Sustaining Data Governance and Adding Value for the Long Term

The First Step in Information Management
www.firstsanfranciscopartners.com
Sustainable
Data
Governance:

Adding
Value
for
the
Long
Term

Kelle
O’Neal

kelle@ﬁrstsanfranciscopartners.com

415-‐425-‐9661

@1stsanfrancisco

Why
We’re
Here

pg 2Copyright (c) 2015 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Purpose:

Understand
criQcal
success
factors
for
sustainability
of
a
Data

Governance
Discipline

Outcome:

§  Understanding
Data
Governance
FoundaQon

§  Understanding
how
to
make
governance
a
core
competency

§  PracQcal
knowledge
that
can
be
immediately
implemented

Agenda

§  Level
SeTng
-‐
FSFP’s
perspecQve
on
Data
Governance

§  Obstacles
&
Challenges
to
Sustainability

§  CreaQng
Sustainable
Data
Governance

−  OrganizaQon

−  Alignment

−  Metrics
&
Measurements

−  CommunicaQon

−  Embedding
Governance

§  Ensuring
success


Level
SeTng

Data
Governance
DeﬁniQon

§  Data
Governance
is
the
organizing

framework
for
establishing
the

strategy,
objecQves
and
policy
for

eﬀecQvely
managing
corporate
data.

§  It
consists
of
the
processes,
policies,

organizaQon
and
technologies
required

to
manage
and
ensure
the
availability,

usability,
integrity,
consistency,

auditability
and
security
of
your
data.

CommunicaQon

and
Metrics

Data

Strategy

Data
Policies

and
Processes

Data

Standards

and

Modeling

A
Data

Governance

Program
consists
of

the
inter-‐workings

of
strategy,

standards,
policies

and
communicaQon

pg 5
© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

pg 6
Data
Governance
Framework

•  Vision & Mission
•  Objectives & Goals
•  Alignment with Corporate
Objectives
•  Alignment with Business
Strategy
•  Guiding Principles
•  Statistics and Analysis
•  Tracking of progress
•  Monitoring of issues
•  Continuous Improvement
•  Score-carding
•  Policies & Rules
•  Processes
•  Controls
•  Data Standards & Definitions
•  Metadata, Taxonomy,
Cataloging, and Classification
•  Operating Model
•  Arbiters & Escalation points
•  Data Governance Organization
Members
•  Roles and Responsibilities
•  Data Ownership & Accountability
•  Collaboration & Information
Life Cycle Tools
•  Data Mastering & Sharing
•  Data Architecture & Security
•  Data Quality & Stewardship
Workflow
•  Metadata Repository
•  Communication Plan
•  Mass Communication
•  Individual Updates
•  Mechanisms
•  Training Strategy
•  Business Impact & Readiness
•  IT Operations & Readiness
•  Training & Awareness
•  Stakeholder Management & Communication
•  Defining Ownership & Accountability
Change
Management

Develop
and
execute
architectures,
policies
and
procedures
to
manage
the
full
data
lifecycle

Enterprise
Data
Management

Enterprise
Data
Management

Ensure
data
is
available,
accurate,
complete
and
secure

Data
Quality

Management

Data
Architecture

Data

RetenQon/Archiving

Master
Data

Management

Big
Data

Management

Metadata
Management

Reference
Data

Management

Privacy/Security

DATA GOVERNANCE
pg 7© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

pg 8
The
Big
Picture:
EIM
Framework

Provides
a
holisQc
view
of
data
in
order
to
manage
data
as
a
corporate
asset

Enterprise
InformaQon
Management

InformaQon
Strategy

Architecture
and
Technology
Enablement

Content
Delivery

Business
Intelligence

and

Performance
Management

Data
Management

InformaQon
Asset

Management

GOVERNANCE
ORGANIZATIONAL ALIGNMENT
Content
Management

Obstacles
&
Challenges

The
landscape
is
changing
…


Obstacles

§  CompeQng
prioriQes
and
lack
of
resources

§  Data
Ownership
and
other
territorial
issues

§  Lack
of
cross-‐business
unit
coordinaQon

§  Lack
of
data
governance
understanding

§  Resistance
to
change
or
transformaQon

§  Lack
of
execuQve
sponsorship
and
buy-‐in

§  Resistance
to
accountability

§  Lack
of
business
jusQﬁcaQon

§  Inexperience
with
cross-‐funcQonal
iniQaQves

§  Change
of
personnel


Obstacles


Why
is
Data
Governance
Important?

Internal
pressures:

§  Desire
to
understand
customer
at
any
Qme

from
any
channel

§  Data
Quality
issues
are
persistent

§  Balance
of
old
mainframe
systems
with
new

technologies

§  Movement
to
the
cloud
and
losing
control
of

data

§  Data
Volumes
are
increasing

§  Mobile
apps
enabling
data
to
be
created
and

accessed
anywhere

§  Project
oriented
approach
to
addressing
issues/
opportuniQes

External
pressures:

§  Greater
amounts
of
new
regulaQons

§  Increasing
Customer
Demands
–
my

informaQon
anywhere
at
any
Qme

§  Technology
and
market
changes

outpacing
ability
to
respond

Ensures
the
right
people
are
involved
in

determining
standards,
usage
and

integra4on
of
data
across
projects,
subject

areas
and
lines
of
business

Establishing
the
OrganizaQon

Don’t
base
your
program
on
speciﬁc
individuals


Process

•  How
are
decisions

made?

•  Who
makes
them?

•  How
are
Commihee’s

used?

Culture

•  Centralized

•  Decentralized

•  Hybrid

OperaQng

Model
•  Data
Governance

Owner

•  SME’s

•  Leadership

People


OperaQng
Model

§  Outlines
how
Data
Governance
will
operate

§  Forms
basis
for
the
Data
Governance
organizaQonal
structure
–
but
isn’t
an
org
chart

§  Ensures
proper
oversight,
escalaQon
and
decision
making

§  Ensures
the
right
people
are
involved
in
determining
standards,
usage
and
integraQon

of
data
across
projects,
subject
areas
and
lines
of
business

§  Creates
the
infrastructure
for
accountability
and
ownership

Wikipedia:
An
OperaQng
Model
describes
the
necessary
level
of
business
process

integraQon
and
data
standardizaQon
in
the
business
and
among
trading
partners

and
guides
the
underlying
Business
and
Technical
Architecture
to
eﬀecQvely
and

eﬃciently
realize
its
Business
Model.
The
process
of
OperaQng
Model
design
is
also

part
of
business
strategy.

Types
of
OperaQng
Models

§  Centralized

−  Similar
to
a
top
down
project
model

§  Decentralized

−  Flat
structure,
more
virtual/grassroots
in
nature

§  Hybrid
/
Federated


Pros:

•  Formal
Data
Governance
execuQve
posiQon

•  Data
Governance
Steering
Commihee
reports

directly
to
execuQve

•  Data
Czar/Lead
–
one
person
at
the
top;

easier
decision
making

•  One
place
to
stop
and
shop

•  Easier
to
manage
by
data
type

Cons:

•  Large
OrganizaQonal
Impact

•  New
roles
will
most
likely
require
Human

Resources
approval

•  Formal
separaQon
of
business
and
technical

architectural
roles

Bus
/
LOBs

pg 19
OperaQng
Model
-‐
Centralized

Copyright (c) 2014 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
DG

Execu4ve

Sponsor

DG

Steering

Commi<ee

Center
of
Excellence
(COE)

Data
Governance

Lead

Technical
Support

Data
Architecture
Group
Technical Data
Analysis
Group
Business
Support

Business

Analysis

Group

Data

Management

Group

LOB/BU

Data
Governance
Steering
Commi<ee

LOB/BU
Data
Governance
Working
Group

pg 20
OperaQng
Model
-‐
Decentralized

Data Stewards
Application
Architects
Business
Analysts
Data Analysts
Pros:

•  RelaQvely
flat
organizaQon

• 
Informal
Data
Governance
bodies

• 
RelaQvely
quick
to
establish
and
implement

Cons:

•  Consensus
discussions
tend
to
take
longer

than
centralized
edicts

• 
Many
parQcipants
compromise
governance

bodies

• 
May
be
difficult
to
sustain
over
Qme

• 
Provides
least
value

• 
Difficult
coordinaQon

• 
Business
as
usual

• 
Issues
around
co-‐owners
of
data
and

accountability

pg 21
OperaQng
Model
-‐
Hybrid

Pros:

•  Centralized
structure
for
establishing
appropriate
direcQon

and
tone
at
the
top

•  Formal
Data
Governance
Lead
role
serving
as
a
single
point

of
contact
and
accountability

•  Data
Governance
Lead
posiQon
is
a
full
Qme,
dedicated
role

–
DG
gets
the
ahenQon
it
deserves

•  Working
groups
with
broad
membership
for
facilitaQng

collaboraQon
and
consensus
building

•  PotenQally
an
easier
model
to
implement
iniQally
and
sustain

over
Qme

•  Pushes
down
decision
making

•  Ability
to
focus
on
specific
data
enQQes

•  Issues
resoluQon
without
pulling
in
the

whole
team
Cons:

•  Data
Governance
Lead
posiQon
is
a
full
Qme,
dedicated
role

•  Working
groups
dynamics
may
require
prioriQzaQon
of

conflicQng
business
requirements

•  Too
many
layers
Data
Governance
Steering
Commihee

Data
Governance
Office

Data
Governance
Working
Group

Business
Stakeholders
IT
Enablement

Data Governance Organization

OperaQng
Model
-‐
Federated

Pros:

