
Three Key Ingredients
Organization Structure
Technology ArchitectureCommon “Lingua Franca”
Enterprise Data
GOVERNS

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Data Engineering Data Science
Knowledge Engineering
(Ontology)
Enterprise Data
External Data
Ontologies
Domain Expertise
(Business SME’s)
Harmonized Data
Expertise
Business Intelligence
Requirements
New Intelligence
Scope
Semantic Mappings
Knowledge Graphs
Data Governance
Internal
External
1: Data Oriented Roles and Activities
C Suite Accountability, Responsibility, Authority
Carl Reed February 24th 2017
1. Data Oriented Roles and Activities

8. 2.1: A Semantically Driven Enterprise Data Archtecture
Carl Reed February 24th 2017
Business & Technology Governance
Information Marts/Warehouses
Source Meta Data
Concepts
Relationships
Domains
Scale Out Compute
Semantic Enrichment
Semantic Transforms
Identity Resolution
Scale Out Storage
Indexing
Integrated Data Sets
Raw Data Sets
Data Engineering
Business Intelligence & Data Analytics
Client/Customer Market Operational Risk/Reputational
OntologyExecutionPersistence
Data Sourcing
DistributionRefinement
Structured Unstructured Visual Physical
Communicatio
n
Data Sources
Acquisition Modes
Search
SourceRegistry
BusinessGlossary
AccessControl
Relational NoSQL GraphTSDB Archive BRM Other
Lineage
2.1: A Semantically Driven Enterprise Data Architecture

9. Carl Reed January 25th 2017
Business & Technology Governance
Information Marts/Warehouses
Source Meta Data
Concepts
Relationships
Domains
Scale Out Compute
Semantic Enrichment
Semantic Transforms
Identity Resolution
Scale Out Storage
Indexing
Integrated Data Sets
Raw Data Sets
Data Engineering
Business Intelligence & Data Analytics
Client/Customer Market Operational Risk/Reputational
OntologyExecutionPersistence
Data Sourcing
DistributionRefinement
Structured Unstructured Visual Physical
Communicatio
n
Data Sources
Acquisition Modes
Search
SourceRegistry
BusinessGlossary
AccessControl
Relational NoSQL GraphTSDB Archive BRM Other
Lineage
Koverse
FTP/CSV, Apache Kafka, Sqoop, Storm
Cloudera
Koverse
Cambridge Semantics
ANZO
GQE
RedOwl
Digital Reasoning
TopBraid
Allegro
2.2: That Can be Implemented and Execute at Scale
2.2: That Can be Implemented and Executed at Scale

10. The New Big Data EcosystemLegacy Enterprise Data Problems
Incrementally solving
legacy data problems
using new Big Data
technology & techniques
Carl Reed February 24th 2017
Add sources to data registry and distribute via
hub supporting legacy client semantics for
existing clients and enforcing enterprise
semantics for new.
Migrate Over Time
2.3: That Can Accommodate the Existing as well as Execute the New
2.3: That Can Accommodate the Existing as well as Execute the New

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Regulatory Perspective & FIBO Update
Mike Atkin, Managing Director, Enterprise Data Management
Council

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Data Management in Perspective
Beachhead for Data
Management Established
Data Management Implementation
Based on Best Practice
Unified View of Data Meaning
(primary data objective)
Consistent Measurement of Data
Management Progress
Data Management
Operational Playbook
Inference Processing for
Analytical Adaptability

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Why Harmonized (common language) Data Matters

