EDM Council Membership Update
Developments in Enterprise Data Management
Michael Atkin
Managing Director, EDM Council
31 March, 2014
Confidential
Query Layer (SPARQL, SQL, CQL, NQL, Natural Language Processing)
Mechanism for perform structured queries over linked datasets
Data Quality Strategy
Fit-for-Purpose - completeness, coverage, conformity,
consistency, accuracy, duplication, timeliness.
Data Quality Management - profiling, testing, cleansing,
QA process and implementation of business rules
Shared Vocabularies (dictionary,
taxonomies, metadata)
Adoption of common financial
language. Must ensure that
concepts are both precise and
based in legal certainty
Mapping to Common Meaning
Alignment of data meaning from one format to another.
The goal is consistency of meaning across repositories.
This includes mapping of business concepts, mapping of
relationships and format transformation
Identification (LEI, ISIN)
Unique and precise identification
of the financial instruments and
business entities that form the
baseline components of our
industry
Location Layer (URL, URI, DNS)
Web-based location identifiers enable data
to be accessed across the web. Namespace
management is included
Ontology (OWL, UML, 1st order logic,
modal logic)
Representation of data to record the
logical relationship between terms in
the context of business reality
Messaging Layer (XML, CSV, SWIFT, Web Services)
Transactions are communicated to involved parties via messaging.
Examples are FIX, ISO 20022, FpML for derivatives, MISMO for loans,
XBRL for balance sheet data
Corporate
Actions
Copyright 2014 EDM Council, Inc.
Positions and
Transactions
Market Data
and News
Transport Layer (HTTP, TCP/IP, FTP)
Facilitation of computer-to-computer communication
in alignment with W3C and IETF standards
Information Technology and Operations Governance
Management of architectural approach, data distribution platform, transformation processes and
data integration
Analytical Layer
Database Types: instrument, entity, transaction, holdings | Core Processes: issuance, trade, servicing, retirement |
Analytical Inputs: cash flow, obligations base on role performed; ownership/control and positions
[the essential ingredients for scenario-based analysis and ability to understand links/relationships]
Reference Data
Data Management Governance
Management of data management strategy, organizational model, funding model, governance
structure, organizational alignment and operational culture
Data Management Infrastructure
BCBS 239 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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Basel RDA Self-Assessment
•
Banks understand the importance of the Principles and are committed to enhancing
data aggregation and risk reporting capabilities
•
“… many banks are facing difficulties in establishing strong data aggregation
governance, architecture and process (initial stage). Instead they resort to extensive
manual workarounds which are likely to impair risk data aggregation and reporting”
•
BCBS specifically calls out the need to:
–
–
–
Upgrade risk IT systems (emphasis is on harmonization of data definitions across the
information lifecycle)
Improve data quality capabilities (focus on improvements in accuracy, completeness,
timeliness and adaptability in times of stress)
Enhance governance (emphasis on data ownership and accountability for risk data quality)
•
BCBS and FSB “… expect banks identified as globally systemically important (G-SIBs)
to comply with the Principles by 1 January 2016”
•
Basel Committee strongly suggests that “… national supervisors apply the Principles to
banks identified as domestically important (D-SIBs) three years after their designation
as such by their national supervisors”
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RDA Self-Assessment
• Principle Two (data infrastructure)
– Lowest rating among the 11 Principles
– 70% are materially non-compliant on integrated data taxonomies and
glossaries
– 60% are material non-compliant on data production and control
across data lifecycle
• Principle Three (data quality)
– Third lowest rating among Principles
– 40% are materially non-compliant on IT alignment across repositories
(generation of enterprise risk)
– 60% are materially non-compliant on level of dependency on manual
data reconciliation processes
•
Principle Six (adaptability)
– Second lowest rating among Principles
– 83% are materially non-compliance on ability to meet ad hoc requests
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Specific Data Issues and Priorities
Gov.
