DCAM_Overview.pdf_______________________

Confidential Copyright © 2015 EDM Council Inc.
Page 1
Data Management Capabilities
Assessment Model
(DCAM)

Page 2
• 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)
2
Copyright © 2014 EDM Council Inc.
BCBS 239 IN CONTEXT

Confidential 3
Principle 1: The Governance Mandate
Implications: Establish the RDA framework, fully documented, appropriately
resourced with no organizational barriers and top-of-the-house engagement
BCBS Findings: lack of formal/documented frameworks; need clear owners
with demarcation of responsibility; need coordination of requirements among business
IT and risk; decentralized and undocumented data policies; need better SLAs and
measurement criteria for RDA processes; need higher standard for audit of RDAR
You Ain’t Got No Choice
BCBS 239 – RISK DATA AGGREGATION

Confidential 4
Principles 2,4,6: The Data Infrastructure Mandate
Implications: need integrated data architecture (taxonomies, metadata, identifiers);
need controls across the full data lifecycle; need flexible classification and aggregation;
must support on-demand, ad-hoc reporting and scenario-based reporting
BCBS Findings: inconsistent taxonomies, metadata, identifiers and dictionaries;
inability to harmonize, integrate and compare among repositories; need identification
and definition of CDE; failure to take into account interdependencies between
processes
Data Harmonization is Mandatory

Confidential 5
Principles 3,5,7,8: The Data Quality Mandate
Implications: Risk data must be timely, accurate and comprehensive; must adopt
authoritative sources and the creation of a data control environment; need to align data
to “concepts” for consistency of meaning across the organization; must be able to
generate timely risk reporting across all dimensions of quality and all risk categories
BCBS Findings: Too much reliance on manual processes; insufficient data
reconciliation (root cause analysis and executable business rules); need better control
across lifecycle of data (data inventory, transformation mapping, cross-referencing,
authoritative sources)
Adaptability Based on Scenarios

Control
Data
Environment

Governed
by
policy,
sanc2oned
by

execu2ve
management,
based
on

standards,
harmonized
across
the

lifecycle,
with
clear
accountability
and

monitored
by
audit

Legacy
Environment

Exis2ng
technical
and

opera2onal
environments

• Disparate
data
sets

• Proprietary
interfaces
and
point-‐to-‐point
links

• Mul2ple
repositories
managed
independently

• Inconsistent
formats
and
data
defini2ons

Alignment
to
Meaning

Harmonized
and
precise
data

based
on
contractual
precision

• AFribute
level
business
glossary/business
conceptual
ontology

• Unique
iden2fica2on
and
flexible
classifica2on
scheme

• Harmoniza2on
and
transforma2on
processes
(cross-‐referencing
and

mapping
across
systems)

• Metadata
repository
(administra2ve,
structural,
descrip2ve)

②

Manage
Data
Quality

Fit-‐for-‐purpose
data
without

reconcilia2on
and
transforma2on

• Data
quality
criteria
(all
relevant
dimensions)

• Establish
data
quality
control
points

• Quality
assessment
and
remedia2on
(current
state
analysis)

• Define
business
rules,
thresholds
and
tolerances

• Root
cause
analysis
(trace
to
source)

• Management
of
data
manufacturing
chain
of
supply

③

Technical
Implementa=on

Integrate
data
into
opera2onal

and
produc2on
environments

• PlaPorm
and
authorized
tool
stack

• Messaging
and
distribu2on

infrastructure

• Seman2c
to
logical
data
model

• Logical
to
physical
instan2a2on

• Loca2on
iden2fiers
and
namespace

management

④

Copyright
©
2015
EDM
Council
Inc.

Scope of Work Required to Achieve a Control Environment
6

Simple
Complicated Simplifica=on

Reconcilia2on
of
complex

environments

• Designa2on
of
“authorized
data
domains”

• Defini2on
of
cri2cal
data
elements
(CDEs)

• Documenta2on
of
end-‐to-‐end
data
flows

(compounding
process,
derived
calcula2ons,

risk
and
business
formulas)

①

Confidential
Unique Identification
7
Precise Description
Structured Messaging
Data Lineage
• Document
end-‐to-‐end
data
flows
(compounding
process,
derived

calcula2ons,
risk
and
business
formulas)

• Designate
“authorized
data
domains”

• Define
cri2cal
data
elements
(CDEs)

• Implement
metadata
linkages
(administra2ve,
structural,

descrip2ve)

Data Quality
• Completeness,
coverage,
conformity,
consistency,
accuracy,

duplica2on,
2meliness

• Establish
data
quality
control
points

• Perform
quality
assessment
and
remedia2on
(current
state
analysis)