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Why Harmonized (common language) Data Matters
• Degree of
interconnectedness
• Transitive relationship
• State contingent cash flow
• Collateral flow
• Degree of centricity
• Funding durability
• Leverage & liquidity
• Guarantee & transmission
of risk
• Degree of diversification
Instruments
• Identification
• Classification
• Description (rates, dates,
features, schemes,
provisions)
• Value (i.e. price, date, time)
• Calculate (volatility,
correlation, duration, tax)
• Maintain (corporate actions)
Entities
• Entity type (legal persons,
formal organizations,
corporations, partnerships,
affiliates, trusts, functional,
etc.)
• Ownership structures
• Controlling relationships
Obligations
• Issuance process
• Trade and execution
• Guarantee
• Allocate and administer
• Clear and settle
• Transfer
Holdings
• Firm portfolio (individual
entity risk)
• Corporate structure
(organizational risk)
• Industry wide (systemic
risk)

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BCBS 239 in Context
2008 Crisis: Inability to model contagion (who
finances who, who is linked to who, what are the
obligations of complex financial instruments)
Senior Banking Supervisors Group: Observations
on Developments in Risk Appetite Frameworks and
IT Infrastructure (intractable relationship between
data and risk management and definition of control
environment)
BCBS 239: Principles of Risk Data Aggregation
and Reporting (governance, content infrastructure
and data quality as mandatory objectives)

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EDMC Regulatory Areas
Regulatory Actions
Fundamental Review of Trade Book (FRTB)
Dodd-Frank: Title I (systemic risk) and Title VII (derivatives)
European Market Infrastructure Regulation (EMIR)
BCBS 239: Principles of Risk Data Aggregation & Reporting
Comprehensive Capital Analysis and Review (CCAR) and Basel III
General Data Protection Regulation (GDPR)
Investment Book of Records (IBOR)
Bank Integrated Reporting Dictionary (BIRD)
Financial Data Standardization Project (EC)
Regulatory Fitness and Performance Program (REFIT)
Common Data Template for Systemically Important Banks (FSB)
Data Gaps Initiative (FSB), Common Reporting (COREP) Template and Inventory of Data
Reporting Requirements (DRR)
Markets in Financial Instruments Directive (MiFID2)
Capital Requirements Regulation & Directive (CCD/CDR IV)
Alternative Investment Fund Managers Directive (AIFMD)
Directive on Undertakings for Collective Investments in Transferable Securities (UCITS)
Solvency II (EIOPA)
Regulatory Agencies
• Office of the Comptroller of the Currency (OCC)
• Federal Reserve Board (FRB)
• Federal Deposit Insurance Corporation (FDIC)
• Securities and Exchange Commission (SEC)
• Commodity Futures Trading Commission (CFTC)
• CPMI-IOSCO Harmonization Group
• House Financial Services Committee (Financial CHOICE Act)
• Senate Banking Committee consolidated audit
• Financial Stability Oversight Council (FSOC) and Office of Financial
Research (OFR)
• Consumer Financial Protection Bureau (CFPB)
• White House: National Economic Council (NEC)
• White House: Office of Science and Technology Policy (OSTP)
• National Institute of Science and Technology (NIST)
• European Central Bank (ECB)
• Financial Stability Board (FSB)
• Basel Committee on Banking Supervision
• European System of Financial Supervision (ESFS)
• European Banking Authority (EBA)
• European Security and Markets Authority (ESMA)
• European Commission (EC): Directorate General for Financial Stability,
Financial Services and Capital Markets Union (DG FISMA)
• European Reporting Framework (ERF)
• European Systemic Risk Board (ESRB)
• European Insurance and Occupational Pensions Authority (EIOPA)
• Single Resolution Board (SRB)

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Data Management Principles

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Principles of Data Management
Content
Infrastructure
Data Quality Governance Integration
1. Executive Air Cover with Visible Support
2. Line of Business Alignment with Commitment
3. Enterprise Wide Ontology stored as Metadata
4. Reverse Engineering of Business Processes
5. Authority via Mandatory Policy
6. Resources for Sustainability
STRATEGY
• Data Strategy
• Cultural Alignment
• Stakeholder Commitment
FORMALITY
• CDO/ODM
• Policy Compliance
• RACI (accountability)
INFRASTRUCTURE
• Data Domains and Mapping
• Identifiers and X-reference
• Conceptual Model/Unified View of
Meaning
• Business Definitions
• Physical Data Models
• Metadata Repository
DQ/CONTROL
• Reverse Engineering
• Data Lifecycle
• Business Requirements to Data
Requirements
• Fit-for-Purpose Quality
Organizational Goals
Data Content Goals Operational Goals
COLLABORATION
• Coordinate with IT
• Align with Control Functions
• Data Flow Forensics
• Technical Integration
GOVERNANCE
• Funding
• Roadmaps and Project Plans
• Metrics and Reporting
• Communication
• Education and Training