Finding/Problem
Flexibility: systems are not flexible enough to adapt/perform scenario-based analysis
IT Integration: IT systems are not integrated across linked processes (alignment across repositories)
control across linked processes
Manual Processes: too much dependence on manual processes
eliminate manual reconciliation processes
Data Standards: inconsistent data taxonomies, dictionaries, metadata (inability to harmonize, integrate
and compare) adopt common financial language
Data Policy: decentralized/undocumented data policies and procedures
Framework: lack of formal and documented RDA frameworks
SLA: insufficient service level standards for RDA processes (measurement criteria)
DQ Management Process: Lack of comprehensive data quality policies and procedures (definition of
core data attributes, documentation of production process, quality metrics)
Quality: insufficient data quality reconciliation and management (logic checks, tolerance levels, root
cause analysis, standard dimensions, executable business rules)
Data Lifecycle: control throughout the lifecycle of data (data inventory, transformation mapping, crossreferencing, authoritative sources)
Interdependencies: failure to take into account the interdependencies between processes
X
Copyright © 2014 EDM Council Inc.
DQ
X
X
X
X
X
X
X
X
X
X
Accountability: clear data owners with accountability mechanism (demarcation of responsibility,
coordination of requirements among business, IT and risk)
strong governance with clear accountability
Audit: need higher internal standards for audit of data aggregation and reporting
Documentation: Inadequate documentation/distribution
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Infra.
6
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
2014 Questionnaire (expectation)
•
Governance Principle
–
–
–
–
•
Is the RDAR framework established (clear accountability)
Has it been approved by executive management and fully resourced
Is the framework documented, verified by stakeholders and aligned with capability
Are RDAR limitations (and methods of redress) known at top-of-house
Data Infrastructure Principle
– Are standard identifiers, metadata and taxonomies established and integrated across
the enterprise and for all functions and processes
– Is lineage controlled across the full data lifecycle including integration into existing IT
environments
•
Data Quality Principles
–
–
–
–
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Are core risk concepts (dictionary) consistently defined across the enterprise
Is data reconciled to source systems
Is the balance between manual and automated systems appropriate and justified
Is the data complete (including off-balance sheet content), timely (produced in near
real time), accurate (able to be validated against precise rules), traceable (across
the data production process), usable (without transformation) and adaptable (useful
for on-demand and ad-hoc analysis)
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BCBS Task Force (US and UK)
Activity Summary/Components
Participants
1. Communication (shared): adoption of common
lexicon and shared framework; clarification of
ambiguities and determination of adherence
•
2. Governance (benchmark): Establish governance
program; document and approve policies and
standards; implement operating model; enforce
and audit
OCC/Treasury and Federal Reserve
•
3. Data Dependencies (benchmark): critical risk
functions  critical measurements  critical
business attributes  authorized data domains
 logical data models  physical repositories 
transformation processes
US/CN: AIG, Bank of America, Bank of New
York, Citi, GE Capital, Goldman Sachs,
JPMorgan, Morgan Stanley, Royal Bank of
Canada, State Street, Wells Fargo
UK: Barclays, HSBC, Lloyds, Royal Bank of
Scotland, Standard Chartered
Prudential Regulatory Authority
•
EY, McKinsey, IIF and EDM Council
4. Material Areas (shared): requirements for
resolution, liquidity risk, counterparty risk, CCAR,
loans/MBS, derivatives, credit exposure, trading
exposure, positions, limits, market concentration
5. Measurement Criteria (shared): current to endstate evaluation; incremental deliverables
6. Standard Infrastructure (benchmarking):
internal process for reconciliation of identifiers,
data taxonomies, classification schemes;
(shared) adoption of industry-wide standards and
process for cross referencing/mapping
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BCBS Capability Framework
A common lexicon
►
Governance framework
for data management is
closely aligned to DMM
(include alignment with
risk and finance
governance
mechanisms)
►
There is good alignment
to framework
Development.