• Define
business
rules,
thresholds
and
tolerances

• Conduct
root
cause
analysis
(trace
to
source)

• Manage
data
manufacturing
chain
of
supply

Integration
• Align
to
meaning
(business
conceptual
ontology)

• Implement
unique
iden2fica2on
and
flexible
classifica2on

• Manage
harmoniza2on
and
transforma2on
processes
(cross-‐
referencing
and
mapping
across
systems)

• Implement
messaging
and
distribu2on
infrastructure

• Seman2c

defini2on
à
logical
data
model
à
physical
instan2a2on

THE CRITICAL COMPONENTS OF “CONTROL ENVIRONMENT”

Confidential Copyright © 2014 EDM Council Inc. Page 8
• G-SIBs and D-SIBs are engaged in remediation
(G-SIBs spend ~ $230m; D-SIBs are far behind)
• Typically led by Group Risk (leveraging existing initiatives)
• Progress on “foundational governance” (not implemented
across enterprise)
• Most RADR initiatives are focused on adherence
(have not calculated business value/benefits)
• Data architecture (lineage, taxonomies, harmonization)
and reliance on manual processes remain as the big
challenges because of magnitude
• Top remediation priorities (in order): data dictionary/taxonomy,
data quality monitoring, data consistency between risk
and finance, alignment of repositories, process automation
• Remediation challenges are significant (IT architecture and
data flows are complex and require extensive work)
• Over 70% of banks have defined the “high level” requirements
for adherence but have not translated them into specific data
and architecture initiatives
BCBS 239 – CURRENT STATUS

• Dodd-Frank Act Title IV, VII, X, XIV = US framework for regulation of swaps markets, hedge funds, CFPB, mortgage, Volker
• 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)
• 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
Y-14Q (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.
• 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.
Transparency
Capital
Risk/Stress
Harmonization
CORE REGULATORY INITIATIVES

Page 10
Intersection of data management best practice and
the reality of financial services operations
Based on Collective Experience
• Synthesis of research and analysis among practitioners since formation of EDM Council (case
studies, regulatory pressure, collaborative research)
• Socratic method to ensure that DCAM is practical and in line with the core principles of data
management (philosophy meets practical reality)
• Aligns with organizational mandate (practical, works in real world, understandable by non-
specialists, based on collaboration, structured for continual improvement)
• Standard criteria for evaluation of capability … linked to benchmarking … providing evidence of
adoption of control environment
DATA MANAGEMENT CAPABILITY ASSESSMENT MODEL

Page 11
DCAM
Construction
Capability
Sub-Capability
Objectives
Artifacts
Required
Purpose
Statement
(what is and why
important)
Management
Context
(definition and
introduction)
Elaboration
Specific Goals
Core
Questions
Informative
Capability
Type
Data-related (identification,
common language, classification,
data quality, lineage, compounding
process)
Organization-related (governance,
funding, data management program)
Ecosystem-related (IT, operations,
security, privacy, finance)
Organizational

Page 12
• Created based on practical experience
• Capabilities orientation: Not done; In process (low,
medium, high); Capability achieved; Capability enhanced
• Each Category is defined by a set of
Capabilities and Sub-Capabilities
• Each Sub-capability is evidenced by a series
of capability objectives
Data
Management
Strategy
Defines the long term goal of the data management
program. The blueprint to gain internal alignment
among stakeholders and to define how the organization
will approach the management of data content
Data
Management
Business
Case
The justification for the data management program. The
mechanism for ensuring sufficient and sustainable
capital. The approach for measuring the costs and
benefits of EDM
Data
Management
Program
The mechanism for EDM implementation. Stakeholder
engagement. Communications program and education
on the concepts of data CONTENT management.
Engagement model and operational routines
Data
Governance
The rules of engagement for implementation of the data
management program. The focus is on implementation
of policies, standards and operational procedures
necessary to ensure that stakeholders “behave”
Data
Architecture
The “design of information content” including the
identification of data domains, establishment of
taxonomies, alignment with contractual obligations,
documentation of metadata and designation of CDEs
Technology
Architecture
The “design of physical architecture” including the
platforms and tools in support of data management
implementation. This is domain of IT and defines how
data is acquired, stored, integrated and distributed
Data
Quality
Deliver to business users data that is fit-for-purpose.
The goal is data that users trust and have confidence in
to be exactly what they expect it to be without the need
for reconciliation and data transformation
Data
Control
Environment
Coordination of the components into a cohesive
operational model; ensure that controls are in place for
consistency across the lifecycle; align with
organizational privacy and security policies
Component
(8)

Capability
(36)

Sub-‐Capability
(112)

Objec=ves
(306)