•  Centralized
Enterprise
strategy
with
decentralized
execuQon

and
implementaQon

•  Enterprise
Data
Governance
Lead
role
serving
as
a
single

point
of
contact
and
accountability

•  “Federated”
Data
Governance
pracQces
per
Line
of
Business

(LOB)
to
empower
divisions
with
differing
requirements

•  PotenQally
an
easier
model
to
implement
iniQally
and
sustain

over
Qme

•  Pushes
down
decision
making

•  Ability
to
focus
on
specific
data
enQQes,
divisional
challenges

or
regional
prioriQes

•  Issues
resoluQon
without
pulling
in
the

whole
team
Cons:

•  Too
many
layers

•  Autonomy
at
the
LOB
level
can
be
challenging
to
coordinate

•  Difficult
to
find
balance
between
LOB
prioriQes
and

Enterprise
prioriQes
Enterprise
Data
Governance
Steering

Commihee

Enterprise
Data
Governance
Office

Data
Governance
Groups

Data
Governance
OrganizaQon

Business

Stakeholders

IT
Enablement

Divisional
DG

Office

Business

Stakeholders

IT
Enablement

Divisional
DG

Office

Business

Stakeholders

IT
Enablement

Business

Stakeholders

IT
Enablement

Divisional
DG

Office

OperaQng
Model
Roles
and
ResponsibiliQes

§  Data
Governance
Steering
Commihee

−  Provides
overall
strategic
vision

−  Approves
funding,
budget
and
resource
allocaQon
for
strategic
data
projects

−  Establishes
annual
discreQonary
spend
allocaQon
for
data
projects

−  Adjudicates
intractable
issues
that
are
escalated

−  Ensures
strategic
alignment
with
corporate
objecQves
and
other
business
unit
iniQaQves

§  Data
Governance
Office

−  Chairs
the
Data
Governance
Steering
Commihee
and
Data
Governance
Working
Group

−  Acts
as
the
glue
between
the
Data
Governance
Steering
Group
and
the
Working
Commihee

−  Defines
the
standards,
metrics
and
processes
for
data
quality
checks,
invesQgaQons,
and
resoluQon

−  Advises
business
and
technical
resources
on
data
standards
and
ensures
technical
designs
adhere
to
data
architectural
best

pracQces
to
ensure
data
quality

−  Adjudicates
where
necessary,
creates
training
plans,
communicaQon
plans
etc

§  Data
Governance
Working
Group

−  Governing
body
comprised
of
data
owners
across
Business
and
IT
funcQons
that
own
data
definiQons
and
provide
guidance
&

enforcement
to
drive
change
in
use
and
maintenance
of
data
by
the
business

−  Validates
data
quality
rules
and
prioriQze
data
quality
issue
resoluQon
across
the
funcQonal
areas

−  Trains,
educates,
and
creates
awareness
for
members
in
their
respecQve
funcQonal
areas

−  Implements
data
business
processes
and
are
accountable
to
decisions
that
are
made


Typical
DG
Oﬃce
Deliverables

§  Some
Typical
Deliverables:

§  Documented
DG
Strategy,
Vision,
Mission,
ObjecQves

§  Documented
DG
Guiding
Principles

§  Documented
roles
&
responsibiliQes
of
the
various
members

§  Up
to
date
OperaQng
Model

§  RACI
matrices

§  Templates
for
Policies
and
Processes

§  Templates
for
capturing
metrics
and
measurement
requirements

§  Templates
for
steering
commihee
meeQngs

§  Training
Plans

§  CommunicaQon
Plans

§  Template
for
regular
DG
communicaQon

§  Templates
for
logging
issues
needing
escalaQon
and
eventual
resoluQon

§  Templates
for
new
DG
service
requests

§  Checklists
for
new
projects
to
ensure
adherence
to
DG
standards


Typical
Roles

§  Business
Steward

§  Data
Owner

§  Data
Steward

§  Data
Quality
Analyst

§  Business
Analyst

§  Data
Architect

§  Technical
Leads
(MDM,
Metadata,
Reference
Data,
App)


Sample
Data
Governance
OperaQng
Model

Direc4on

TBD

Execu4ve
Sponsor

Business
and
IT

Business
Steward
Leads

Service
Order
Management

Finance
FP&A
Sales

Market
Strategy

Analy4cs

Data
Governance
Steering

Commi<ee

Finance

(CFO)

InternaQonal

(President)

Global

Services

(COO)

IT

(CIO)

MarkeQng

(CMO)

Data
Governance
Oﬃce

Data
Governance
Leads

Business
and
IT

Data
Governance
Coordinator

Management

Provides
budget
and

resource
approvals.

Forum
for
issue

escalaQon

Craps
the
enterprise
data

strategy,
including
polices,

processes
and
standards

to
ensure
that
data
is

managed
as
an
asset

Execu4ve
Level

Management

Level

Stewards
data
within

their

BU
to
ensure
that

the
enterprise
policies

are
applied

Tac4cal

Level

Strategic
Level

Provides
overall
strategic

direcQon,
budget
and

resource
approvals

forum
for
issue

escalaQon

Execu4on

Data
Management
IT
Support
Group

Data
Quality
Lead
Metadata
Lead

Data
Architect

BI
Delivery

Opera4ons
External

Repor4ng

DGWG

Enterprise

Architect

BA

Data
Analyst

IT
Security

Privacy

Legal

Data
Stewards

Risk

Centralized
Data
Steward
Pool

Accoun4ng

Data
Governance
Leadership
Team

Sample
MulQ-‐Domain
OperaQng
Model

Program
Oversight
&
DirecQon

ExecuQve
Sponsor

Program
Management

DG
Working
Group
Data
Governance
Program
Management
Team

DG
Program
Manager

DG
Coordinator

Program
ExecuQon

IT
Manager

Data Domain Owners
Business
Data
Leads

Data
AcquisiQon

Data
Stewardship

IT
Enablement

Supply
Chain
InternaQonal
Sales
HR
Finance
IT
MarkeQng

Customer
Product
Employee
Vendor
Supplier

DG
Data
Quality
Manager

Direc4on

TBD

Enterprise
Data
Sub-‐Commi<ee

Business
Data
Stewards

Data
Governance
Steering
Commi<ee

Business
Unit

Officers

Data
Owners
IT
Partner(s)

Data
Governance
Office
(DGO)

Management

Program
Oversight.
Allocates
budget
&

resource.
Empower
Business
Data

Stewards.
Forum
for
issue
escalaQon.

Craps
the
Enterprise
Data
Strategy,

processes
and
standards
to
ensure
that

data
is
managed
as
an
asset.

Execu4ve
Level

Management

Level

Stewards
data
within
their
BU
to
ensure

that
the
enterprise
policies,
standards
&

processes
are
applied.

Tac4cal

Level

Strategic
Level

Provides
overall
strategic

direcQon,
budget

&
resource
approvals.
Forum
for
issue

escalaQon.
Approval
of
data
domains
under

governance
control.

Execu4on

Technical

Data
Stewards

Local
Data
Governance
Working
Groups

Chair:

Enterprise
Data
Officer

Chair:

Data
Governance
Office
Lead

IT
Partner(s)

Sr.
Execu4ves

Business
Units

Sample
Enterprise
OperaQng
Model

Business
&
Technical
Data
SMEs

Scalability
at
the
Data
Domain

Security,
Balance,
PosiQon
&
TransacQons

Accountable
ExecuQve

Company/Account

Accountable
ExecuQve

Enterprise
Data

Sub-‐Commihee
Member

Security,
Balance,
PosiQon
&
TransacQons

Business
Data
Owner

Company

Business

Data
Owner

Security

Business

Data
Steward

Balance,
PosiQon

&
TransacQon

Business

Data
Steward

Company

Business

Data
Steward

Account
Business

Data
Steward

Security
DG

Working
Group

BP&T
DG

Working
Group

Company
DG

Working
Group

Account
DG

Working
Group

Layers
scale:

§  OrganizaQon

§  Maturity

§  Complexity
of
Domain

Leadership
can
be

responsible
for
mulQple

domains

Data
Stewardship
=

focused

Account

Business

Data
Owner

Members
of
DG

Steering
Commi<ee

Members
of

Business
Data

Steward
Prac4ce

Group

Members
of
Enterprise

Data
Sub-‐Commi<ee

Copyright
(c)
2015
-‐

First
San
Francisco
Partners
www.ﬁrstsanfranciscopartners.com

Proprietary
and
ConﬁdenQal

pg 30
Use
Case
–
Account
Local
Data
Governance
Alignment

Accountable
ExecuQve

Business
Data
Steward

Local
Data
Governance

Working
Group

Business
Steward
Lead

Account
Domain
Enterprise
Opera4ng
Model

Keys
to
a
Successful
DG
OrganizaQon

§  Governance
team
must
contain
members
from
mulQple
lines
of
business

§  Ensures
cross
funcQonal
buy-‐in
and
ownership

§  Key
lines
of
business
must
be
represented

§  Team
members
must
represent
both
business
and
IT

§  IT
needs
to
be
able
to
implement
per
the
governance
policies
and
the
business
needs
to
be
aware
of
IT

limitaQons…

§  Team
needs
to
meet
on
a
regular
basis

§  Business
is
constantly
changing

§  Discuss
new
and
emerging
programs

§  Current
IT
acQviQes
and
their
effect
on
the
data

§  Review
policies
and
study
measurement
output

§  Agreed
upon
fundamentals
that
serve
as
the
Guiding
Principles

§  If
this
doesn’t
exist,
the
first
mandate
is
to
create
this

§  Standards
are
mechanisms
for
Qe-‐breaking

§  Clear
lines
of
communicaQon

§  Regular
interacQon
with
execuQve
management

§  Ensure
communicaQon
methods
to
enforce
policies
at
the
steward
and
stakeholder
level

§  Invite
stewards,
project
managers,
stakeholders
etc
to
provide
status
updates
on
criQcal
iniQaQves
that

affect
the
data

§  Ensure
the
Opera4ng
Model
fits
the
culture
of
the
company

Alignment

Random
House
DicQonary:
a
state
of
agreement
or
cooperaQon

among
persons,
groups,
naQons,
etc.,
with
a
common
cause
or

viewpoint.