(Alignment to risk as
well)
►
Data policy and
standards are addressed
in DMM
►
Financial model (funding)
for RDA is broader than
data, but DMM has
components that will
ensure the data funding
issues are covered
►
Culture and
communication is shared
with risk awareness;
most likely will be driven
by the risk organization
Page 9
►
Nearly all of the Data
Aggregation components
of RDA will be covered in
DMM.
►
Architecture will be shared
with technology
2/13/2014
►
Process controls and security controls are elements of
‘partnership’. These will be the responsibility of other
groups (operations; business continuity; info-security).
These are discussed in DMM, with respect to the
importance of a DM Program being ALIGNED to other
key stakeholders
BCBS Risk Data Aggregation & Risk Reporting Principles Industry Roundtable
►
Data Quality
considerations are a
key component of
the DMM and will be
addressed
Reverse Engineering Flow
BCBS239 Prioritization Approach
Reverse engineer starting with critical risk
functions backwards to data source
CR
Critical Risk
Measurements
Critical Risk
Functions
CBEs
Critical Business
Entities
Persisted Data
Repositories
(Physical)
Data Standardization
Authorized Data
Domains
(Logical)
Need to harmonize data across disparate
physical repositories with clear lineage to
source
Data Flow
Derivatives Transparency
Microcosm of the data challenge: complex instruments; highly customized;
links to underlying instruments; links to entities; multiple participants/roles;
standard identifiers missing; fragmented trading and reporting processes
According to the CFTC
•
“ISDA taxonomy is not granular enough”
•
“Data is not aligned for exotics and
bespoke instruments and doesn’t map to
ISDA taxonomy”
“Complex data compiled from 20 SEFs
on a continually changing basis”
•
“Each department has a different
analytical objective”
•
“Data is all over the place with no
consistency in reporting of values”
•
“Must be able to compare data across
multiple SDRs”
•
“Data is not clean enough to perform
analysis and we can’t reconcile manually”
•
•
“USI alpha swap is not associated with
beta and gamma, can’t link swaps based
on multiple criteria”
“We need a mechanism for mapping,
understanding data relationships and
unraveling process flows”
•
“Can’t unravel trades to analyze notional
amounts .. can’t look at entity roles …
can’t compute cash flows”
•
•
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“Data needs to be standardized and
aligned to common meaning”
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OFR/FRAC (Recommendations)
Risk Subcommittee
Data Subcommittee
•
• Collateral Mapping
Instrument Database
–
–
–
–
Adopt ontology to fulfill legislative obligation
Evaluate FIBO as the instrument database
Follow LEI model of collaboration
Use derivatives as test case of viability (OFR
 CFTC  FSB/EMIR  FSOC collaboration)
– Build and populate map of collateral flow
– Use liquidity map as model
– Mechanism is critical for Identification of
critical roles and obligations to track
• Macro-Prudential Stress Testing
– Current stress testing analysis is for microprudential assessment
– Aggregate CCAR data to fulfill DFA
mandate on macro-prudential oversight
– Use CCAR to assess impact of scenariobased shocks
• Reporting Modernization
– Migrate from report-based to data-centric
reporting
– Working group to create business case and
build inventory of reports/data overlap
– Implement POC (rule-based requirements,
adopt standards, define technical and
operational capabilities)
– Investigate entitlement requirements and
mechanisms to avoid duplicate data collection
Confidential
Research Subcommittee
• Forensic Investigation expertise “on call”
• Accounting expertise on staff (state
contingent cash flow, fair valuation, etc.)
