The document discusses the Data Management Maturity (DMM) framework, which defines best practices for data management across five categories: data strategy, data governance, data quality, data operations, and platform/architecture. It describes the goals and key questions for each of the 20 process areas within these categories. For each process area, it provides example work products and functional practices at different maturity levels (performed, managed, defined, measured, optimized). The document is intended as a comprehensive reference for organizations to evaluate and improve their data management capabilities.

2. S No. Topic
1. What is DMM?
2. DMM Framework – An Overview
3. DMM Framework - Detailed
4. Process Area Description
5. Functional Capability & Maturity Definition
6. Process Areas of Category 1 - DM Strategy
7. Process Areas of Category 2 – Data Governance
8. Process Areas of Category 3 – Data Quality
9. Process Areas of Category 4 – Data Operations
10. Process Areas of Category 5 – Platform and Architecture
11. Supporting Processes
12. Infrastructure Support Practices

3.  Intended as a comprehensive reference model for
the state-of-the-practice process improvement
 Defines fundamental business processes of Data
management and specific capabilities that
constitute a gradated path to maturity
 Allows organizations to evaluate themselves
against documented best practices, determine
gaps and improve the management of their data
assets across functional lines of business and
geographic boundaries

4.  Comprises of 20 Data Management Process Areas across 5
major categories
 5 Supporting Process Areas and 3 Levels of Infrastructure
Support Practices
Categories
DM Strategy
Data Quality
Data OperationsPlatform & Architecture
Data Governance
Supporting Processes
Infrastructure Support Practices

5. Category Process Areas
DM Strategy DM Strategy
Communication
DM Function
Business Case
Program Funding
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data Quality Data Quality Strategy
Data Profiling
Data Quality Assessment
Data Cleansing

6. Category Process Areas
Platform &
Architecture
Architectural Approach
Architectural Standards
DM Platform
Data Integration
Historical data archiving and retention
Data
Operations
Data Requirement Definition
Data Lifecycle Management
Provider Management

7. Category Process Areas / Practices
Supporting
Processes
Measurement and Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Infrastructure
Support
Practices
ISP Level 1 – Perform the Functional Practices
ISP Level 2 – Implement a Managed Process
ISP Level 3 – Institutionalize Organizational Standards

8. Each PA composed of
 Purpose – Why an org. wants to implement processes of this
PA
 Introductory Notes – What the org. will accomplish if it
implements the PA
 Goals – What key capabilities of the org. will be when the PA
is implemented
 Core Questions – Use the questions to quickly evaluate if the
PA is achieving the desired results
 Related PAs – PAs that provide inputs, consume outputs or
provide material support
 Functional Practices – What capabilities need to be in place
to successfully achieve the intended results for each maturity
level 1-5
 Example Work Products – Types of work products that would
be produced by successful implementation of the PA

9. Level 1 – Performed
Data managed as per the requirement of each project implementation
Level 2 – Managed
Awareness of the importance of Data as a critical infrastructure Asset
Level 3 – Defined
Data treated at organizational level as critical to the success of
its mission and performance
Level 4 – Measured
Data treated as a competitive advantage
Level 5 – Optimized
Data is seen as critical for survival in a dynamic and competitive market

10. Deep Dive into the Process Areas of
each Category
CATEGORY 1 : DM Strategy

11. DM
Strategy
•Focus on development, strengthening and enhancement of Enterprise DM Program
•Define DM Vision, Goals & Objectives and align it with Org. business goals and objectives
•Ensure that the relevant stakeholders are aligned to the program’s implementation
Commu
nication
•Importance of Bi-directional Stakeholder Communication
•Planned approach to facilitate continued collaboration among stakeholders
•Determine types and frequency of program information via multiple channels
DM
Functio
n
•Effectively scope, plan and resource DM activities as a sustained, continuous function
•Develop Strong Leadership
•Inculcate a shared stakeholder approach to DM Roles and Responsibilities
Business
Case
•Helps Org. to frame, justify and gain approval for DM Initiatives based on the scope and
plan created for the DM Strategy
Program
Funding
•Addresses the development and ongoing justification of funding
•Funding model employed for the DM program and its component projects
•Provide appropriate funding for phased, sustained DM improvements

12. An Effective DM Strategy defines why the Org. is implementing a DM program,
explains what the overall program aims to achieve and identifies how the
various components of the initiative fit together. A functional DM strategy should
be developed collaboratively and approved by all stakeholders.
 A Current State Assessment including capability gap analyses
and identifying key dependencies provide a foundation for
buy-in to the strategy and the corresponding plan for the
implementation
 The DM Strategy defines the overall framework of the
program and usually consists of
› A Vision Statement e.g. goals & objectives ; core operating principles;
priorities
› Program Scope e.g. Including both key business areas (Customer
accounts) and DM priorities and key data sets
› Business Benefits
› Selected DM Framework & how it will be used
› Major gaps identified in the current state based on a DM assessment

13. Goals
1. Establish,
maintain and
follow a DM
Strategy
approved by all
the relevant
stakeholders
communicated
across the org
and reflected in
architecture,
technology and
business planning
2. Maintain the
DM Strategy for all
business areas thru
data governance
3. Develop, moni-
tor and measure
the plan for guid-
ing the DM
program imple-
mentation
CoreQuestions
1. Do Executive
Stakeholders visibly &
actively support the DM
strategy?
2. Is the DM roadmap
aligned with business
priorities and milestones?
3. Is there sufficient
understanding and agree-
ment among executives
and operational, IT and
business stakeholders to
support a long term
sustainable DM program?
4. How are projects
aligned with the roadmap
that guides the imple-
mentation of the DM
program?
5. Are resources in place
to architect, design & lead
the DM program and train
to enable the desired
maturity? RelatedPAs
1. Business Case PA
2. Communications PA
3. Architectural
Standards PA
4. Data Lifecycle
Management PA

14. Level1:Performed
1.Docume
nted
Business
Objectives
2. DM
Objectives
, priorities
and scope
for a
project
3. Report
on DM
outcomes
vs
Objectives
for a
project
Level2:Managed
1. DM
objectives and
corresponding
metrics
2.DM scope
Definition
3. Subject area
mapping to
functions that
create, update
and delete
data
4. Approved list
of DM priorities
5. DM priorities
mapped to
business
objectives
6. Project
Prioritization list
7. Capability
enablement
Sequence plan
Level3:Defined
1. DM Stategy
2. List of DM
objectives &
Priorities
3. DM Policies
4. Stakeholder
participation
and approval
docs
5. DM program
scope docu-
mentation
6. DM strategy
sequence plan
7. DM program
metrics
8. DM program
Cost benefit
analysis
9. DM program
reviews
10. DM Strategy
Dashboard
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Metrics
based DM
Program
Reports
2. Plan &
document
ation for
monitoring
emerging
industry or
regulatory
requireme
nts
3. DM
Policies
1.External
publications
& PPTs about
best practi-
ces at
industry
2.Comparativ
e analysis
reports of
best
practices