Page 13
DATA MANAGEMENT STRATEGY
Blueprint defining the goals of the data management program
and how the organization will approach implementation
Establish the framework
for the data management
program including how it
will be defined,
organized, funded and
governed
Clearly define the goals,
objectives and scope of
the program to ensure
alignment and
commitment from the
critical stakeholders
Articulate how the
program will be
practically implemented
including how it aligns
with business value and
organizational objectives

Page 14
Data Management Strategy Capabilities
DATA MANAGEMENT STRATEGY
❶ Data management strategy is
specified and shared
❷
High level business
requirements are captured
and integrated into the DMS
❸
DMS defines the importance of
defining and appropriately using
“authorized data domains”
❹
DMS is aligned and mapped to
architectural, IT and
operational capabilities
❺
DMS requires the
creation of a formal
governance program
❻
DMS defines how the
data management
program will be
measured and
evaluated
{
DMS calls for the
creation of a
communications and
training program

DATA MANAGEMENT BUSINESS CASE & FUNDING MODEL
The justification for the data management program including
how it is funded and evaluated
Mechanism to ensure
allocation of sufficient
capital and methodology
for cost allocation
Approach used to
measure costs and
contributions from the
data management
program
Rationale for the
investment in data
management including
the costs, benefits, risks
and expected outcomes

Data Management Business Case & Funding Model Capabilities
DATA MANAGEMENT BUSINESS CASE & FUNDING MODEL
The data management business case is aligned to
strategic drivers and tangible business outcomes
❶
The data management funding model has been
established, approved and adopted by the
organization
❷
The funding model can be measured and
evaluated against tangible business objectives
❸

DATA MANAGEMENT PROGRAM
Establish the data management function and ensure that it operates
effectively within the culture of your organization
Clear understanding of
the meaning of data
“content” management
and why it is essential to
the operations of the
organization
Establish an operational
framework to ensure
sustainability and
authority of the data
management program
Establish and confirm
stakeholder engagement
along with the process to
ensure that the data
management program is
implemented

DATA MANAGEMENT PROGRAM
Data Management Program Capabilities
Implement the Data Management
Program Organizational Structure
❷
Roadmaps are developed,
socialized and approved
❸
Stakeholder engagement is
established and confirmed
❹
Communication
Program is designed
and operational
❺
Data Management routines are
established and operational
❻
Establish and Empower the
Data Management Program
❶

DATA GOVERNANCE
Rules of engagement necessary for implementation of the
data management program
Establish the structure
and operating model
needed to establish lines
of authority and integrate
the DMP into the
corporate hierarchy
Define, implement and
enforce data
management policies,
procedures, guidelines
and standards
Measure and monitor the
effectiveness of the data
management program
and ensure sufficient
resources have been
allocated

Data Management Strategy Capabilities
DATA GOVERNANCE
❶ Data governance
structure is created
❷
Thing
Independ
ent Thing
Person
Relative
Thing
Employe
e
Custome
r Pilot
Mediatin
g Thing
(context)
Employm
ent Sales Aviation
Content governance is defined
❸ Policy and standards are
written and approved
❹ Program governance is
operational
❺
Program
governance
controls are in
place
❻
Technology
governance is
aligned
{
Cross-
organizational
enterprise data
governance is
aligned

DATA ARCHITECTURE
Design, management, implementation and maintenance of the
precise meaning of the information
Definition of the precise
contractual meaning of all
data attributes including
how they relate to one
another and their
expression as metadata
Governance procedures
to ensure the
maintenance, control and
appropriate use of data
and its harmonization to
business meaning
Identification of (and
agreement on) the logical
domains of data
as well as location of the
underlying physical
repositories

DATA ARCHITECTURE
❶
Identify the data (logically
and physically)
❷ Define the data (semantically and
structurally)
❸
Govern the data
(establish sustainable
data architecture
governance)
Data Architecture Capabilities

TECHNOLOGY ARCHITECTURE
Physical architecture and how data is acquired, stored,
distributed and integrated across the organization
Strategy, architecture,
roadmaps and
governance on the
physical IT infrastructure
needed to support data
management objectives
Definition of the allowable
tool stacks (i.e. BI, ETL,
storage) needed to
support implementation
of the data management
strategy
Strategies and
approaches to address
operational risk, business
continuity and disaster
recovery

TECHNOLOGY ARCHITECTURE
❶
Technology
architecture
is defined and
governed
❸
Data storage
management
strategy is
defined and
governed
❹ Operational risk
planning is in place
Technology Architecture Capabilities
Data technology
tool stack is
defined and
governed
❷