• Systemic implications of insurance sector
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Systemic Risk (what keeps FSOC/ESRB up at night)
•
“Data challenges loom large” (Mary Miller, Undersecretary of Treasury for Domestic Finance)
Issues of Importance
– Short vs. long term funding (degree of interconnectivity and durability)
– Shadow banking (funding without insurance)
– Operational risk (#1 threat to the financial sector)
•
•
•
•
•
•
Relationship between players (degree of complexity and interrelationships; more
connections = higher risk)
Flow of funds (direction, timing, concentration)
Partial disruption = asymmetry = conservative decisions
Cyber-security (very, very, very real threat; most likely malware)
Internal fraud, sabotage, bad contractors
Technology failure (poor design, bad testing, insufficient resiliency)
– Counterparty risk (SSG Report) – “five years after the crisis, progress on timely
and accurate counterparty risk has been largely unsatisfactory”
– Derivatives and complexity of financial system (understanding relationships,
interconnectivity, transitive exposure and contract terms are critical )
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Core Regulatory Initiatives
• Dodd-Frank Act Title IV, VII, X, XIV = US framework for regulation of swaps markets, hedge funds, CFPB, mortgage, Volker
Transparency
• EMIR – European Market Infrastructure Regulation (EU version of Dodd-Frank Title VII on derivatives transparency).
• Regulation AB2 = regulations on asset backed securities (unravel links between loan, tranches, pool, etc.).
• FATCA = individual reporting of foreign accounts and FSI reporting of foreign financial accounts about US clients
• UCITS = Undertakings for Collective Investment in Transferable Securities (EU Directive on simplification of prospectus and
their expression using clear, accessible and standardized data).
• AIFMD – Alternative Investment Fund Managers Directive (EU proposed law to provide more oversight and transparency to
hedge funds and private equity).
• Dodd-Frank Act Title I = the financial stability component (creates Financial Stability Oversight Council and OFR)
Capital Risk/Stress
• EU System of Financial Supervision = establishment of the European Systemic Risk Board (and ESFS)
• Basel Principles for Effective Risk Data Aggregation and Reporting = implementation of a “data control environment” and
healthy “risk appetite framework” within systemically important financial institutions
• Basel III – global regulatory standard on bank capital adequacy, stress testing and market liquidity risk.
• CCAR = Comprehensive Capital Analysis and Review (stress test methodology in the US; CCAR reporting is putting lots of
pressure on data alignment and comparability. This includes the FR Y-9C (Bank Holding Company Capital Report) and FR Y14Q (detailed ‘show your calculation methodology work for BHC). This is the US version of Basel III.
• Solvency II – EU Directive that harmonizes insurance regulation (requirements for capital reserve and reduction of risk of
insolvency) – to be implemented January 2014.
Harmonization
• MiFID II – Revised Markets in Financial Instruments Directive (mostly about trading, but does require common instrument
identification for consolidated pricing).
• ACORD – Insurance standards development body (UK) likely to be mandated as the format for reporting.
• Regulation SCI – SEC proposed Regulation Systems Compliance and Integrity (to ensure that core infrastructure is functional)
• COREP = Common Reporting requirements (developed by Committee of European Banking Supervisors (CEBS) with the goal
of developing a supervisory reporting framework based on common data standards and formats.
• FSB Templates = Common Data Template for G-SIB’s seeking to harmonize the data compounding
methodology for reporting.
Copyright © 2014 EDM Council Inc.
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Data Management Maturity Model
• Harmonized with CMMI/SCAMPI methodology
• Capabilities defined with enriched
evaluation criteria for “capability” and “maturity”
• Alignment with Basel RDA Principles
• Expected Version 1.0 release 2nd qtr ‘14
Data Management Maturity
Model
(DMM)
Category
DM Function
Governance
Data Content Mgt.