15. Goals
1. DM Communi-
cation Strategy
ensures that the right
messages about the
program are under-
stood by the right
people at right time
2. Industry or
regulatory guidance
that impacts data
management is
promulgated
internally in a timely
manner
3. Stakeholders
participate in the
development of DM
communications
CoreQuestions
1. How are policies,
standards and
processes for DM
promulgated?
2. How does the org
keep stakeholders
informed about DM
plans and projects?
3. How is bi-
directional
communication
accomplished
among business, IT,
DM and executive
management about
DM priorities,
approaches and
deliverables?
RelatedPAs
1. DM Strategy PA

16. Level1:Performed
1.Commu
nications
are
managed
locally e.g.
Announce
ments,
emails ,
meeting
notes or
web portal
Level2:Managed
1.Communicati
on Policy
2.Announceme
nts, emails ,
meeting notes
or web portal
3.Communicati
ons Strategy
4.Communicati
ons Examples Level3:Defined
1.Communicati
on Policy
2.Announceme
nts, emails ,
meeting notes
or web portal
3.Communicati
ons Effective-
ness Metrics
4.Communicati
on Plan
5. Peer feed-
back about
communicatio
n
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Changes
to DM
communic
ation plans
linked to
communic
ations
effectiven
ess metrics
2.Regulato
ry corres-
pondence
i.e.
responses
to inquiries,
reports
and
memos
1.External
communicati
ons
2.Impacted
public
policies and
industry best
practices

17. Goals
1. Establish and
follow role
definitions,
responsibilities,
authorities and
accountability to
decisions and
interactions related
to DM.
2. Establish the
process for
executive oversight
of data manage-
ment to ensure
adoption of
consistent policies,
processes and stds
3. Align the DM
function to data
governance on DM
priorities and
decisions.
4. Develop, evaluate
& reward DM mgmt
staff
CoreQuestions
1. Is the DM function
defined such that it is
clear to all relevant
stakeholders?
2. Is the DM function
aligned to DM
strategy through
metrics and
measures?
3. What role do
executives play in the
design and oversight
of the DM function?
RelatedPAs
1. DM Management PA
2. Program Funding PA
3. Governance
Management PA
4. Data Lifecycle
Management PA

18. Level1:Performed
1.DM
Resourcing
and
oversight
are event-
driven e.g.
.Project
communic
ations,
meeting
min
2.Assignm
ent of
data
related
roles to
projects
Level2:Managed
1.Policies
2.Process
documents
3.Program
guides
4.Defined Roles
and
Responsibilities
5. Metrics
related to the
DM function
6. Function
review notes or
report
7. Lessons
learnt
document
Level3:Defined
1.DM function
documentation
2.DM Structure
3.Training
records
4.Compliance
or audit reports
5. Project
reports
6. Governance
oversight plan
7. Communi-
cation plans
and schedules
8. Definition of
roles, responsi-
bilities
9. Performance
measures
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.DM
Structure
2.Performa
nce
measures
3. Record
of
changes
to DM
structure
1.Resource
plans
2.Priorities
aligned with
strategy
3. Strategic
decisions and
supporting
metrics

19. Goals
1. Obtain executive
sponsorship for the
DM program
2. Stakeholders
approve and adopt
the business case
across lines of
business.
3. Business cases
justify and help to
ensure sustainable
financing for DM
initiatives
4. Business cases for
DM are comparable
to approved
business cases for
other org-wide
investments
CoreQuestions
1. How does the org.
determine the level of
investment required
for the DM program?
2. How does the org.
decide whether to
develop one
umbrella business
case or multiple,
linked business cases?
3. What are the
success criteria for the
business case?
4. Who needs to be
involved and who
needs to approve?
5. Does the business
case reflect the
objectives and
priorities of the DM
strategy and
sequence plan?
6. Does the business
case reviewed and
approved by DM
sponsors?
RelatedPAs
1. DM Strategy PA
2. Program Funding PA
3. Governance
Management PA
4. Data Lifecycle
Management PA

20. Level1:Performed
1.A
Business
Case is
develope
d for
project
initiatives
e.g. 1.
Project
document
ations,
meeting
min,
discussion
document
s
2.Project
level
business
case
Level2:Managed
1.Business case
standard
methodology
2.DM Business
case initiatives
3.Documentati
on or notes of
business case
approvals and
rejections
Level3:Defined
1.DM business
cases are
defined and
consumed by
all stakeholders
2.Approval
documentation
for DM business
cases & DM
TCO and
methodology
3.Cost benefit
analysis results
for DM
4. DM business
case perform-
ance metrics
5. Traceability
matrix for DM
TCO
6. Process to
collect info on
DM costs &
allocation
methodology
Level4:Measurednge
Level5:Optimized
Functional Practice Statements & Example Work Products
1 TCO mgmt
reports
2.Program
change
recommend
ations based
on cost
metrics
3. Infra
budgets
4. DM TCO
metrics
5. DM TCO
methodolog
y doc.
6.DM TCO
change
recommend
ation
7. Audit
results & Perf
scorecard
1.Proposed
changes and
updates to
DM TCO
model
2.Published
industry
articles, white
papers,
conference
sessions
3. Predictive
analysis tools
and models

21. Goals
1. Priorities and
criteria for both
discretionary and
non discretionary
investment are
established and
followed
2. Sustainable
program funding
methods for making
cost and benefit
allocations,
managing
expenditures and
establishing priorities
are defined and
followed.
3. Program funding
reflects business
objectives and
organizational
priorities
CoreQuestions
1. Is there an
approved set of
investment criteria
and priorities for DM?
2. How does data
governance provide
oversight for DM
funding?
3. Was the program
funding approach
developed,
evaluated and
approved by relevant
stakeholders?
4. Does the funding
model reflect the
org’s business models,
priorities and financial
decision processes?
5. Are there defined
Cost benefit
allocation methods,
expense mgmt
practices and
business cases across
the org?
RelatedPAs
1. Business Case PA
2. DM Strategy PA
3. Governance
Management PA

22. Level1:Performed
1.DM
project
budgets
2.DM
funding
approvals
3. Funding
requests
that incl
cost
benefit
analysis
Level2:Managedg
1.DM Business
cases
2.DM Program
funding
method
3.DM Budget
4.Mgmt reports
on mapping of
DM costs to the
overall
pgm,business
unit and
projects
5.Governance
documentation
related to
funding
Level3:Defined
1.DM funding
criteria
2.Budget
planning
process
3.Metrics to
measure
investment and
funding
objectives
4. Documented
DM funding
model
5. Reports
measuring DM
benefits
6. Prioritization
criteria and
mapping to DM
strategy Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1 Metrics
and analysis
of program
funding
effectivenes
s
1.Public
presentations
or white
papers about
DM funding
2.Approved
changes to
program
funding
based on
predictive
analysis
3.
Presentations,
white papers,
articles etc