Wikipedia:
Alignment
is
the
adjustment
of
an
object
in
relaQon

with
other
objects,
or
a
staQc
orientaQon
of
some
object
or
set

of
objects
in
relaQon
to
others.

Understanding
a
process
from
the
perspec4ve
of
others

Working
individually
towards
a
common
goal

DeﬁniQon
of
Alignment


Impact
on
Governance
Programs

Sources
of
mis-‐alignment

§  Lack
of
understanding

−  Of
how
an
individual’s
role
fits
into

Corporate
ObjecQves

−  Of
other
jobs,
roles,
experiences,

objecQves

§  ConflicQng/
compeQng
objecQves

§  PoliQcs

styles

§  Personality
conflicts

Importance
of
Alignment

§  Creates
a
conQnual
“buy-‐in”

process
with
all
Stakeholders

§  Helps
organizaQons
“think
globally

and
act
locally”

§  OpQmizes
resources
to
manage

costs

§  Work
towards
a
common
goal

§  Minimizes
risk


Alignment
Process

•  Why
is
this

important?

•  Why
should
we

care?

Value

•  Who
cares?

•  Why
should

they
care?

Stakeholders

•  How
does
the

value
beneﬁt

the

stakeholders?

Linkage


IdenQfy
and
Align
Values

Value
of
DG
to
Business
Value
of
DG
to
IT

IdenQfy
Stakeholders

§  Who
are
the
Stakeholders?

§  IT

§  OperaQons

§  Compliance

§  Line
of
Business

§  What
are
their
drivers?

§  What
are
their
key
goals?

§  What
are
their
concerns?

§  What
are
they
trying
to
avoid?

§  What
are
their
prioriQes?

§  Which
goals
are
criQcal?

§  What
happens
if
those
goals
aren’t
achieved?


pg 39Proprietary & Confidential
Stakeholder
Map

Value
of
DG
to

Business

Value
of
DG
to

IT

Linkage
is
the
tacQcal
process
of
mapping
your
delivery
to
the

issues
important
to
the
stakeholder.

•  Per
Stakeholder,
idenQfy
what
is
important
to
them
and
why.

§  What
happens
if
they
don’t
achieve
their
goal?

•  List
elements
of
DG
soluQon

•  Choose
Top
3

•  Choose
up
to
3
elements
of
the
DG
soluQon
and
arQculate
how

those
deliverables
can
help
that
person
achieve
their
goals

§  ConQnually
ask
yourself,
So
What?

Linkage
delivers
Alignment

Create
Linkage


PotenQal
Deliverables

§  Consistency
of
customer/product/employee
data

§  Improve
data
quality

§  Improve
data
consumpQon
and
appropriate
usage

§  Create
and
understand
data
lineage

§  Create
a
data
platorm
to
support
a
single
face
to
the
Customer

§  Facilitate
the
concept
of
“Single
Sourcing”
of
data
to
the
Data
Warehouse

and
Business
ApplicaQons

§  Create
and
implement
common
enterprise
systems/tools
and
processes
for

selected
data


DG
Program

Sales/MarkeQng

Improve
Understanding

of
Customers

Improve
SegmentaQon

Understand
Risk

IT

Improved
ProducQvity

ProacQvely
support

business

Lower
TCO

Improved
Data

Quality

Single
Repository
of

Customer
Data

Create
Data
Lineage

ArQculate
Linkage

The
Single
Repository
of
Customer
data
will

improve
my
understanding
of
customers
by

providing
me
a
trusted
source
of
Qmely,

accurate
and
perQnent
data
from
which
to

execute
analyQcs,
segmentaQon
and
risk

assessment.

CreaQng
and
understanding
Data
Lineage
will

improve
IT
producQvity
by
reducing
the
Qme

spent
searching
for
data,
ensure
the
appropriate

data
is
used
and
validaQng
the
data.
Data

Lineage
that
is
created
and
understood
by
both

IT
and
business
will
facilitate
a
common

language
and
enable
IT
to
beher
support
the

business
growth
and
expansion.

Linkage
creates
Alignment


Measurement
&
Metrics

Why
are
Metrics
Important?

Alignment

Rele-‐
vance

Value


Aligning
Benefit
to
Value

Benefits
of
Data
Governance

•  Data
lineage
and
auditability

•  Improved
data
transparency
and
quality

•  Repeatable
processes
and
reusable
arQfacts

•  Consistent
definiQons

•  Appropriate
use
of
informaQon

•  CollaboraQon
among
teams,
business
units,
etc..

•  Accountability
for
informaQon
use

•  Quality
of
all
data
types

•  Easier
sharing
of
informaQon

•  Visibility
into
the
enterprise
via
data

•  InformaQon
security

Content
property
of
IMCue
and
FSFP,
Copyright
2013

ReproducQon
prohibited

Impact
Determines
Success

Issues

• Report
Quality

and
Accuracy

• Low
ProducQvity

• Regulatory

Compliance
/

Audit
Response

Goals

• Improve
data’s

usability

• Improve

eﬃciency
and

producQvity

• Reduce

compliance
/

audit
cost

Metrics/KPI’s

• Data
Quality

• Data
remediaQon

Qme

• Eﬀort
to
comply

Impact

• Improve
client

relaQonships

• Address
new

markets

• Improve

producQvity

• Improve
analysis

&
decision

making

Content
property
of
IMCue
and
FSFP,
Copyright
2013

ReproducQon
prohibited

DefiniQon

§  Metric

−  A
metric
is
any
standard
of
measurement

§  Number
of
business
requests
logged

§  Number
of
data
owners
idenQfied

§  Percentage
business
requests
resolved
within
agreed
SLA,
etc.

§  Key
Performance
Indicator
(KPI)

−  A
Key
Performance
Indicator
(KPI)
is
a
quanQfiable
metric
that
the
DG
Program

has
chosen
that
will
give
an
indicaQon
of
DG
program
performance.

−  A
KPI
can
be
used
as
a
driver
for
improvement
and
reflects
the
criQcal
success

factors
for
the
DG
Program

§  A
metric
is
not
necessarily
a
KPI


Metrics/KPIs
examples

People

§  #
of
DGWG
decisions
backed
up
by
the
steering
commihee

§  #
of
approved
projects
from
the
DGWG

§  #
of
issues
escalated
to
DGP
and
resolved

§  #
of
data
owners
idenQfied

§  #
of
data
managers
idenQfied

§  DG
adop4on
rate
by
company
personnel
(Survey)

Process

§  #
of
data
consolidated
processes

§  #
of
approved
and
implemented
standards,
policies,
and
processes

§  #
of
consistent
data
definiQons

§  Existence
of
and
adherence
to
a
business
request
escalaQon
process
to
manage
disputes
regarding
data

§  Integra4on
into
the
project
lifecycle
process
to
ensure
DG
oversight
of
key
ini4a4ves

Technology

§  #
of
consolidated
data
sources
consolidated

§  #
of
data
targets
using
mastered
data

§  Address
accuracy
for
mailing/shipping

§  Data
integrity
across
systems

§  Records/data
aged
past
target

§  Presence and usage of a unique identifier(s)

CreaQng
Metrics

Process
to
Establish
Metrics

pg 51
Issues

• What
are
the

issues
in
your

group?

• What
do
you

mean
by
that?

• Why
is
it

important?

• What
are
your

objecQves?

Goals

•  What
is
the
change

you
would
like
to

see?
What
acQon?

•  How
will
that

change
impact

you?

•  What
is
the
impact

if
those
objecQves

aren’t
met?

Metrics/KPI’s

•  What
processes
are

involved
in
that

change?

•  How
is
informaQon

used
in
that

process?

•  What
informaQon
is

used?
What
data?

•  What
data

improvements
are

needed?