Operations
Platform and IT
Process Area
Elaboration
Strategy
Rationale for the data management program and long-term
vision
Business Case
Justification for the investment and funding model
DM Program
Framework for sustainability
Governance Management
Operating model, policies and procedures
Communication
Mechanism to achieve buy-in
Control Environment
Data standards and common language
Data Quality
DQ strategy, measurement and remediation
Information Lifecycle
Lineage, data flow and controls
DM Ecosystem
Entitlement, permit to build, privacy, change management
Architectural Framework
Architectural standards and approach
Integration
Platform, storage, access and release management
DMM Example (Data Management Program)
Definition
A Data Management Program is an organizational function
dedicated to the management of data content …
Purpose
The purpose of a Data Management Program is to
establish a sustainable process …
Introduction
The concept of managing “data as meaning” is not always
well understood. For many organizations …
Goals
• Ensure that the Data Management Program is
established and institutionalized …
• Ensure that the Data Management Program is staffed to
provide sustainable …
Core Questions
• Is the Data Management Function defined in a way that
can be clearly understood …
• Is the Data Management Function aligned with the
strategy and organizational …
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Defines the process area
Establishes why the process
area is important and
describes its intended
purpose
Extensive narrative to help
stakeholders understand the
objectives of the process
area in real terms
Summary of the
management goals for each
process area
Top line questions that
executive management
should be asking about
each process area
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SCAMPI Methodology
•
Maturity Model Principles
– Proven practices (in widespread use with consistent terminology)
– Clarity between “required” concepts (what you are assessed against) vs.
“informative” concept (designed to facilitate understanding)
– Measurement of both “capability” (within a single process area) and “maturity”
(across a set of process areas)
•
CMMI Structure
–
–
–
–
–
•
Practice Statements
–
–
–
–
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Supported by organizational policy
Implemented using standards and standard processes
Defined resource and training requirements
Clear responsibilities and involvement of stakeholders
Control over the process and evidence of adherence
“What” not “how” (does not specify an implementation)
Unambiguous (no vague verbs, only one interpretation, complete statements)
Supported by evidence (must be able to demonstrate capability)
Level progression (capabilities are inherited from previous level)
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Data Quality (interim report)
•
Survey sponsored and performed by DTCC (with help from Element22
and aligned with DMM)
– DQ management is underway across industry (still in early stages, not formalized)
– EDMC seven dimensions are being used as the common language about data
quality (completeness, coverage, conformity, consistency, accuracy, duplication,
timeliness)
– Characteristics of Mature Programs
•
•
•
•
•
•
•
•
Serve business requirements
Focus on core data attributes
Understand end-to-end data lifecycle
Mandate use of authoritative source systems
Apply consistent DQ business rules
Perform root cause analysis and remediate at source
Align with Basel RDA principles
Seek to integrate multiple platforms (operational efficiency)
– Measurement of Data Quality
•
•
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Align with business outcomes (client service, reporting comparability, process breaks,
alignment with risk assessment)
Quantitative measures (breach of SLA’s, vendor performance, internal consistency,
symbology conflicts, duplicate identifiers, activity volume)
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Governance: Three Deadly Sins
1. Maturity: Implementing governance
structure before understanding of what
we are governing (and why)
2. End-User Computing: Tactical, end-ofchain data fixes that become intractable
3. Data as Infrastructure: Failure to
implement the underlying data foundation
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10 Principles of Sustainable Governance
1.
Executive Mandate
6.
Data quality is the goal – but it is dependent on
business requirements and measured against what
users have at stake in their decisions.
Adopting a sustainable data culture and getting
people to behave is impossible without top-of-thehouse mandate.
2.
Serve Business
7.
Capture, verify and re-verify business requirements
to align strategy with operational goals.
3.
Process Understanding
8.
5.
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9.
Education is Essential
Data content management is a new concept for
everyone. It is not the same thing as data
processing management.
Standards
Standards are essential for data harmonization.
They are the key to integration. They will unify the
industry. They are the legacy of the financial crisis.
Authoritative Source
Every reference data element must have a single
authoritative source – and all copies must be
identical to the authoritative source.
Core Data Elements
Not all data elements are created equal. Do the
business forensics to identify critical attributes.
Accountability is mandatory!
Data Meaning
Unambiguous shared meaning of the obligations of
the contract is the key to everything. Learn to love
the word ontology.