23. Deep Dive into the Process Areas of
each Category
CATEGORY 2 : Data
Governance

24. Governance
Management
•Addresses the processes that facilitate collaborative decision
making
•Implement effectively the building, sustaining and compliance
functions of governance bodies
Business
Glossary
•Helps an organization to achieve a common understanding and
representation of an expanding compendium of approved
business terms
•Prioritize and sequence the development of the business terms,
manage their creation and changes over time
Metadata
Management
•Provides top-down approach to architecting, planning,
populating and managing the metadata repository to fully
describe the organization’s data assets

25. Effective Data Governance management provides oversight, ensures
stakeholder collaboration ond facilitates decisions for critical data subject areas.
It addresses three basic data governance functions supporting the org’s data
assets – building, sustaining and compliance
 Building is the creation of new capabilities
 Sustaining consists of the processes for collaboration,
evaluation and decision making
 Compliance is instituted and managed to control data assets
 Key Functions of data governance are
› Approve the enterprise data strategy, policies & Stds
› Define business terms by subject areas
› Assign accountabilities and responsibilities
› Develop decision rights and change mechanisms
› Address regulatory and other external requirements, data security and
access
› Enforce Compliance

26. Goalsall
1. A Process is
established and
followed for aligning
data governance
with business
priorities e.g.
ongoing evaluation
and refinement to
address changes in
the business like
adding new
domains and
functions
2. Data Governance
ensures that all the
relevant
stakeholders are
included and roles
and resp. are
defined clearly.
3. Compliance and
control mechanisms
with appropriate
policies, processes
and stds followed
CoreQuestions
1. Does it facilitate
collaboration and
decision making
across business & IT
functions?
2. Does it clearly
define responsibilities
and accountability
for data domains?
3. Does it provide a
mechanism for
definition of priorities
and resolution of
competing priorities?
4. Does it effectively
provide a process for
defining, escalating
and resolving issues?
5. How does the
executive sponsors
support and how are
they informed of the
efforts ?
6. Does the org have
a process to review
the activities?
RelatedPAs
1. Data Lifecycle
Management PA
2. DM Strategy PA
3. Data Management
Function PA
4. Communications PA

27. Level1:Performed
1.Governa
nce
Process
Document
ation
2.Evidence
of imple-
mented
governan
ce proc-
esses
3. Descri-
ption of
data
governan
ce roles
and
responsibili
ties
Level2:Managed
1.Data
Governance
Charter
2.Data
Governance
Charter
3.Documented
processes and
stds incl.
decision
process, issue
resolution and
operations
4.Roles,
responsibilities
and
accountability
matrix
5.Meeting Min.
Level3:Defined
1.Executive
level data
governance
charter
2.Org-wide
data
governance
rollout plan
3.Metrics to
evaluate the
effectiveness of
data
governance
4. Adoption of
policies and
processes
5. Training
materials
6. Meeting Min.
7. Reports of
decisions and
action items
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1 Metrics
based
analytical
performanc
e reports
2. Executive
reports of
governance
effectivenes
s
1.Internal ppt
or white
papers on
data
governance
model as an
industry best
practice
2.Reports on
continuous
governance
improvement
s

28. Goalsall
1. The language of
Data is
unambiguously
aligned with the
language of the
business.
2. The Org has
created a
comprehensive,
approved business
glossary.
3. The Org. follows
the stds for naming,
definitions and
metadata of
business terms.
4. Org-wide access
to the business
glossary leads to a
common
understanding of
business terms.
5. Consistent
application of
business terms as
new projects come
CoreQuestions
1. Is there a policy
mandating the use of
business glossary?
2. How are the
glossary created,
approved, verified
and managed?
3. Are the business
terms referenced in
the design of data
stores and
repositories?
4. Does the org
perform cross-ref and
mapping of specific
business terms to
standardized ones?
5. Is it accessible to all
and how is it
enhanced and
maintained ?
6. Is compliance
process in place to
ensure that BUs and
projects correctly
apply business terms?
RelatedPAs
1. Data Lifecycle
Management PA
2. Meta Data
Management Function
PA

29. Level1:Performed
1.Defined
business
terms in
project
document
ation
2.Business
glossary
maintaine
d by a BU
3. Business
terms and
logical
attribute
mapping
Level2:Managed
1.Business
Glossary
2.Business
Glossary Policy
3.Business
Glossary
management
process
4.Business
Glossary
available
online
5. Business
Glossary
Compliance
process
6. Data
Requirements
documentation
using business
terms
Level3:Defined
1.Business Terms
Glossary
2.Mapping of
business terms
to attributes to
physical data
elements
3.Business Terms
compliance
process
4. Policy on the
use of std
business terms
5. Business
terms metrics
6. Compliance
monitoring &
Business NC
and exception
report
7. Business
Glossary
update log
8. Impact
assessment
results
Level4:Measuredsin
Level5:Optimized
Functional Practice Statements & Example Work Products
1 Metadata
repository –
unified
business
terms
glossary
integrated
with logical
and physical
data and ref
to industry
stds
2. Metrics
and analysis
reports
3. Published
exception
reports,
impact
analyses
and
remediation
plans
1.Business
rules and
ontologies
associated to
business
terms in
automated
mechanism
2.White
papers and
case studies

30. Metadata is a category of information that identifies, describes, explains and
provides content, context, structure and classification related to Org’s data
assets and enables effective retrieval, usage and management of these assets
 Effective metadata management and the creation of the
Org’s metadata catalog facilitates, supports and contributes
to achievement of critical data management activities and
objectives
 Metadata contains 3 Categories
› Business Metadata e.g. Taxonomies, Ontologies, business glossaries and
standards
› Technical Metadata e.g. Run-time or dynamic metadata like XML,
messaging and config. Information and Design-time or static metadata
like physical data models, DDLs, Data Dictionary and ETL scripts
› Operational Metadata e.g. Process metadata like process steps for
production and maintenance, data quality measurement and analysis,
Business Rules, names of systems, jobs and programs as well as
governance , regulatory and other control requirements

31. Goalsall
1. Management
appreciates the
value of metadata.
2. Data governance
oversight directs the
development and
implementation of
the metadata
strategy,
categorization and
stds and ensures its
adoption and
consistent use.
3. Contents of the
metadata repository
span all categories
and classification of
data assets and
reflects the
implemented data
layer of Org.
4. Internal and
relevant external
stds are
incorporated into
metadata
CoreQuestions
1. Is the metadata
strategy aligned with
internal and the
selected external
stds?
2. How is the scope of
metadata addressed
for inclusion within the
metadata repository
defined?
3. Are all relevant
stakeholders involved
in defining metadata
categories and
properties?
4. What is the method
for developing and
evaluating metadata
stds and processes?
5. What is the method
for maintaining the
metadata repository?
6. Are Roles and Resp.
defined for the
capture, updating
and use of
metadata?
RelatedPAs
1. Governance
Management PA
2. Data Management
Function PA
3. Data Management
Lifecycle PA
4. Data Requirements
Definition PA
5. Architecture Approach
PA
6. Business Glossary PA
7. Data Integration PA