Impact

• PosiQve
change

created
by

addressing
issues

• Beneﬁt
of

improving
data
to

impact
objecQve

GeTng
to
Data
Change
Metrics

Issues/
Objec4ves

Goals
Informa4on
Data
Data
Change
Addi4onal
Ac4on

Report
Quality
and

Accuracy

Improve
Data

Understanding

Accounts
Client
InformaQon

Reduce
duplicaQon

of
client
data

Improve
Data

Transparency

Increase

completeness
of

record

Reduce
Manual

RemediaQon

Track
data
lineage
Ensure

thoroughness
of

data
sources

Products
owned

Increase

Completeness
of

record

Ensure

thoroughness
of

data
sources

Households
RelaQonship

Groups

pg 52

Sample
Data
Metrics

Data
Change
Measurement
Target
Frequency

Reduce
DuplicaQon
of

Client
Data

%
DuplicaQon
1%
Daily

Increase
Completeness

of
Client
Record

%
Completeness
of
key
ﬁelds
99%
Daily

Track
Data
Lineage
Completeness
of
lineage
in

metadata

99%
Monthly

Ensure
Thoroughness
of

Client
Data
Sources

Review
of
data
acquisiQon
and
ETL

process

Business

consensus

Quarterly

Increase
Completeness

of
Products
Owned

%
Completeness
of
key
ﬁelds
99%
Weekly

Ensure
Thoroughness
of

Product
Data
Sources

Review
of
data
acquisiQon
and
ETL

process

Business

consensus

Quarterly

pg 53
Data

Understanding

Data

Transparency

Reduce
Manual

RemediaQon

GeTng
to
Business
Change
/
Impact
Metrics

pg 54
Goal
Measurement
Target
Frequency

Improve
Data
Understanding
Completeness
of
Business
Glossary

%
of
Business
Users
Trained

100%

100%

Monthly

Monthly

Improve
Data
Transparency
Completeness
of
Lineage
80%
Monthly

Reduce
Manual
RemediaQon
Time
to
complete
report
process
(baseline
is
6
days)
1
Day
Monthly

Increase
Report
Quality
and

Accuracy

Improved
Business
Stakeholder
SaQsfacQon
Survey

Reduced
Issue
Requests

Business

Approval

10%
drop

Quarterly

Monthly

This
is
your
KPI

BU
2

SCORECARD

BU
4

SCORECARD

BU
1

SCORECARD

BU
3

SCORECARD

DATA
GOVERNANCE

SCORECARD

(FUTURE
STATE)

STRATEGIC

VIEW

OPERATIONAL

SCORECARDS

CONSOLIDATED
BY

BUSINES
UNIT

SETUP
RULES
THRESHOLDS

DATA
QUALITY

DIMENSIONS

FFREQUENCY
WEIGHTING

ALL
SCORECARDS

START
WITH
A

BASELINE

Scorecard
Approach:
Show
some
vision
forward

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

ATTRIBUTE

SCORECARD

Ahribute
level
Supports

OperaQonal
Use
Case

EnQty
Level
Supports

Company
Data
Governance

(Strategic
Value)

CommunicaQon
&
Stakeholder
Management

Why
is
CommunicaQon
Important?

Ø Creates
Awareness

Ø Aligns
expectaQons

Ø Creates
an
opportunity
for

feedback
/
engagement

Ø ProacQvely
addresses
Change

Ø Publishes
Success

Ø Answers
the
quesQons
“Why?”
and
“What’s
in
it
for
me?”

Ø Aligns
acQviQes

TranslaQng
Data
Value
into
Business
Value

is
key
to
maintaining
commitment

§  The
right
metrics
help
maintain
alignment

−  Metrics
have
no
value
if
they
aren’t
aligned
to
the
interests
of
a
stakeholder

−  Ensure
there
is
some
way
of
measuring
how
the
improvement
in
data
is
helping

stakeholders
progress
toward
their
goals

−  What
informaQon
do
you
need
to
track
and
measure
to
those
goals?

§  Translate
the
value
statement
into
the
language
of
the
recipient


Purpose:
Increase
Stakeholder
Engagement

Using
this
framework
enables
clear
gaps
in
stakeholder

engagement
to
be
idenQﬁed
and
subsequent
change

strategies
to
be
put
in
place
to
enable
the
gaps
to
be
closed

T I M EStatus Quo Vision
COMMITMENT/ENTHUSIASM
High
Contact
I’ve heard about this
program/project
Low
I know the concepts
Awareness
I understand how
Program/project positively impacts
and benefits me and the organization
Positive Perception
This is how we do business
Institutionalization
Understanding
I understand what this means to
me and the organization as a
whole
Adoption
I am willing to work hard
to make this a success
Internalization
I’ve made this my own and will
constantly create innovative
ways to use it

•  Engagement
Strategy:

•  Focused
effort
must
be
given

to
high
priority
groups

•  Provide
sufficient
level
of

informaQon
to
less
influenQal

groups
to
ensure
buy-‐in

•  Move
people
and
or
groups

to
the
right
by
trying
to

increase
their
level
of

interest

•  Forms
the
foundaQon
of
your

engagement
/

communicaQon
strategy

Stakeholder
Engagement
Strategy

Meet

Their
Needs

Key

Player

Lower

Priority

Show

Considera4on

Stakeholder

Influence

Stakeholder
Influence
Stakeholder
Interest

What
is
a
CommunicaQon
Plan?

Plan
DeﬁniQon

−  A
wrihen
document
that
helps
an
organizaQon
achieve
its
goals
using
wrihen
and

spoken
words.

−  Describes
the
What,
Why,
When,
Where,
and
How

§  Importance
of
a
CommunicaQon
Plan

−  Gives
the
working
team
a
day-‐to-‐day
work
focus

−  Helps
stakeholders
and
the
working
team
set
prioriQes

−  Provides
stakeholders
with
a
sense
of
order
and
controls

−  Provides
a
demonstraQon
of
value
to
the
stakeholders
and
the
business
in
general

−  Helps
stakeholders
to
support
the
DG
Program

−  Protects
the
DG
Program
against
last-‐minute
demands
from
stakeholders


CommunicaQon
Plan

§  Brings
it
all
together:

−  Who
do
we
need
to
communicate
to?

−  What
informaQon
will
be
important
to
them?

−  Metrics
that
map
to
their
professional
and
personal
goals

−  How
frequently
should
they
be
updated?

−  What
is
the
method
of
communicaQon?

−  Who
should
be
communicaQng
to
them?


Components
of
a
CommunicaQon
Plan

Communica4on
Plan
Stakeholder:

XXX

QualitaQve
InformaQon
Any
general
qualitaQve
informaQon
that
I
would
like
to
receive
related

to
this
deliverable

QuanQtaQve
InformaQon
Of
the
quanQtaQve
metrics
that
have
been
deﬁned,
which
are
the
ones

I
would
like
to
be
informed
about
AND
how
do
I
want
the
metric

communicated
to
me
to
make
the
message
perQnent

Frequency
How
open
do
I
want
to
be
informed
about
progress

Method
What
is
my
preferred
mechanism
of
receiving
the
informaQon

Item Frequency Description Purpose Audience Documentation From Date Owner Status
Meetings
First BSL Meeting One-Time
Introduction
Get explicit buy-in from the participants and
resource ask
DGWG BSLs PowerPoint Presentation John 8/25/11 John Complete
DGWG Core Team Kickoff Meeting One-Time DGO kickoff and vision from IT Sponsor Kickoff DGWG-Core, IT Sponsor PowerPoint presentation John 9/15/11 John Complete
DGO Launch Logistics One-Time Communication announcing the DGO Plan on the best way to communicate the DGO
launch and PR effort
DGO, SVB Corporate
Communication
Email John TBD John Complete
DGO-DGWG-Core Status Meeting Weekly DGWG accomplishments, progress towards goals
and issues
Status DGWG-Core members SharePoint Agenda & Content John Ongoing Flo In progress
Meeting with DGO IT Lead Weekly Planning and strategy Status/Planning DGO Chair, DGO IT Lead and
DGC
John Ongoing John
DGO & MDM alignment meetings Weekly MDM Implementation update Status MDM team, DGO Chair & DGC Agenda Rebecca Ongoing Rebecca
Mentoring program
(Data Stewardship Program)
Weekly Opportunity to learn from Business Steward Leads.
Best practices, polices, processes, standards,
definitions
Enrichment DGWG Data Stewards Data Stewardship Best practices.
DGO Polices, processes,
standards, definitions
TBD TBD TBD Not Started
Meeting with Program Sponsors Bi-Weekly? Provide DGWG accomplishments, progress towards
goals and issues
Status DGO Chair, Biz and IT Sponsor PowerPoint presentation John TBD John Not Started
DGO-DGWG Decision
(Core & Advisory) Meeting
Monthly DGWG voting meeting Vote and approve DGWG materials DGWG members SharePoint Agenda & Content John Ongoing Flo In progress
DGO-DGWG - DM IT Support Group Meeting Monthly DGWG DM IT Support Group team monthly update Bring the advisory team up to speed on status
before the decision meeting
DGWG Advisory members SharePoint Agenda & Content John TBD Flo Not Started
EIC Meeting Monthly DGWG accomplishments, progress towards goals,
issues, documents for informational purposes only
Status, Informational EIC members PowerPoint presentation John Ongoing John In progress
Meeting with SAM - Fund Business stakeholders As needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started
Meeting with Purchasing stakeholders As needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started
Meeting with Product Implementation stakeholdersAs needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started
Meeting with Global Product stakeholders As needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started
DGO Town Halls One/Year DGWG accomplishments and progress towards goals
Forum for open discussion
Team Building All DGWG members PowerPoint presentation John TBD Flo Not Started
Sample
CommunicaQon
Plan

And
these
are
just
the

meeQngs!
Also:

• 
Awareness
&
Training

• 
CommunicaQon
Vehicles

• 
Knowledge
Sharing

• ….