Data is a manufactured product and we must
understand how data flows from process-to-process.
4.
Data Quality
10.
Collaboration
Data is foundational. It brings people from diverse
areas together and promotes data collaborative
sharing. This is about creating a shared vision.
Copyright © 2014 EDM Council Inc.
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Legend
2011 & 2013 Industry
DMM Scorecard
Quality
2011
2013
Quality
Assurance
Goals
Corporate
Culture
Data Quality
Framework
Technology
Platform
and
Integration
Strategy
Governance
Model
Architectural
Framework
Funding
Sourcing
Levels
Standards
and
Procedures
Operations
Requirements
Lifecycle
0 – does not exist
1 – project or ad-hoc
2 – business line
3 – enterprise
4 – measured
5 – optimized
Financial Industry Business Ontology
Goal of data harmonization and comparability established
(common financial language aligned to meaning)
Top Line Summary
•
Standard: FIBO Foundations and Business Entities through OMG architecture board
and technical evaluation process (unanimous)
•
RDF/OWL: Documented and streamlined the technical migration from “requirements
modeling” to RDF/OWL (pathway for expedited development)
•
Release Schedule: Development pathway defined and dependencies mapped
•
Repository and Access: Syntactic transformation to import FIBO into Adaptive
Metadata Manager (interface design and access methodology)
•
Model Validation: Process defined for member review and validation (business
meaning and externality review)
•
Use Cases: Substantial progress in demonstrating the various use cases for FIBO
and inference processing (proving the practical application)
•
Technical Strategy: Positioned for long-term collaboration with financial institutions,
academia and semantic processing companies (operational implementation, business
rules, shared semantics, data visualization)
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POC Activities and Use Cases
Ensure FIBO Supports Industry Requirements
•
–
–
–
–
–
–
–
Operational ontology (inference
processing using RDF/OWL)
Intersection of interest rate swaps,
credit default swaps and business
entities
Classification and aggregation
Data validation
Alignment with messaging (FpML
and FIXML
Fact-based queries
Transitive exposure analysis
•
Data Repository Alignment
•
Executable Business Rules
–
–
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•
Derivatives
Regulation W
RuleLog and SBVR
Liquidity Mapping
–
–
–
•
Messaging Alignment
–
–
–
•
Effective rates
Sectorial detail
Core liquidity concepts
FpML for derivatives
MISMO for mortgage securitization
XBRL for COREP
Data Model Integration
–
–
–
IBM/IFW
BIAN
Teradata
•
OFR Instrument Database (common
reference point)
•
Internal Process Improvement
•
Valuation Process Support
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Data Management Implications
•
Adopt standards-based infrastructure (common
financial language is the top priority)
•
Basel BCBS 239 (RDA) obligations are firm (how to
“do it right” vs. “do it quick” given deadline)
•
Scenario-based analysis (on demand, ad-hoc,
adaptability)
•
Degree of interconnectedness is the most important
criteria for financial stability analysis
•
Adopt standard measurement criteria for data quality,
infrastructure alignment and governance
•
Derivatives are the front line of the regulatory data
challenge
•
Collaboration with regulators on priorities and
dependencies (knowledge gap exists)
•
Data flows and process alignment is as important as
data quality
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Contact
Michael Atkin
Managing Director, EDM Council
(o) 301.933.2945
(m) 240.602.8390
atkin@edmcouncil.org
www.edmcouncil.org
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FIBO Development Scenario
Reference Data (product) Semantics
Domain
Sub-Domain
Class
Dependency
X
(all Instruments)
X
Common Debt
Terms
X
Phase 2
Bonds
Listed
Instruments
X
Equities
Phase 3
Common
Concepts
Loans
Common
Concepts
Derivatives
X
(FIBO 1.0) Major Milestones
Indices and
Indicators
Rate Based
Dependent on Indices
X
Credit Default
Dependent on Common Concepts for
Loans, Common Debt Terms and
Indices/Indicators
X
Phase 4
Derivatives
OTC
OMG = in RDF/OWL; Beta = Model Reviewed by SMEs; Model = Modeled in Enterprise Architect; Initial = Not Yet Modeled
X
FX
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Model
X
Business Entities
Common
Concepts
Beta
X
Phase 1
Foundations
OMG
Initial
FIBO Development Scenario
Reference Data (product) Semantics
Domain
Sub-Domain
Class
Dependency
Structured
Finance
Dependent on Bonds and Mortgage
Money
Markets
Includes Repo, Treasury,
Government, Agency, Tax-Free, etc.