32. Level1:Performed
1.Metadat
a
repository
or virtual
metadata
repository
Level2:Managedn
1.Metadata
Management
Policy
2.Business
Metadata
3.Metadata
repositories
4.Metadata
Meta Model
5. Metadata
governance
and
publication
approval
documentation
6. Metadata
standards
7. Audit results
8. Metadata
change log
Level3:Defined
1.Metadata
management
strategy
2.Metadata
roles and
responsibilities
3.Repository
reports of
metadata
extensions
4. Metadata
management
org stds
5. Metadata
meta model
diagrams
6. Gap analysis
results compar-
ing imple-
mented plat-
forms against
metadata
7. Metadata
metrics reports
& progress
reports
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Document
ed quant-
itative
objectives
for meta-
data
2.Comprehe
nsive meta-
data repo-
sitory reports
3.Measurem
ent appro-
aches to
include
statistical
and other
quantitative
techniques
4.Process
efficiency
reports
5.Unified
metamodel
6. Stds and
practices
1.Consistent
reporting
based on std
definitions
2.Results of
analysis of
repository
information
3.Document
ation of
prediction
models
4.Define
Quantitative
objectives
5.Impact
analysis
reports

33. Deep Dive into the Process Areas of
each Category
CATEGORY 3 : Data Quality

34. Data
Quality
Strategy
•Describes activities designed to help the org. develop a defined,
approved and integrated plan to ensure that the quality of data
meets business needs
Data
Profiling
• Activities that help the organization to assess the data under
management against a set of quality objectives which are defined
in the Data Quality Strategy
Data
Quality
Assessme
nt
•Activities that help the organization to assess the data under
management against a set of quality objectives which are defined
in the Data Quality Strategy
Data
Cleansin
g
•Achieving efficiencies and successful, repeatable processes for
Data Cleansing activities reduces effort and lowers costs enabling
the org. to assure ‘fit for purpose’ data assets across its data assets
and physical data stores

35. Data Quality Strategy defines the goals, objectives and plans for improving data
integrity. Data Quality Strategy addresses data store design, business process
and aligned to the target data architecture and reduce ROT (redundant,
obsolete and trivial) information in the data.
 Strategy created based on an analysis of existing quality issues
and business objectives for trusted data.
 High quality of data is not the result of technologies alone but
also the result of continued scrutiny shared and communicated
by all the stakeholders
 To achieve a data quality culture, an org. should develop a
comprehensive measurable strategy applicable across all
business units, business processes and applications.
 Measurement criteria to be defined for each of the dimensions
of quality like
› Accuracy
› Completeness
› Coverage
› Conformity
› Consistency
› Duplication
› Integrity
› Timeliness

36. Goals
1. Data quality
strategy
collaboratively
developed with lines
of business aligned
with business goals.
2. Priorities and goals
translated into
actionable criteria.
3. Org-wide data
quality program
defined and roles
and responsibilities
established to meet
program needs.
4. Data quality
processes are
integrated and
aligned with the
data quality strategy
CoreQuestions
1. Is data quality
emphasized in all
initiatives involving
the data stores?
2. How does the org.
measure data quality
program progress?
3. Org unit made
responsible for
maintaining the data
quality strategy and
initiatives?
4. Is the strategy
widely distributed,
communicated and
promulgated?
5. Does it clearly
describe objectives,
policies and
processes?
6. Is it integrated with
the systems
development
lifecycle and business
process improvement
efforts?
RelatedPAs
1. Data Requirement
Definition PA
2. Data Management
Strategy PA
3. Data Quality
Assessment PA
4. Data Profiling PA
5 Data Cleansing PA

37. Level1:Performed
1. Data
Quality
Plans,
criteria
and rules
2. Meeting
notes
3. Status
updates
4. Metrics
5. Data
quality
processing
document
ation
6. Rules
implement
ed in
Database
and S/W
document
ed as
requireme
nts
Level2:Managed
1.Data Quality
Strategy
2. Data Quality
sequence plan
with key
milestones
identified
3. Policies,
processes and
guidelines
Level3:Defined
1. Data Quality
strategy
approvals
2. Data
management
stds providing
criteria and
guidelines
3. Approved
policies and
processes
4. Approved
metrics
5. Embedded
SDLC data
quality
processes
6. Business rules
organized
around subject
areas
7. Standard
data quality
processes
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Approved
changes to
the data
quality
strategy
2.Approved
changes to
policies,
processes
and metrics
3.Std metrics
based on
analytical
reports of
data quality
progress
4. Approved
modification
s to the
strategy, seq
plan,
supporting
policies,
processes
and plans
1. PPTs,
whitepapers
and articles
communicati
ng best
practices for
data quality
strategy

38. Goals
1. A standard set of
methods, tools and
processes for data
profiling is
established and
followed.
2. Produce
recommendations
for improving the
data quality
improvements to
data assets.
3. Physical data
representation is
factual,
understandable and
enhances business
understanding of the
set of data under
management.
CoreQuestions
1. Does the org. have
a standard method
for profiling data?
2. Has the org trained
or acquired staff
resources with
expertise in data
profiling tools and
techniques?
3. Does the org. apply
statistical models to
analyze data profiling
reports?
4. Do policies and
processes specify the
criteria for a data
store to undergo
profiling?
5. Is data profiling
scheduled based on
defined events,
considerations or
triggers? RelatedPAs
1. Business Glossary PA
2. Metadata
Management PA
3. Architectural Standards
PA

39. Level1:Performed
1. Data
Profiling
reports
2. List of
data
profiling
checks
Level2:Managed
1.Data profiling
methodology
documentation
2. Approved
data profiling
plan and
schedule
3. Data profiling
findings reports
and metrics
4. Proposed
business rule
additions
based on data
profiling
5. Defined skill
set and training
plan for staff
with data
quality
responsibilities
Level3:Defined
1. Data profiling
stds incl. criteria
for processes,
stds, best
practice
criteria,
tailoring and
reporting
formats
2. Data profiling
methodologies
tailored to org
stds
3. Traceability
of data
requirements
with content
4. Metrics
5. Data related
decisions and
rationale
6. Std Data
profiling tools &
baselines
7. Business &
technical
impact analysis
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Document
ed profiling
methodolog
y, best
practices
and stds
2.Reports on
profiling
results
3.Dashboar
ds,Scorecar
ds or other
decision
support tools
for data
quality and
data
profiling
4. Portal
displaying
data quality
models and
results used
for
performanc
e baselines
1.Log of
stakeholders’
usage of
profiling
results
2.Control
charts
showing
stabilized
processes
3.Data
profiling
improvement
s included in
strategies,
programs
and reports
4.Real time
data profiling
reports
generated
on schedule
5.
Conclusions
drawn from
data profiling
analyses