Embedding
Data
Governance

Ensuring
DG
is
Sustainable

•  Incorporate
DG
goals
into
other
goals,

objecQves
and
incenQves
Incorporate

•  Align
DG
with
strategic
objecQves,

programs
and
projects
Align

•  Embed
DG
into
standard
project,
change

control,
new
iniQaQve
and
operaQonal

processes

Embed

•  Focus
on
delivering
business
value
Focus


Incorporate
IncenQves

Carrots
SQcks

Oversight
AllocaQon


Align
with
ObjecQves,
Programs
and
Projects

§  Examples:

§  Alignment
with
Stakeholder
goals
(already
discussed)

§  Alignment
with
Corporate
ObjecQves

§  Alignment
with
strategic
Programs/Projects


Example:
Alignment
with
Corporate
ObjecQves


Example:

Tie
Principles
to
Corporate
Strategic
ObjecQves

Corporate

Objec4ve

Principle

Client
Data
is
a
key
asset
to
our
company.
We
will
enhance
and
manage

this
asset
by
emphasizing
clear
strategies,
decisive
acQon,

innovaQon
and
results.

Capabili4es
Business
stakeholders
will
get
informaQon
delivered
at
the
right

Qme,
locaQon
and
amount
as
efficiently
as
possible.

Execu4on
Data
Governance
will
introduce,
support
and
drive

standardizaQon
of
enterprise
data.

Brand
Best
in
class
customer
data
quality
will
significantly
improve
both

the
internal
as
well
as
external
customer
experience.

People
Data
Governance
should
increase
producQvity
through

centralized,
streamlined
processes
and
eliminate
non-‐value
added

acQviQes.
Maximizing
automaQon
is
a
key
way
to
improve
human

resource
efficiencies
and
is
preferable
over
manual
processes.

Principles
drive
crea.on
and
execu.on
of
policies,
standards,
processes,
etc….


Program
/
Project
Alignment

Project

IniQaQon

Project

ExecuQon

Change

Control

OperaQonal

pg 72

Sample:
Embed
in
Project
IniQaQon
Process

IdenQfy

informaQon/

infrastructure

needs

Proﬁle
to
Iden4fy

data
issues

Analyze
to

Iden4fy
root

causes/
gaps

Design
solu4ons

to
root
cause

problems
/
gaps

Implement

process
&
Tech

soluQons

Sustain

Proac.vely
iden.fy
problems
and
solve
root
causes

Sample:

Embed
Data
Governance
Into
Your
Project
Methodology

Engage
DG,
DQ,
DA,

MDM,
Metadata

Leads

Assess
adherence
to

Guiding
Principles

Alignment

Workshop

Assess
adherence
to

Guiding
Principles

Engage
DG,
DQ,
DA,

MDM,
Metadata
Leads

Engage
DG,
DQ,
DA,

MDM,
Metadata
Leads

AddiQonal
DG,
DQ,
DA,
MDM
and
Metadata
related
deliverables
added
to
‘typical’

list:

Data
Profiling
Reports,
New/modified
Score-‐cards,
AddiQonal
Metadata,
New/
modified
Processes,
Data
Model
Reviews,
etc

Engage

DG,
DQ,

DA,
MDM,

Metadata

Leads

Engage

DG
Lead

Sample:

Embed
Data
Governance
with
Change
IniQators/Control

A
process
ﬂow
will
help
ensure
consistent
change

requests
related
to
data

Sample:
OperaQonal
Process
(Client
On-‐Boarding)

New
Client

Request

DocumentaQo
n
&
Due

Diligence

Terms

conﬁrmed

Agreement
/

Contract

Created

Create
Client

• ExisQng
or
Previous
Client

(Master
Data
Check)

• Data
Standards
and

ValidaQon

• Data
Quality
Check

• Regulatory
Checks

• RACI
/
Data
Ownership

• Data
Enrichment

• Data
ClassiﬁcaQon

• Data
RemediaQon

• Decision
Making
/
EscalaQon

Processes

• Hierarchy
/
RelaQonship

Check

• Client
SegmentaQon

• Contract
Management

• Document

Management

• Update
Master
Data

• Create
Hierarchies

• Data
Standards
and

ValidaQon

• Data
Quality
Check

• Data
Sharing,
Access
&
Use

Policy

• …

Sample:
OperaQonal
Process
(Unique
Device
IdenQﬁcaQon

Management)

IdenQfy
Products

for
Submission

IdenQfy
Data

Sources

Proﬁle
Product

Data

IdenQfy
and

Address
DQ

Issues

Aggregate
Data

Cleanse
/
Enrich

Data

Review
/
Approve

Data
for

Submission

Submit
Data
and

resolve
errors

Publish
data
for

internal
/external

consumpQon

ArQfacts
needed
for
IdenQfying
Product
Data
&
Sources

IdenQfy
Products

for
Submission

IdenQfy
Data

Sources

Proﬁle
Product

Data

IdenQfy
and

Address
DQ

Issues

Aggregate
Data

Cleanse
/
Enrich

Data

Review
/
Approve

Data
for

Submission

Submit
Data
and

resolve
errors

Publish
data
for

internal
/external

consumpQon

Data
DicQonary
/
Business
Glossary

Data
Inventory

Data
Flow
Diagrams

Product
Data
Hierarchies

Data
Standards

Data
Ownership
and
RACI
matrices

ArQfacts
needed
for
Profiling
and
Addressing
DQ

IdenQfy
Products

for
Submission

IdenQfy
Data

Sources

Profile
Product

Data

IdenQfy
and

Address
DQ

Issues

Aggregate
Data

Cleanse
/
Enrich

Data

Review
/
Approve

Data
for

Submission

Submit
Data
and

resolve
errors

Publish
data
for

internal
/external

consumpQon

• Data
Quality
Standards

• Data
Quality
Rules

• Data
Profiling
SoluQons

• Data
RemediaQon
Processes

• Decision
Making
&
EscalaQon

Processes

ArQfacts
needed
for
AggregaQng,
Cleansing,
Enriching

IdenQfy
Products

for
Submission

IdenQfy
Data

Sources

Proﬁle
Product

Data

IdenQfy
and

Address
DQ

Issues

Aggregate
Data

Cleanse
/
Enrich

Data

Review
/
Approve

Data
for

Submission

Submit
Data
and

resolve
errors

Publish
data
for

internal
/external

consumpQon

• Product
Hierarchies
and
RelaQonships

• Match
/
Merge
Rules

• Data
ValidaQon
and
Cleansing
Rules

• Data
AcquisiQon
Policies
(Purchasing
and

IntegraQng)

• ExcepQon
and
Error
Handling
Processes

ArQfacts
needed
for
Review,
Approve,
&
Submit

IdenQfy
Products

for
Submission

IdenQfy
Data

Sources

Profile
Product

Data

IdenQfy
and

Address
DQ

Issues

Aggregate
Data

Cleanse
/
Enrich

Data

Review
/
Approve

Data
for

Submission

Submit
Data
and

resolve
errors

Publish
data
for

internal
/external

consumpQon

• Data
Profiling

• Data
Management
Workflows

• RACI
Matrices

• Decision
Making
and
EscalaQon
Processes

• Approval
Process

• ExcepQon
and
Error
Handling
Process

• Measurement
and
Monitoring
of
the
process

ArQfacts
needed
to
Publish
&
Manage

IdenQfy
Products

for
Submission

IdenQfy
Data

Sources

Proﬁle
Product

Data

IdenQfy
and

Address
DQ

Issues

Aggregate
Data

Cleanse
/
Enrich

Data

Review
/
Approve

Data
for

Submission

Submit
Data
and

resolve
errors

Publish
data
for

internal
/external

consumpQon

• Data
Sharing
Policies
(Usage,
Access
Rights)

• Data
RetenQon

• Training
and
CommunicaQon

Ensuring
Success

Principle
Descrip4on

Be
clear
on
purpose
Build
governance
to
guide
and
oversee
the
strategic
and
enterprise
mission

Enterprise
thinking
Provide
consistency
and
coordinaQon
for
cross
funcQonal
iniQaQves.
Maintain
an
enterprise
perspecQve
on

data

Be
flexible
If
you
make

it
too
difficult,
and
people
will
circumvent
it.

Make
it
customizable
(within
guidelines),
and

people
will
get
a
sense
of
ownership

Simplicity
and
usability
are
the
keys
to

acceptance

Adopt
a
simple
governance
model
people
can
use.

A
complicated
and
inefficient
governance
structure
will

result
in
the
business
circumvenQng
the
process

Be
deliberate
on
par4cipa4on
and
process
Select
sponsors
and
parQcipants.
Do
not
apply
governance
bureaucracy
solely
to
build
consensus
or
to

saQsfy
momentary
poliQcal
interest

Enterprise
wide
alignment
and
goal
congruence
Maintain
alignment
with
both
enterprise
and
local
business
needs.
Guide
prioriQzaQon
and
alignment
of

iniQaQves
to
enterprise
goals

Establish
policies
with
proper
mandate
and

ensure
compliance

Clearly
define
and
publicize
policies,
processes
and
standards.
Ensure
compliance
through
tracking
and

audit

Communicate,
Communicate,
Communicate!

Frequent,
directed
communicaQon
will

provide
a
mechanism
for
gauging
when
to

“course
correct”,

manage
stakeholder
and
effecQveness
of

the
program

Governance
Design
Principles


Ensuring
Success

§  The
following
factors
are
usually
evident
in
a
successful
program:

−  First
create
a
strategy
and
then
follow
it
(agreed
on
starQng
point
&
steps

necessary)

−  Ensure
solid
alignment
between
Business
&
IT

−  Clearly
deﬁned
and
measureable
success
criteria

−  Small
iteraQons
vs.
all
or
nothing

−  ExecuQve
sponsorship
is
criQcal

−  IdenQfy
and
assess
the
importance
of
key
people
and
or
groups

−  Really
know
your
data

−  Leverage
prior
experience/work…don’t
re-‐invent
the
wheel

−  Embed
governance
into
the
operaQons
of
your
company

−  Communicate,
Communicate,
Communicate!