DATA QUALITY PROGRAM
Approach to ensure that data consumers trust and have
confidence in the data as fit for their intended purpose
Implement data quality
governance mechanisms
and processes for
management of the data
manufacturing chain of
supply
Establish the goals,
approaches, dimensions
and plans of action to
ensure data content
supports the objectives of
the organization
Create a profile of the
current state of data
quality, identify areas that
need remediation and
cleanse against approved
business rules

DATA QUALITY PROGRAM
❶
Data quality
program is
established
❷
Quality of existing stores of
data are identified and assessed
Quality of new data is
monitored, analyzed
and reported
❸
Data Quality Capabilities

DATA CONTROL ENVIRONMENT
Coordination of the components of the data lifecycle across
the data ecosystem of the organization
Objectives and
capabilities of the data
management program
are embraced and
adopted throughout the
organization – creating a
control environment
Identification and
documentation of the
end-to-end data flows
and reverse engineering
of data compounding
processes (formulas,
derived calculations, etc.)
Integration of data
management into the
organizational
“ecosystem” and
alignment with other
enterprise control
functions

DATA CONTROL ENVIRONMENT
❶ A data control environment is
❸
Control environment ensures that the
discipline of data management is
operating collaboratively with cross-
organizational control functions
Data Control Environment Capabilities
❷
A data control
environment
supports the data
management
lifecycle

Page 29
Process Formality Engagement
Not Initiated
Capabilities are not Being Performed
Tactical Ad Hoc Heroes
In Process
(Conceptual)
Capabilities are in their Initial Planning Stages
Issues are under debate White board planning Data practitioners
In Process
(Developmental)
Capabilities are Being Developed
Policies, procedures, standards, roles
and accountabilities are being
established
Meetings are underway
(notes and planning documents)
Stakeholders are identified
(negotiated resources/annual
budgets)
In Process
(Defined)
Capabilities are Defined and Formalized
Policies and standards exist
(roles, responsibilities and
accountabilities are being coordinated)
Routines exist
(structured documentation)
Verified by stakeholders
(business and functional
responsibility/sustainable funding)
Achieved
Capabilities are Achieved and Implemented
Policies and standards are
implemented
(proactive issue management)
Capabilities are embedded into
operations
(standardized methodologies)
Executive management authority
(strategic investment funding)
Enhanced Capabilities are fully integrated into the operating culture of the organization
DCAM Scoring Philosophy

Confidential
1. Confidence Rule: We have an endorsed data
management strategy that is meaningful to business
users
2. Owner Rule: We have a senior executive (with
authority) in charge of the data management program
3. Alignment Rule: Stakeholders understand (and buy
into) the need for the data management program
4. Communication Rule: We do a good job
communicating the value proposition and operational
implications of the data management program
5. Business Case Rule: The business case and funding
model for the data management program is
6. Metrics Rule: We do a good job of measuring the
costs and benefits of the data management program
7. Policy Rule: The data management program has the
authority to enforce adherence to policy
8. Resources Rule: The data management program has
enough resources to be sustainable
9. Accountability Rule: The data management
governance and accountability structures are
operational
10.Responsibility Rule: The roles and responsibilities of
data “owners” and “stewards” are assigned
11. Enforcement Rule: Data policies and standards are
implemented and enforced
12. Ontology Rule: The business meaning of data is
defined, governed and harmonized across repositories
13. CDE Rule: Critical data elements are identified, verified
and managed
14. Data Domains Rule: Logical categories of data have
been defined and catalogued
15. Capability Rule: The data management program is
aligned with technical and operational capabilities
16. Profiling Rule: Data in existing repositories has been
profiled, analyzed and graded
17. DQ Control Rule: Data quality control procedures,
business rules and measurement criteria are operational
18. Root Cause Rule: The root cause of data quality
problems are identified and corrected
19. Lineage Rule: End-to-end data lineage has been
identified across the full data lifecycle
20. Ecosystem Rule: Data management collaborates with
existing enterprise control functions
30
EDM Council’s 20 Rules of Data Management

Page 31
Operational
Efficiency
Trust &
Confidence
Flexible Data
Infrastructure
Commercial
Insight
Sphere
of Value
• Reconciliation & manual processes
• Trade repairs & fails
• Settlement instruction mismatches
• Corporate actions processing
• Redundant systems
• Capital reserve/unproductive capital
• Transformation & integration
challenges
• Risk analysis & reporting
• Compliance requirements
• Derived data, calculations
and analytical models
• Business process automation
• Management & financial
reporting
• Upselling opportunities
• Enhanced customer service
• New product engineering
• Product & client ROI
• Sentiment and contextual
analysis
• Classification and
aggregation capabilities
• Flexible queries
• Data harmonization
• Adaptability
• Extensibility
• Structural data quality
BUSINESS VALUE TO BE REALIZED

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