40. Goals
1. Establish & sustain
a business driven
function to evaluate
and improve the
quality of data
assets.
2. Standardize data
quality assessment
objectives, targets
and thresholds as
per industry
techniques.
3. Establish methods
for statistical
evaluation of data
quality.
4. Establish std data
quality assesment
reporting utilizing
scorecards,
dashboards and
other analytical
reports
5. Utilize the results
and conclusions of
data quality
assessments
CoreQuestions
1. Are std quality
assessment
techniques and
methods
documented and
followed?
2. How are data
quality assessments
conducted and are
they scheduled or
event driven?
3. Are std data quality
rules developed for
core data attributes?
4. Are data quality
rules engines or
assessment tools
employed?
5. Are the business,
technical and cost
impacts of data
quality issues
analyzed and used as
input to data quality
improvement
priorities?
RelatedPAs
1. Data Quality PA
2. Data Profiling PA
3. Metadata
Management PA

41. Level1:Performed
1. Data
Quality
Rules
2. Data
Quality
Assessmen
t results
Level2:Managed
1.Documented
objectives,
targets and
thresholds.
2. Documented
data quality
dimensions and
attributes
3. Metrics for
data quality
assessments
4. Documented
analysis of
business and
technical
impacts
5. Effort
estimates for
data quality
improvements
6. Business
stakeholders
review data
quality
assessments &
provide
recommendati
Level3:Defined
1. Documented
scores, targets
and thresholds
for each std
data quality
dimension
2. Published
and accessible
org level data
quality rules for
approved
attributes
3. Org level
data quality
assessment
policy
4. Std org level
data quality
assessment
processes
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Audit &
Control
reports
2.Data
Quality
assessment
progress
reports for
improvemen
ts
3.Data
quality
confidence
surveys
1.Assessment
analysis
reports
2. Process
review
documentati
on
3. Process
improvement
proposals
and
approvals

42. Goals
1. A Data Cleansing
strategy has been
created and is
consistently
followed.
2. Standard data
cleansing processes
are established and
sustained.
3. Data cleansing
standards are
consistently verified
by all stakeholders.
CoreQuestions
1. Does the org have
a reusable set of data
cleansing processes
(automated and
manual) to resolve
data quality issues?
2. Is there a defined
process for verifying
corrections and
assessing
effectiveness?
3. How does the org
cleanse duplicate
records?
4. Are corrections
implemented at the
source of capture?
5. Are data cleansing
followed through to
analysis of root
causes?
6. Does ROI
incorporate data
cleansing costs?
7. Are consistent
toolsets used?
RelatedPAs
1. Data Requirement
Definition PA
2. Data Quality
Assessment PA
3. Metadata
Management PA
4. Provider Management
PA

43. Level1:Performed
1. Data
Cleansing
requireme
nts.
2. Data
Cleansing
guidelines.
Level2:Managed
1.Data
Cleansing
policy.
2. Data
Cleansing
processing and
rules.
3. Data
Cleansing
metrics.
4. Data
Cleansing
plans.
5. Data
Correction
methodologies.
6. Data
Cleansing
issues
Level3:Defined
1. Data
change history
log.
2. Traceability
matrix
3. Data
cleansing
feedback
4. RACI matrix
for data
cleansing
governance,
activities and
rule
development
5. Data
Cleansing
results report
templates
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Service
level
agreements
2.Feedb
ack
documentat
ion
1.Meeting
Min. showing
involvement
in stds.
2. SLAs
include
cleansing
processes
and
expectations
for data
providers

44. Deep Dive into the Process Areas of each
Category
CATEGORY 4 : Data Operations

45. Data
Requirements
Definition
•Contains practices which ensure that specifications for data
used by a business process satisfy business objectives, are
validated by stakeholders, prioritized and well documented
through a repeatable process
Data Lifecycle
Management
•Assists an organization to ensure that its data flows are well
mapped to business processes through all lifecycle phases
Provider
Management
•Describes best practices for data source selection and
controlled, bidirectional interactions with internal and external
providers

46. Data Requirement Definition established the process used to identify, define,
prioritize, document and validate the data needed to achieve business
objectives. Data Requirements should be stated in business language and
reuse the approved and standard business terms as it is essential for
effectively sharing data across the organization.
 Data Requirements Definition process is an important
contributor to the enrichment, validation and creation of
business glossary terms and definitions.
 Method chosen for defining and documenting data
requirements should be in alignment with application
lifecycle processes.
 Requirement Definition follows an orderly discovery and
decomposition process that includes articulation of business
concepts and needs.
 Business Rules are developed in parallel with the logical
design of the applications supporting the destination data
store.

47. Goals
1. Data requirements
definitions
consistently satisfy
business objectives.
2. All relevant
stakeholders have a
common
understanding of
data requirements.
3. Approved
standards are
followed for data
names, definitions
and representations
in requirements
definitions as
appropriate.
CoreQuestions
1. How are business
and technical data
requirements
solicited, captured,
adjudicated and
verified with
stakeholders?
2. How are the data
requirements
mapped to the
business objectives?
3. How are approved
data requirements
validated against
standard data
definitions as well as
logical and physical
representations?
RelatedPAs
1. Data Lifecycle
Management PA
2. Data Profiling PA
3. Data Management PA
4. Governance
Management PA
5. Data Management
Function PA
6. Architectural Standards
PA

48. Level1:Performed
1. Catalog
of business
terms and
their
definitions
2. Data
Requireme
nts
Document
ation
3. Review
Board
meeting
notes
4.Docume
nted
stakeholde
r
requireme
nts review
decisions
Level2:Managed 1.Data
Requirements
specification
document.
2.Requirements
mapping to
business
objectives.
3.Requirements
mapping to
data models
4. Stakeholder
requirements
approvals
5. Review
board notes
and decisions.
Level3:Defined
1. Std . Data
requirements
template
2.Requirements
mapping to use
case
documentation
3.Requirements
mapping to
business
processes
4. Documented
data security
and
entitlement
rules
5. Stakeholder
or review
board
consensus
documentation
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Std toolset
to maintain
mapping
and
traceability
between
business and
data
requirement
s
2.Selection
criteria for
adoption of
industry best
practices for
the data
requirement
s definition
framework
1.Recommen
dation to
improve data
requirement
processes.
2. Decisions
to change
data
requirement
processes
3. Public ppts,
articles and
white papers

49. Goals
1. The data lifecycle
pertaining to
selected business
processes is defined
and maintained to
reflect changes.
2. Business processes
are mapped to data
flows based on a
framework for
identifying and
prioritizing shared
data flows; this
mapping extends
through the data
lifecycle at the
attribute level.
3. Mapping of data
impacts,
dependencies and
interdependencies
are defined and
maintained.
CoreQuestions
1. What activities,
milestones and
products are defined
for mapping business
processes to the data
created and
maintained in support
of these processes?
2. Has the org.
established clear roles
and responsibilities for
creating and
maintaining a
mapping of business
processes to data?
3. Are std process
modeling methods
and tools employed
to model and define
business processes?
4. Does governance
have a role in the
management and
orchestration of
business process data
needs, mapping and
prioritization?
RelatedPAs
1. Data Requirements
Definition PA
2. Metadata
Management PA
3. Governance
Management PA