Thank
you!

Kelle
O’Neal

kelle@ﬁrstsanfranciscopartners.com

415-‐425-‐9661

@1stsanfrancisco

Appendix
1
Roles
&
ResponsibiliQes


Direc4on

TBD

Execu4ve
Sponsor

Business
&
IT

Business
Data
Stewards

Data
Governance
Steering
Commi<ee

Business
Unit

Oﬃcers

Data
Owners
IT
Partner(s)

Data
Governance
Oﬃce
(DGO)

Management

Program
Oversight.
Allocates
budget
&

resource.
Empower
Business
Data

Stewards.
Forum
for
issue
escalaQon.

Craps
the
Enterprise
Data
Strategy,

processes
and
standards
to
ensure
that

data
is
managed
as
an
asset.

Execu4ve
Level

Management

Level

Stewards
data
within
their
BU
to
ensure

that
the
enterprise
policies,
standards
&

processes
are
applied.

Tac4cal

Level

Strategic
Level

Provides
overall
strategic

direcQon,
budget

&
resource
approvals.
Forum
for
issue

escalaQon.
Approval
of
data
domains
under

governance
control.

Execu4on

Technical

Data
Stewards

Local
Data
Governance
Working
Groups

Reference
OperaQng
Model

Business
&
Technical
Data
SMEs


Core
Data
Governance
Roles

Role
Common
Aliases
Insights

Execu4ve
Sponsor
(Business)
In
an
ideal
state
there
is
execuQve
sponsorship
in
both
Business

&
IT.
If
there
is
a
single
sponsor,
look
to
the
Business.

Data
Owner
(Business)
Business
Data
Owner,
Accountable

ExecuQve,
Business
Steward
Lead

Probabili4es:

-‐“Owner”
may
not
be
accepted
by
culture

-‐May
not
be
able
to
idenQfy
“Owners”

Large/Complex
Organiza4ons:
May
need
both
Data
Owner
and

Business
Steward
Lead

Data
Steward
(Business)
Business
Data
Steward,
Data
Custodian,

Chief
Data
Steward

It’s
all
about
the
details.
Never
assume
the
R&R’s
based
on
the

Qtle.

Technical
Data
Steward
(IT)
Technical
Lead,
IT
Support
Partner

Data
Architect
(IT)
Open
part
of
Enterprise
Architecture

Member
of
Architecture
Review
Board
(ARB)

May
not
exist,
however
responsibiliQes
should
be
assigned

Business
Analyst
(Business)
BA’s
with
Data
Governance
experience
are
extremely
valuable

and
provide
criQcal
support
to
the
Data
Stewards.

Data
Governance
Oﬃce
Lead
DGO
Lead


SupporQng
Roles

Role
Common
Aliases
Insights

Data
Analyst
Business
Data
Analyst,
Technical
Data

Analyst

Data
Architect
InformaQon
Architect
(IA)
Diﬀerent
from
an
Enterprise
Architect,
open
part
of
Enterprise

Architecture

Member
of
ARB

If
ARB/EA
funcQons
don’t
exist:
Assign
responsibiliQes.

Data
Quality
Analyst

Librarian
Knowledge
Worker
Common
in
MDM
Programs,
more
so
when
MDM
technology
is
in
place.

Role
is
dedicated
to
Data
Maintenance
acQviQes
associated
with
Data

Governance.

Data
SME
Subject
Maher
Expert,
Knowledge
Worker,

User,
Data
Entry
Clerk

SME’s
can
be
found
in
the
Business
&

IT,
and
at
all
levels
in
an

organizaQon.

In
some
organizaQons
a
“SME”
is
considered
highly

skilled,
respected
role.


Leadership
Roles
&
Decision
Making
Bodies

pg
91

ExecuQve
Sponsor
(Business
and
IT)

§  Chairs
the
Data
Governance
Steering
Commihee

§  UlQmate
authority
and
responsible
for
overall
program
direcQon

§  Provides
overall
strategic
vision

§  Sets
strategy
and
direcQon
for
Data
Governance
&
Management

§  Works
with
the
Data
Governance
Office
to
formulate
the
data

governance
strategy

§  Sets
direcQon
for
the
Data
Governance
Working
Group
(DGWG)

and
ensures
that
the
implementaQon
is
in-‐line
with
the
strategy

§  Conveys
the
data
management
and
governance
strategy
to
the

other
Exec
Commihees

§  Clarifies
business
strategies
to
the
DGWG

§  Provides
reinforcement
to
enable
the
success
of
data
governance

through
communicaQon

§  Gathers
funding
and
resource
availability
for
the
governance

program

§  Approves
changes
to
the
data
governance
strategy

Resources:
Virtual

Primary
ResponsibiliQes

Set
Strategy
and
Steer

Skills/CapabiliQes

§  Generally
a
Corporate
ExecuQve/Officer
of
Company

§  Recognized
cross-‐funcQonal
leadership
and
influencing
skills

§  PoliQcally
astute

!
Roles
may
be
different
in

large
or
complex

organizaQons
,
i.e.
the
DGO

Lead
can
run
the
DGSC

Data
Governance
Steering
Commihee

Resources:
Virtual

Primary
ResponsibiliQes

Membership

§  Chaired
by
the
ExecuQve
Sponsor
(Business)

§  IT
Sponsor

§  Cross
LOB
execuQves

§  Data
Owners

§  Data
Governance
Oﬃce
Lead
(usually
non-‐voQng)

§  IT
Partner

Oversight

§  Brings
corporate
and
cross
LOB
perspecQve

§  Approves
budget
and
allocates
funding

§  Approves
funding
for
enhancements

§  Appoints
and
approves
data
governance
resources

§  Nominates,
selects
and
empowers
and
mandates
the
DGWG

§  Ensures
strategic
alignment
between
DG
program
and
other

business
unit
iniQaQves

§  Ensures
strategic
alignment
with
corporate
objecQves

§  Adjudicates
intractable
issues
that
are
escalated
by
the
Data

Governance
Working
Group
(DGWG)

§  Approves
funding
for
enhancements

§  Enforces
the
data
governance
polices,
processes
and
standards
for

the
organizaQon

§  Approves
changes
to
the
data
governance
strategy

§  Has
the
ﬁnal
say
in
all
data
governance
decisions

§  Owns
key
data
assets
across
enterprise

!

May
have
addiQonal

decision
making
bodies
in

large
or
complex

organizaQons

Data
Governance
Working
Group

Resources:
Virtual

Primary
ResponsibiliQes

§  Data
Governance
Office
Lead

§  Data
Stewards

§  Technical
Data
Stewards

§  Business
&
Technical
Data
SMEs

§  Key
Stakeholders

Management
&
ExecuQon

Membership

§  Governing
body
across
Business
and
IT
funcQons
that
own
data

definiQons
and
provide
guidance
&
enforcement
to
drive
change

in
use
and
maintenance
of
data

§  Defines
data
polices,
processes
and
standards

(PPS)

§  PrioriQzes
opportuniQes
to
develop
data
polices,
processes
and

standards,
and
iniQates
data
quality
iniQaQves

§  Advises
data
stewards
on
the
development
and
maintenance
of

the
data

PPS

§  Assists
in
the
approval
and
enforcement
of
data
data
PPS

§  Assess
compliance
and

manages
risk

§  Resolves
issues
that
have
been
escalated
to
the
DGWG

§  Approves
data
polices,
processes
and
standards

§  Reviews
and
approves
appeals
and
excepQons;
escalates
rare

excepQons

!

Local
DGWG
for
large
or

complex
organizaQons

Led
by
the
Data
Owner,

Business
Data
Lead
or
Data

Steward

Data
Governance
Roles

Data
Owner

§  Member
of
Data
Governance
Steering
Commihee

§  Accountable
for
represenQng
the
Business
Unit
and
corporate

interests
from
an
Enterprise
perspecQve

§  Accountable
for
the
Business
Unit
at
the
Data
Governance
Steering

Commihee

§  IdenQﬁes
and
prioriQzes
issues
and
suggested
enhancements
from

end
users

§  Helps
to
promote
the
data
governance
program
across
the

Enterprise

§  Serves
as
an
escalaQon
point
for
all
data
governance
issues
for
the

Data
Steward
and
Data
Governance
Working
Group

§  Works
with
other
Data
Owners
to
idenQfy
and
resolve
speciﬁc
data

quality
issues

§  Responsible
for
ensuring
compliance
with
data
governance
policies

and
standards
across
the
Enterprise
and
within
the
Business
Unit

§  Seeks
and
manages
funding
for
iniQaQves
to
improve
data
quality

§  Trains,
educates,
and
creates
awareness
for
members
in
their

respecQve
funcQonal
areas

Resources:
Virtual

Primary
ResponsibiliQes

§  Business
RepresentaQve

§  Ability
to
syndicate
and
achieve
organizaQonal
change
in
a

decentralized
environment

§  Demonstrated
program
management
and
enterprise-‐wide

coordinaQon
experience

§  Expert
communicaQon
skills
(verbal
and
wrihen)
with
the
ability
to

communicate
complex
issues
/
requirements
to
technical
and
non-‐
technical
audiences
as
well
as
educate
the
business
about
data

management

Manage

CapabiliQes/Skillsets

!