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Financial Industry Business Ontology (FIBO)
FIBO is a business conceptual model that
precisely describes financial instruments,
pricing, legal entities and financial processes
(what they are and how they work)
FIBO facilitates data harmonization
across disparate repositories
based on legal meaning and
contractual obligation
FIBO provides structural validation
to ensure completeness,
consistency and allowable values
FIBO feeds analytical processes with
trusted data and powers smart contracts
FIBO is expressed in the W3C standard
(RDF/OWL) for flexible and scenario-
based/inference analysis
FIBO is built on state-of-the-art
collaboration technology and supported
by documented and tested governance

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FIBO – Collaboration Process is OPERATIONAL
Infrastructure for linking users into the
“Build, Test, Deploy, Maintain”
process is fully operational
(generate diagrams from OWL and incorporate changes
from diagrams to OWL)

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FIBO Master and FIBO Release are OPERATIONAL
Unified repository linking all FIBO domain
ontologies has been delivered
(published on spec.edmcouncil.org/fibo)
automated testing and generation of
machine executable FIBO

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FIBO Model Validation Pathway
Tools are now in place to
expedite SME verification of domain models
Foundational Elements
(core components needed to express
financial concepts)
FIBO-Foundations
Business Entities
Financial/Business Concepts
Indices/Indicators
FIBO Content Teams
(organized and validated)
Equities
Corporate Bonds
Interest Rate Swaps
Loan Concepts
Model Validation
(member SME activity ready for rollout
and implementation)
Derivatives
Debt (beyond corporate bonds)
Mortgages
Funds
Rights/Warrants
Pricing
Financial Processes (corporate
actions, issuance, securitization)
DELIVERED Organized and Regular Meetings
Operational Rollout 2017
Continual Enhancement

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FIBO Pilots and POCs to Demonstrate Potential
Regulation W (business rules) – Completed
State Street (unified meaning and classification) – Completed
|-------------------------------------------------------|
CFTC (navigation across multiple counterparties) – 2Q17
25 Member Use Cases (EDW Conference) – April 2017
|-------------------------------------------------------|
FIBO Training & Certification – Planned 2018
FIBO Applications Event – Planned 2018

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FIBO Contributors

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State Street - FIBO Interest Rate Swap Demo
Arthur Keen, Managing Director, Cambridge Semantics

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Business Objectives
• Purpose: Demonstrate Real World Capability
- The practicality of using FIBO to harmonize diverse derivative and entity data
- The usefulness of FIBO for comprehensive reporting and analytics, both traditional and
innovative
• PoC approach: Apply FIBO to operational “In the wild” data
- Implement using a state-of-the-art semantics platform
• Rapid implementation, no coding required
• Project Participants:
State Street Business requirements and operational data
EDM Council FIBO mode and recommended reports/analytics
Cambridge Semantics Operational platform and implementation services
dun & bradstreet Business Entity and Corporate Hierarchy data
Wells Fargo FIBO consultation

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State Street Bank/D&B/EDM Council
FIBO PoC Solution Architecture
Front
Arena
Data
Dun &
Bradstreet
Data
Internal Data Sources
Map & Load (QA) Link & Query (Classification, analytics)
External Data Sources
Derivatives Data
Entity &
Corp. Hierarchy
Data
Reports & Analytics
© 2016 State Street Corporation. All rights reserved. Information Classification: Limited Access16

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