50. Level1:Performed
1. Business
process to
data
element
mapping,
specifying
CRUD
matrix
2.Consum
er and
producer
matrix
3. Data
Flow
diagrams
at attrib.
level
4. List of
data
sources
and
attributes
for a data
set
Level2:Managed
1.Data Change
management
process.
2.Governance
process for
shared data
assets and
data sets.
3.Business
process cata-
logs & maps to
shared attri-
butes.
4. Data source
selection crit-
eria
5. Mapping
between data
producers and
consumers
6. Business
process model-
ing tools
7. Metadata
repository
8. Data attrib
Level3:Defined
1. Process to
Data mapping
template
2.Data
mapping
project plan
3.DM org. roles
&responsibilities
4. Change
mgmt process
for defined
data sets
5. Lifecycle
data mapping
of core business
processes
6. Data maps
7. Context
diagrams
8. Interface,
data source
and destination
change
records
9. Identified
data attrib.
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Metrics
documentat
ion and
results
2.Approved
process
mapping
change
requests
3.Remediati
on process
4.Remediati
on Plans
1.Data
Dependenci
es Reports
2.Recommen
dations to
improve data
lifecycle
mgmt
processes
3. Data
lifecycle
forecasting
reports
4.Reports to
Sr mgmt
based on
statistical
analysis
5.Public
ppts,white
papers or
docs on data
lifecycle
mgmt
process
experience

51. Goals
1. Data reqmts for
sourcing, procure-
ment and provider
mgmt incl. data
quality criteria are
assessed according
to a documented
process.
2. Selecting,
contracting, monito-
ring and managing
data providers is
performed accord-
ing to a std data
source selection and
control process
3. Potential sources
and providers, incl.
their services, data
scope, processes
and technologies
are identified.
4. Std SLAs address
all bus. Requirements
& used to manage
data providers
CoreQuestions
1. How are data sour-
cing requirements
captured, validated
& understood?
2. Are requirements
for data sourcing
specific, unambi-
guous, driven by
business require-
ments and feasibly
procurable?
3. Is there a mecha-
nism that ensures
business approval of
sourcing require-
ments?
4. How are data attri-
butes mapped to
data sources and
downstream appli-
cations?
5. How is the data
source selection
process managed?
6. How are service
and content quality
from data providers
monitored?
RelatedPAs
1. Data Requirements
Definition PA
2. Data Quality Strategy
PA
3. Governance
Management PA
4. Data Profiling PA
5. Data Quality
Assessment PA

52. Level1:Performed
1. Data
sourcing
requireme
nts.
2.Data
source
selection
criteria.
3.Contract
Coverage
checklist
for exter-
nal
providers
4. Data
feed
evaluation
reports
5. Agree-
ment with
internal &
external
data
providers
6. Approv-
ed vendor
invoices
Level2:Managed
1.Procurement
policies.
2.Data source
selection
criteria.
3.Data sourcing
requirements.
4. Mapping of
data require-
ments to
sources.
5. Providers
SLAs
6. Procurement
process
7. Data Source
Evaluations
8. Meeting Min.
with data
providers
Level3:Defined
1. Std data
sourcing
process
2.SLA template
3.SLAs with
providers
4. Defined
Quality criteria
for data
sourcing
5. Defined
metrics for
measuring
data sourcing
6. Updates to
data sourcing
process based
on stakeholder
feedback &
best practices
7. Stds,
procedures,
policies and
work flow diag.
8. Data
provider
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Performan
ce reports,
dashboards,
scorecards
& heat
maps
2.Scoring
criteria for
data
providers
3.Analytical
reports of
provider
performanc
e
4. Recomm-
endations
for changes
to provider
SLAs
1.Analytical
results
2.Data
Sourcing
performance
related
recommend
ations
3. Alignment
mechanism
for data
sources to
business
objectives
4.PPTs,
articles and
white papers

53. Deep Dive into the Process Areas of each
Category
CATEGORY 5 : Platform and
Architecture

54. Architec
tural
Approac
h
•Assists the Org. in developing an approved approach to scope and design a
consumable data and technology architecture that stresses on the duplicate data
reduction and maximizing data sharing
Architec
tural
Standar
ds
•Addresses the development and approval of standards of data representation, data
access and data distribution
DM
Platform
•Emphasizes stakeholder involvement and governance in decisions that affect platform
selection and implementation
Data
Integrati
on
•Helps the Org. to create and maintain alignment with business needs through design of
shared data stores and to establish and enforce standards
Historical
Data
archiving
&
Retention
•Addresses versioning, record retention and archiving, ensuring that data satisfies
availability needs, business needs and regulatory requirements as applicable

55. Goals
1. The approved
architectural
approach is
consistent with
business needs and
Arch. stds.
2. The transition plan
from the “As-is” to
the “To-be” state is
consistently monitor-
ed to ensure that
projects are aligned
with long term
objectives
3. The Arch.
approach is approv-
ed and adopted by
all relevant stake-
holders.
4. Platform and tech
capability decisions
are aligned with the
arch approach and
approved by
stakeholders
5. Metrics used by
Bus. & IT stakeholders
CoreQuestions
1. How does the Org.
approach archi-
tecting information
assets?
2. Is the Arch appr-
oach consistently
followed and adopt-
ed by all relevant
stake-holders?
3. What is the
rationalization
method used for
eliminating duplicate
data?
4. Does the Org. have
an approved data
dictionary stack and
governance applied
to modifications,
additions and sun
setting?
5. Has the Org.
documented and
approved the tech
capabilities and
reqmts to satisfy
operational bus.
Continuity?
RelatedPAs
1. Data Management
Strategy PA
2. Architectural Standards
PA
3. Governance
Management PA
4. Data Integration PA
5. Data Profiling PA

56. Level1:Performed
1.Architect
ure design
for imple-
mentation
2.Business
and tech.
approvals
for archi-
tecture
3.Stakehol
der list for
architectur
e appro-
vals
Level2:Managed
1.Documented
approval for
architectural
designs.
2.Approval
process for
arch design
through
governance.
3.Approved
arch utilization.
4.Shared data
interface
traceability
map.
5.Implementati
on consistent
with approved
designs
Level3:Defined
1. Ration-
alization reports
and decision
criteria
2.Data related
arch approach
3. Implement-
ation checklists
aligned with
transition plan
4. Evaluation of
external & inter-
nal stds.
5. List of Arch
adoption stake-
holders and BUs
6. Tech req.
specifications.
7. Arch blue-
print compared
to the As-Is
architecture
8. Data Quality
profiling reports
applied to
design
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Cost Bene-
fit analyses
2.Quantitati
ve perform-
ance criteria
& evaluation
targets for
designed
components
and archi-
tecture
3.Statistical
models
employed
to guide
arch
decisions
4.Document
-ed limit-
ations of
current arch
approach
1.Modificatio
ns to arch
approach
2.Prediction
model comp-
arison report
against
business
objectives
3. Stakehold-
er feedback
4.PPTs or pub-
lications abt
the org’s
arch
approach
5. Identified
enhanced
bus.
Capabilities
due to
enhanced
data analysis