“Data
Owner”
may
not

be
embraced

Complexity/Scope
may
require

addiQonal
layers/roles
to

execute
at
the
enQty
level

Business
Steward
Lead

for
represenQng
the
LOB
and
corporate
interests
from
an

enterprise
perspecQve

§  Represents
the
LOB
at
the
Data
Governance
Working
Group
(DGWG)

§  IdenQfies
and
prioriQzes
issues
and
suggested
enhancements
from
end
users

§  Helps
to
promote
the
data
governance
program
across
the
enterprise

(primarily
within
their
LOB)

§  Defines
polices
and
standards
to
ensure
data
quality
within
the
LOB

§  Sets
goals
on
how
to
manage
business
informaQon
beher

§  Serves
as
an
escalaQon
point
for
all
data
governance
issues
within
the
LOB

§  IdenQfies
and
resolves
LOB-‐specific
data
quality
issues;
works
with

appropriate

§  Responsibility
for
ensuring
compliance
with
data
governance
policies
and

standards
within
the
LOB

§  Seeks
and
manages
funding
for
iniQaQves
to
improve
data
quality

§  Trains,
educates,
and
creates
awareness
for
members
in
their
respecQve

funcQonal
areas

Resources:
Virtual

Primary
ResponsibiliQes

•  Solid
knowledge
and
understanding
of
the
business,
organizaQon,
and

funcQonal
area

•  Excellent
communicaQon
skills
(wrihen
and
oral)

•  FacilitaQon
and
consensus
building
skills

•  Ability
and
willingness
to
work
as
part
of
a
team

•  Ability
to
funcQon
independently

•  ObjecQvity,
CreaQvity
and
Diplomacy

Execute


!

Business
Steward

lead
takes
the
place

of
the
“Data

Owner”

Data
Steward
(Business)

§  Develop
policies
and
standards
to
ensure
data
quality;
has
overall

accountability
for
data
quality

§  Ensures
compliance
with
data
governance
policies
and
standards

§  UlQmately
accountable
for
the
execuQon
of

the
data
governance

strategy

§  Ensures
that
all
policies,
standards,
escalaQons,
and
decisions
follow

the
predefined
processes

§  Performs
root
cause
and
impact
analysis

and
accountable
to
Business
Steward
Lead
for
the
subject

maher
knowledge
within
a
parQcular
LOB

§  Works
on
Data
Governance
Working
Group
(DGWG)
when
assigned
to

specific
requests
and
projects

§  Works
with
the
Business
Steward
leads
to
help
define
metrics
to

measure
and
monitor
data
quality

§  Ensures
consistency
of
data
quality
processes
within
an
LOB

§  Resolves
daily
data
quality
operaQonal
issues
and

performs
root

cause
analysis
to
idenQfy
point
of
failure

§  ParQcipates
in
the
wriQng
of
data
definiQons
and
genealogy

Resources:
Virtual

Primary
ResponsibiliQes


§  Experience
developing
standards,
processes
and
policies

§  Exposure
to
mulQple
business
units
in
relevant
industry
in
order
to

understand
linkages
and
dependencies

§  Ability
to
understand
upstream
and
downstream
needs

§  Can
represent
a
broader
view
(beyond
LOB)

§  Knowledge
of
data
/
content
management

§  Experience
with
technical
wriQng

§  Excellent
oral
/
wrihen
communicaQon

Execute


Data
SME
(Business
and
IT)

§  Member
of
Data
Governance
Working
Groups
for
specific
data
domain(s)

§  Recognized
experts
within
the
organizaQon

§  May
not
be
officially
responsible
for
managing
an
Domain
but
may
be

consulted
on
topics
related
to
Domain

§  Considered
a
data
“go-‐to”
person
within
their
Business
Unit

§  Deep
understanding
of
use
and
impact
of
data
within
and
across
Business

Unit

§  Ability
to
parQcipate
in
development
of
standards,
processes
and
policies

§  Ensures
compliance
with
data
governance
policies
and
standards

§  Ensures
that
all
policies,
standards,
escalaQons,
and
decisions
follow
the

predefined
processes

§  Performs
root
cause
and
impact
analysis

and
accountable
to
Data
Steward
for
the
subject
maher

knowledge
within
Business
Unit.

§  Resolves
daily
data
quality
operaQonal
issues
and

performs
root
cause

analysis
to
idenQfy
point
of
failure

§  ParQcipates
in
the
wriQng
of
data
definiQons
and
genealogy

§  Works
with
other
SMEs
and
the
data
steward
to
idenQfy
and
address
data

interdependencies
across
businesses
and
funcQons

§  Work
with
other
SMEs
and
the
data
steward
to
resolve
issues

§  Drive
awareness
and
adopQon
of
policies,
standards
and
business
rules

Resources:
Virtual

Primary
ResponsibiliQes


§  Business
RepresentaQve

§  Considered
a
data
“go-‐to”
person
within
their
business
unit

§  Deep
understanding
of
use
and
impact
of
data
within
and
across
business
unit

§  Ability
to
parQcipate
in
development
of
standards,
processes
and
policies

§  Exposure
to
mulQple
business
units
in
relevant
industry
in
order
to
understand

linkages
and
dependencies

§  Knowledge
of
data
/
content
management

§  Excellent
oral
/
wrihen
communicaQon

Execute


Business
Data
Analyst

§  Supports
the
Business
Data
Steward

§  Work
with
Technical
Data
Stewards
and
IT
Data
Governance
resources
to

support
data
modeling,
metadata
and
data
quality
acQviQes.

•  Leverage
well
thought
out
methodology
applying
speciﬁc
data
enQty
and

business
process
experQse.

•  Provide
metrics
and
reporQng
support
(both
adhoc
and
repeQQve)
to
data

management
programs
and
Data
Governance

•  Make
recommendaQons
for
correcQng
and
prevenQng
errors
and
defects

that
include
process
changes,
data
cleansing
and
integrity
rule
updates.

•  DocumenQng
the
types
and
structure
of
the
business
data
(conceptual
&

logical
modeling)

•  Analyze
and
mine
business
data
to
idenQfy
paherns
and
correlaQons
among

the
various
data
points

•  Design
and
create
data
reports
and
reporQng
tools
to
help
business

execuQves
in
their
decision
making

Resources:
Dedicatedl

Primary
ResponsibiliQes


§  Strong
relaQonship
with
technical
staﬀ

§  Ability
to
map
and
tracing
data
from
system
to
system
in
order
to

solve
a
given
business
or
system
problem

§  Ability
to
perform
staQsQcal
analysis
of
business
data

§  Able
to
translate
business
quesQons
into
data
requirements
to
IT

§  Able
to
analyse
large
sets
of
complex
datasets,
examining
for
both

standard
and
anomalies
of
data

Execute


Support
Roles

Resources:
Dedicated

Execute
-‐
TacQcal

Role
Primary
ResponsibiliQes

Data
Librarian

§  Uses
well
documented
"playbooks",
execute
manual

data
remediaQon/data
cleansing
acQviQes.

§  Execute
manual
processes
to
close
the
gap
on
key
data

that
cannot
be
ﬁxed
by
automaQon
tools
and

technology.

§  Apply
the
established
data
quality
playbook
of
policies

and
processes
to
the
data
i.e.
IdenQfy
and
remediate

duplicate
records,
improve
completeness
for
criQcal

data
ahributes.

Data
Users

§  Deﬁnes
business
requirements

§  Understands
the
data’s
term
of
use

§  Complies
with
data
governance
policies

§  Involved
in
accessing
and
invesQgaQng
integrated

datasets
for
staQsQcal
and
research
purposes


The
Data
Governance
Oﬃce

Data
Governance
Oﬃce

§  Documented
DG
Strategy,
Vision,
Mission,
ObjecQves

§  Documented
DQ,
MDM/RDM
and
Metadata
Management
Strategies

§  Documented
DG
Guiding
Principles

§  Documented
roles
&
responsibiliQes
of
the
various
members

§  Up
to
date
OperaQng
Model

§  RACI
matrices

§  Templates
for
Policies
and
Processes

§  Templates
for
capturing
metrics
and
measurement
requirements

§  Templates
for
steering
commihee
meeQngs

§  Training
Plans

Plans

§  Template
for
regular
DG
communicaQon

§  Templates
for
logging
issues
needing
escalaQon
and
eventual
resoluQon

§  Templates
for
new
DG
service
requests

§  Checklists
for
new
projects
to
ensure
adherence
to
DG
standards

Resources:
Dedicated

Primary
ResponsibiliQes

Lead,
Advise
&
Support

Data
Governance
Oﬃce

Data
Quality
Management

MDM
Management

Metadata
Management

Coordinator/

Program

Manager

Data

Governance

Lead

!