Asset
Dependent on Equities, Bonds and
Common Debt Terms
OMG
Beta
X
Commodity
X
OTC
X
Phase 6
Derivatives
Debt
X
Phase 5
Listed
Instruments
X
Mortgage
Collective
Investment
Vehicles
X
Exchange Traded
Other
General Purpose, Construction, Student, Miscellaneous
X
Dependent on Common Concepts for
all Instruments
X
Rights and
Warrants
Dependent on Listed Instruments,
Derivatives and Indices
X
Phase 7
Loans
X
Contracts for
Difference
Derivatives
OMG = in RDF/OWL; Beta = Model Reviewed by SMEs; Model = Modeled in Enterprise Architect; Initial = Not Yet Modeled
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Model
27
Initial
FIBO Development Scenario
Market Data (time and date) Semantics
Domain
Class
Dependency
OMG
Beta
Temporal Component
X
Equity Pricing
X
Debt Pricing and Yields
Debt Analytics
X
Debt Pool Analytics
X
CIV Temporal
Terms
X
Future Phase
Debt Temporal
Terms
X
Loan Temporal
Terms
X
Trading Status
X
Credit Rating
Credit Status
X
Credit Temporal
Terms
OMG = in RDF/OWL; Beta = Model Reviewed by SMEs; Model = Modeled in Enterprise Architect; Initial = Not Yet Modeled
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Model
X
Common Terms
Sub-Domain
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Initial
FIBO Development Scenario
Process Related Semantics
Domain
Sub-Domain
Class
Dependency
OMG
Beta
X
Common Issuance Process Terms
IPO, Other Equity Issue Processes,
Primary Market
Debt/Bonds Issuance
Auction, Syndication, Other Issuance
Processes
Agency
X
Asset-Backed /
Mortgage-Backed
Issuance
X
Equity Issuance
X
Future Phase
Securities
Issuance
X
Non-Agency
OTC Derivatives
Transactions
See OTC Derivatives Terms
X
Trade, Post-Trade, Clearing,
Settlement
X
Securities
Transactions
X
Payments
Processing
Positions and Holding Terms
X
Portfolio and
Holdings
OMG = in RDF/OWL; Beta = Model Reviewed by SMEs; Model = Modeled in Enterprise Architect; Initial = Not Yet Modeled
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Initial
X
Corporate
Actions and
Events
Model
29
Industry
Requirements
FIBO Q1 2014 Development Process
Review
Readiness
with SME
Team
Syntactic
Transformation
FIBO
BCO/UML
Model in
Cameo/VOM
FIBO
BCO/UML
Model in
EA
Happy
EDM Council Determines Next
FIBO SubDomain Release
Not Happy
FIBO Use and Maintenance in Cameo/VOM
Perform Externality
Review
Semantic
Enhancement
Change
or add
No
Validation with
Instance Data
Enhance?
yes
Consistency
Testing/Repairs
Yes
Spiral implementation of enhancements
Refactoring
No
Semantic
Issues
Correction
Pass?
No
Yes
Yes
Submission to OMG
Architecture Board
No
Build/Test OMG
Submission
No
Yes
OMG Public
comments
Changes
30
Development Version: 1/24/14
Change
FIBO BCO
No
Final SME Review