57. Goals
1. Develop a comp-
rehensive set of data
standards aligned
with the Arch
approach and the
DM strategy.
2. Institute a sust-
ainable standards
development and
maint. process
involving business
and IT stakeholders
3. Establish effective
governance and
auditing processes
for standards
adherence and
exceptions
4. Define and
enforce a data
distribution stds for
requests and
approvals
5. Define and
enforce approved
data access
methods across
platforms
CoreQuestions
1. What are the
categories of stds
required for the org’s
target data arch and
how are they scoped
and defined?
2. How does the org
determine business
needs and techno-
logy strategy for dev-
eloping approved,
std data access and
governance?
3. How are data
models approved,
maintained and
governed?
4. Has the Org.
defined architect-
urally aligned, std
data access methods
and criteria?
5. How does the Org
promulgate, audit
and enforce
standards?
RelatedPAs
1. Data Management
Strategy PA
2. Data Quality PA
3. Governance
Management PA
4. Data Management
Function PA
5. Data Requirements
Definition PA
6. Business Glossary PA
7. Metadata
Management PA
8. Data Integration PA

58. Level1:Performed
1.Data
Stds used
by projects
2.Validatio
n of As-Is
data
stores
against
referenc
ed stds
Level2:Managed
1.A Policy that
requires
adherence to
standards.
2.Standards
artifacts.
3.Standards
approval
process.
4.Standards
Change
Request
process.
5.Project
references to
standards
6. Guidance for
incorporating
stds into design
Level3:Defined
1. Compliance
or regulatory
reporting stds
and require-
ments
2.Stds develop-
ment and
modification
process
3. Standards
tailoring
guidance
4. Standards
exception
process
5. Audit results
reports
6. Architecture
review board
meeting notes
7. Data Std
policies and
procedures
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Standards
review docu
mentation
2. Impact
analysis for
proposed
changes to
stds
3. Architect-
uralstandard
s complia-
nce metrics
1.Engageme
nt with
external stds
bodies
2.Research of
emerging
technologies
3. Proposed
stds for future
technologies
likely to be
adopted
4. PPTs and
other
published
work related
to data stds

59. Goals
1. The Platform satis-
fy the approved
requirements and
architecture.
2. Processes exist
and are followed for
effective platform
management to
meet business needs
3. The Platform is
supported by
adequately trained
and skilled personnel
4. The platform
provides trusted
data
CoreQuestions
1. How are
authoritative data
sources defined,
selected and
integrated into
particular portions of
the platform?
2. How does the org
address overlapping
platforms and data
duplication?
3. Does the org have
a process for making
“build versus buy”
decisions?
4. How does the org
address platform
scalability, security
and resiliency in
accordance with?
Anticipated growth of
data, users and
overall complexity?
5. What forms of
data, data exchange
and interfaces are
supported by the
platform?
RelatedPAst
1. Data Lifecycle
Management PA
2. Data Quality
StrategyPA
3. Governance
Management PA
4. Data Management
Function PA
5. Data Management
Strategy PA
6. Business Glossary PA
7. Metadata
Management PA

60. Level1:Performed
1.Inventory
of data
managem
ent
platforms
and
compone
nts
Level2:Managed
1.Data
Management
platform
documentation
2.Approved
deployment
and conversion
and migration
plans.
3.Documented
platform
decisions and
rationale.
4.Documented
stakeholder
involvement in
the design and
approval of
data manage-
ment platform
deployment
plan
Level3:Defined
1. Document-
ation mapping
critical data
elements to
platforms
2.Documents
identifying and
justifying data
duplication
3. Platform
implementation
plan
4. Platform
architecture
designs
5. Platform
performance
data
6. SLAs
7. Platform
metadata
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Metrics to
measure
both qualit-
ative and
quantitative
performanc
e of DM
platform
2. Measure-
ment and
analysis
plans
3. Statistical
analysis
according
to the
measureme
nt plan
4. Approved
decisions
based on
analysis of
metrics
1.Causal
analysis
2.Performanc
e prediction
models
3. Approvals
for improve-
ments
4. Predicted
Vs actual
performance
analyses
5. Approved
optimization
plans
6. Public PPTs
and other
formal &
informal docs
related to
DM platform

61. Goals
1. Establish and
follow a consistent
process to ensure
ongoing business &
technology align-
ment for data
integration.
2. Data Integration is
performed utilizing
std processes and
toolsets that enable
compliance with
data architecture
stds & data quality
requirements
3. Proactively
research and eval-
uate integration
technologies for
application and
adoption
4. Establish, manage
data conversion,
transformation and
enrichment so that
the data is fully
processed & meets
quality stds
CoreQuestions
1. How are data
consolidation needs
assessed?
2. How is future re-
dundancyminimized?
3. How does the org
consolidate data
effectively where
redundancy exists?
4. Do Data Integ-
ration stds exist & are
they reviewed, moni-
tored, approved &
enforced?
5. Describe the
compliance process-
es employed to en-
force integration
stds?
6. How are data
quality thresholds &
targets applied to
sources of data at
ingestion, integration?
7. Are the processes
to identify missing
data automated?
RelatedPAs
1. Architectural Standards
PA
2. Data Quality Strategy
PA
3. Data Lifecycle
Management PA
4. Data Profiling PA
5. Metadata
Management PA

62. Level1:Performed
1. Data
Integration
scripts
Level2:Managed
1.Data
Integration
standards
2.Verification
and Validation
plans.
3.Integration
test environ-
ments.
4.APIs
5. Data
Integration
policy
Level3:Defined
1. Verification &
Validation
results
2.Performance
requirements
3. Performance
metrics and
analysis results
4. Measures &
metrics for
continuous
improvement in
data quality
5. Integration
method stds
6. Data Delivery
policy & SLAs
7. Integration
best practices
guidance
8. Standard
interface
specifications
9. Integration
environment
CM process
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Statistical
analysis
results
2. Data
profiling
analyses
3.Consolidat
ed highly
shared data
with
continuous
improvemen
t
1.Quantitativ
e methods
2.Performanc
e triggers
and
thresholds
3. Root cause
analysis
results
4. PPTs, white
papers or
published
articles

63. Goals
1. Historical data is
managed consist-
ently leveraging
common standards
2. Business needs for
capturing and
storing historical
data are met.
3. An approved
process for deter-
mining when and
how data should be
archived is followed
containing defined
activity steps
4. Data retention
periods are
consistent with both
legal and regulatory
requirements.
5. Data archives
reflect
organizational and
regulatory
requirements.
CoreQuestions
1. What are the arch
stds & conventions
applied to the
structure & mgmt of
historical data & how
are the corres.
Business rules defined
and governed?
2. How is data retent-
ion for the required
length of time
assured?
3. How is the integrity
of archived data
maintained?
4. Is there a consistent
approach for the
retrieval & integration
of archived historical
data with current
data?
5. How is an audit trail
for data changes
monitored and
managed?
6.What consider-
ations applied when
archived data can
be deleted?
RelatedPAs
1. Architectural Standards
PA
2. Data Requirements
Definition PA
3. Data Management
Function PA
4. Governance
Management PA

64. Level1:Performed
1. Backup
Registers
for data
stores and
data
archives
2.Archiving
procedure
s
3. Change
log files
4. Data
retention
business
rules
5. Data
archiving
or dest-
ruction
procedure
s
Level2:Managed
1.Data
Retention
policies
2.Restoration
testing records.
3.Encrypted
archives.
4.Data
Encryption
requirements
5. Archived
data access
tests
Level3:Defined
1. Restoration
procedure
documentation
2.Application
with access to
historical data
3.Data logging
policy including
log retention
4. Data archive
requirements
5. Archive
backup and
restoration
requirements
6. Restoration
testing records
for archived
data
7. Audit and
test records
Level4:Measured
Level5:Optimized
Functional Practice Statements & Example Work Products
1.Improvem
ent process
2. Process
improvemen
t reports and
records
3.Change
manageme
nt records
4. Regulator
or stake-
holders
feedback
5. Statistical
and other
quantitative
analysis
reports
1.Public PPTs,
white papers,
articles and
other
documents
communicati
ng processes
and
experience

65. SUPPORTING PROCESSES
INFRASTRUCTURE SUPPORT
PRACTICES

66. Measure
ment &
Analysis
•Addresses measures and select analytical techniques for identifying strengths and
weaknesses in data management processes
Process
Manage
ment
•Addresses a usable set of organizational process assets and plans, implements and
deploys organizational process improvements informed by the business goals and
objectives and the current gaps in the organization’s processes
Process
Quality
Assuran
ce
•Provides staff and management with objective insight into process execution and the
associated work products
Risk
Manage
ment
•Identifies and analyzes potential problems to take appropriate action to ensure
objectives can be achieved
Configur
ation
Manage
ment
•Addresses the integrity of the operational environment using configuration identification,
control, status accounting and audits

67. Purpose & Overview
• Purpose of this SPA is to develop and sustain a measurement capability and
analytical techniques to support managing and improving DM activities
• Measurement and analysis provides visibility into the performance of the DM
program and involves activities like specifying objectives of measurement;
analysis techniques and mechanisms for data collection, storage, reporting &
feedback; implementing the above techniques and provide objective results
to be used for making informed decisions and take the appropriate action.
• The Integration of measurement and analysis into DM processes supports
activities like planning & estimating; tracking actual progress; identifying &
resolving issues; integration of remedial actions into the DM program etc
Goals
• A set of metrics that measures the
satisfaction of the DM program’s
objectives is established and used
• The process of measuring DM capa-
bilities and improvements based on
defined metrics is established & used
• Org-wide access to DM measure-
ments & analysis results
• Stakeholders are kept informed
about the status of the DM program
Core Questions
• What measures and analyses exist to
determine if DM goals and
objectives are being met?
• How does the Org define, measure,
analyze and report on DM?
• How are measurements and
analyses integrated into DM
processes?

68. Purpose & Overview
• Purpose of this SPA is to establish and maintain a usable set of org. process
assets and plan, implement and deploy org process improvements informed
by the business goals and objectives & the current gaps in the org’s processes
• Org. process assets enable consistent process execution across the org. and
provide a basis of cumulative, long-term benefits to the organization
• Improvements to the processes are obtained from various sources like
measurement of processes; lessons learned in implementing processes; results
of process appraisals; product & service evaluation activities; customer
satisfaction evaluations and benchmarking against other org’s processes an d
recommendations from other improvement initiatives in the organization.
Goals
• The Org operates according to its set
of standard processes
• The Org follows defined methods for
maintaining their processes to
accommodate changes in business
requirement, stds and technology
• Process measures, process assets
and examples are maintained in a
repository
Core Questions
• How are processes, methods,
procedures, policies and standards
maintained?
• How is process performance
measured?
• How does the org. measure process
compliance?
• How does the org. ensure that
improvements are identified,
pursued, implemented and
validated?

69. Purpose & Overview
• The Purpose of this SPA is to provide staff and management with objective
insight into process execution and the associated work products
• This SPA involves activities like objectively evaluating performed processes and
work products against applicable process descriptions, standards and proced-
ures; identifying and documenting NCs; providing feedback to staff and
managers on the results of QA activities and ensuring that NC issues are
addressed.
• The methods used to perform objective evaluations are formal audits by
separate QA organizations; peer reviews; in-depth review of work e.g. desk
audits; distributed review of work products and process checks built into the
processes such as fail-safe when they are done incorrectly
Goals
• Management has visibility into the
quality of the process and products
• NC issues are addressed at the
appropriate level
• Process and Product quality have
become an embedded discipline at
all levels in the organization
Core Questions
• Are Process NC issues raised to an
appropriate level?
• Are quality issues analyzed for
positive trending?
• Do all relevant stakeholders have
visibility into the quality of the
process and products?

70. Purpose & Overview
• The Purpose of this SPA is to identify and analyze potential problems in order to
take appropriate action to ensure objectives can be achieved.
• Risk Management addresses issues that could endanger achievement of
critical objectives
• Effective Risk Management includes early and aggressive risk identification
through collaboration and the involvement of relevant stakeholders
• Risk Management consider internal and external, technical and non-technical
sources of risks
• Risk Management process involves defining a risk management strategy;
identifying and analyzing risks and handling identified risks i.e. risk mitigation
Goals
• The Organization is operating with
an understanding of its current level
of risk
• The Organization is pursuing risk
mitigation plans to limit the potential
damage from identified risks
• Risks are continually identified,
analyzed and monitored.
Core Questions
• Does the Org. know the amount of
risk it is operating under?
• Has the Org. identified and
implemented risk mitigation and
contingency plans?
• Does the Org. periodically monitor
risks and take appropriate update
actions?

71. Purpose & Overview
• The purpose of this SPA is to establish and maintain the integrity of the
operational environment using configuration identification, control, status
accounting and audits
• CM is a partnership between business, data, and IT resources to control the
integrity of the products, data stores and interfaces and changes to them.
• CM involves activities like identifying the configuration of the operational
environment and data interfaces at given points in time; Controlling and
managing data interfaces and the operating environments; Managing
changes to data interfaces; Maintaining the integrity of data interfaces and
providing accurate status of data interfaces to the end users & customers
Goals
• Maintain the integrity of data as
changes occur.
• Define and implement a
configuration and release
management system.
Core Questions
• How is configuration management
implemented and measured?
• How are data changes planned and
controlled across the data lifecycle?

72. ISP – Level 1
Perform the
Functional
Practices
•Ensure that adopting organizational components like project,
business unit perform the processes
ISP – Level 2
Implement a
Managed
Process
•Ensure that adopting organizational components do the
following : operate under an org policy; plan the process;
provide resources; assign responsibility; train people, manage
configurations, identify & involve relevant stakeholders; monitor
and control the process; objectively evaluate adherence &
review status with Higher management
ISP – Level 3
Institutionalize
Org.
Standards
•Ensure that a set of std processes is in place; Org. assets support
the use of the std process; elements can be tailored from the
standard process to fit unique circumstances; process related
experiences are collected to support future use and
improvements

73. Prepared By:
Meena Shivram
meenashivram@gmail.com