Strong
Partnership

between
the
DGO

and
IT
DG

OrganizaQons

Data
Governance
Office
Lead

§  Co-‐chairs
Data
Governance
Working
Group
(DGWG)
meeQngs
with
the
IT

lead

§  Establishes
policies
and
guidelines
through
DG
best
pracQces

§  Oversee
the
integrity,
usability,
and
security
of
all
data
assets
under
DGO

purview

for
enterprise-‐wide
change
management
and
communicaQons

for
DG
efforts

§  Defines
the
polices,
processes
and
standards,
metrics
and
processes
for
data

quality
checks,
invesQgaQons,
and
resoluQon
across
organizaQon

§  Advises
business
on
data
polices,
processes
and
standards

§  Develops
the
DG
strategy
based
on
direcQon
set
by
the
execuQve
sponsors

§  Resolves
conflicts
on
data
across
business
and
technical
areas

§  Works
closely
with
IT
lead
to
develop
and
evolve
data
governance

communicaQons
and
branding

§  Trains,
educates,
and
creates
awareness
for
members
in
their
respecQve

funcQonal
areas

Resources:
Dedicated
FTE

Primary
ResponsibiliQes


•  Extensive
leadership
experience
with
experQse
in
Data
Governance

•  PoliQcally
astute
and
ability
to
communicate
effecQvely
both
up
and
down

•  Ability
to
effecQvely
monitor
industry
and
regulatory
acQviQes
and
assess
impact
on
Data

Governance
pracQce

•  Excellent
listening,
wrihen,
and
oral
communicaQon
skills

•  Strong
organizaQonal,
analyQcal,
and
decision-‐making
skills

•  Ability
to
anQcipate
and
solve
problems

•  Proven
business
leader
with
technology
delivery
capabiliQes

•  EnergeQc,
innovaQve,
and
flexible

Lead
and
Advise

Data
Governance
Office

Data
Quality
Management

MDM
Management

Metadata
Management

Coordinator/

Program

Manager

Data

Governance

Lead


Data
Governance
Office
Coordinator/Program
Manager

§  Provides
administraQve
support
to
the
DGO

§  Assists
with
the
administraQve
funcQon
of
the
Data
Governance
Office

§  Solicits
meeQng
agenda
items
and
drap
agenda

§  Prepares
and
distributes
the
agenda
and
other

§  Publishes
meeQng
minutes

§  Updates
issue
log
with
resolved
agenda
items

§  Coordinates
follow-‐up
acQviQes
regarding
acQon
items,
issues,
and
risks

§  Records
key
decisions

§  Tracks
resoluQon
of
program
issues
and
follow
up

§  Documents
issues
details,
and
updates
issue
log
with
resolved
agenda
items

§  Coordinates
follow-‐up
acQviQes
regarding
acQon
items,
issues,
data

documentaQon
in
projects
and
risks

§  MeeQng
management:
prepares
and
distributes
the
agenda
and
other

related
material
to
the
members.
Produces

minutes
and
ensures
they,
and

related
documents
are
published
to
a
common
collaboraQon
area
accessible

by
all
stakeholders

§  Gathers
and
reports
DG
metrics

§  Manages
data
definiQon
reviews,
approvals
for
important
data
concepts

Resources:
Dedicated

Primary
ResponsibiliQes


•  Advanced
knowledge
of
project/program
management
tools
and
disciplines

•  Proven
problem
solving
skills

•  High-‐degree
of
credibility
and
influence
within
the
business
and
technology

•  Strong
coordinaQon,
interpersonal,
and
facilitaQon
skills

•  Analysis,
evaluaQon,
and
ability
to
arQculate
value
of
projects/programs
and
make

informed
and
objecQve
recommendaQons

•  CommunicaQon
across
project
members/parQcipants
to:
address
issues,
track
and

convey
status,
provide
guidance,
assign,
manage
issues,
manage
scope

•  Capacity
to
work
across
projects/releases
simultaneously

Administra4ve/Suppor4ng

Data
Governance
Office

Data
Quality
Management

MDM
Management

Metadata
Management

Coordinator/

Program

Manager

Data

Governance

Lead


IT
Data
Governance
Roles

Data
Governance
Office
-‐
IT
Lead

§  Co-‐chairs
Data
Governance
Working
Group
(DGWG)
meeQngs
with
the

Business
lead

for
represenQng
technology
interests
from
an
enterprise

perspecQve

for
ensuring
the
technical
implementaQon
and
execuQon
of

DGSC
and

DGWG
decisions
unless
otherwise
specified

§  IdenQfies
issues
and
suggested
enhancements
for
all
users

for
the
Data
Management
IT
support
Group

§  Custodian
of
informaQon
across
the
enterprise

§  Member
of
Architecture
Review
Commihee
(ARC)

§  Advises
technical
resources
on
data
standards
and
ensures
technical
designs

adhere
to
data
architectural
best
pracQces
to
ensure
data
quality

§  Resolves
conflicts
on
data
across
business
and
technical
areas

§  Advises
on
implementaQon
of
data
polices,
processes
and
standards

§  Trains,
educates,
and
creates
awareness
for
members
in
their
respecQve

funcQonal
areas

Resources:
Virtual
or
Dedicated

Primary
ResponsibiliQes


•  Extensive
leadership
experience
with
experQse
in
Data
Governance

technologies
and
implementaQon

•  Understanding
of
implicaQons
of
policies
and
standards
on
data
structures

•  Excellent
listening,
wrihen,
and
oral
communicaQon
skills

•  Ability
to
anQcipate
and
solve
problems

•  Proven
technology
delivery
capabiliQes

•  EnergeQc,
innovaQve,
and
flexible

Lead
and
Advise

IT
Data
Governance

Data
Quality
Support

MDM
Support

Metadata
Support

IT
Governance
Lead


Data
Management
IT
Support
Group

§  Collaborate
with
the
DGWG
to
provide
the
day-‐to-‐day
custodianship
of
data

assets

§  Partners
with
the
DGO
to
support
and
evolve
the
Data
Governance
Program

§  IdenQfy
business
data
requirements
by
working
with
all
data
consQtuents

§  Create
metrics
and
measures
to
help
analyze
and
report
on
the
quality
of
the

data

§  Communicates
with
DGO
regarding
data
issues

§  Validates
data
quality
rules
and
prioriQze
data
quality
issue
resoluQon

§  Stores,
retains
and
disposes
of
data
as
per
requirements

§  Designs
technical
infrastructure
to
meet
requirements
(i.e.
data
architecture)

§  Trained
and
fully
knowledgeable
about
data
governance
processes
and

standards
and
conﬁrms
adherence

§  Performs
impact
analysis
for
changes
to
exisQng
data
sources,
informaQon

architecture,
security,
etc.

§  Ensures
security
of
informaQon

§  Maintains
enterprise
data
model

§  Support
ongoing
data
cleansing
and
data
migraQon

Resources:
Virtual

Primary
ResponsibiliQes

Execute

IT
Data
Governance

Data
Quality
Support

MDM
Support

Metadata
Support

IT
Governance
Lead

!

Strong
Partnership

between
IT
DG
and

DGO
OrganizaQons

Technical
Data
Steward
(IT
Support
Partner)

§  Supports
the
Data
Steward
for
specific
data
domain(s)

§  Member
of
Data
Governance
Working
Group
(DGWG),
Collaborates
with
the

DGWG
to
provide
the
day-‐to-‐day

custodianship
of
data
assets

for
designing
technical
infrastructure
to
meet
requirements
(i.e.

data
architecture)

for
managing
technical
aspects
of
Data
Governance,
e.g.,
technical

metadata,
data
modeling

§  Assists
in
compliance
with
data
standards,
policies

§  IdenQfy
business
data
requirements
by
working
with
all
data
consQtuents

§  Create
metrics
&
measures
to
help
analyze
and
report
on
the
quality
of
the

data

§  Communicates
with
DGO
regarding
data
issues

§  Validates
data
quality
rules
and
prioriQze
data
quality
issue
resoluQon

§  Stores,
retains
and
disposes
of
data
as
per
requirements

§  Performs
impact
analysis
for
changes
to
exisQng
data
sources,
informaQon

architecture,
security,
etc.

§  Ensures
security
of
informaQon
according
to
exisQng
InformaQon
Security

Policy

§  Support
ongoing
data
cleansing
and
data
migraQon
Resources:
Virtual

Primary
ResponsibiliQes


§  Able
to
translate
business
quesQons
into
technical
requirements

§  Able
to
analyze
large
sets
of
complex
datasets,
examining
for
both

standard
and
anomalies
of
data

§  Have
specific
data
enQty
knowledge,
understands
the
business

process
that
uses
the
data
and
is
generally
responsible
for
technical

development/enhancement/support
in
one
or
more
applicaQons
or

data
sources
that
house
the
data.

§  Has
subject
maher
experQse
in
specific
data
enQty

Execute


Best
PracQces

Ensuring
Success

§  Don’t
need
to
re-‐invent
the
wheel:
Leverage
exisQng
execuQve
leaderships
and
decision
making

forums
with
expanded
decision
making
responsibiliQes

§  Prevent
“delegated
responsibiliQes”
in
decision
making
bodies
–
require
“peer
proxy”
at
the

execuQve
level

§  Titles
need
to
fit
the
culture:
Focus
on
the
Roles
&
ResponsibiliQes,
not
the
name
of
the
role

§  One
individual
may
fill
mulQple
roles:
Role-‐based
not
job-‐based

§  Ensure
robust
support
for
Data
Stewards
with
key
support
roles:
Business
Analyst,
Data
SMEs.

§  Know
the
program
focus
and
fill
roles
accordingly:
Example:
A
focus
on
Data
Quality
will
require

experienced
Data
Analysts,
BA’s,
IT
Roles

§  Establish
PracQce
Groups/Centers
of
Excellence
to
support
and
evolve
the
program,
provide

educaQon
and
training:

§  Business
Data
Steward
PracQce
Group
(chaired
by
DGO
Lead)

§  Technical
Data
Steward
PracQce
Group
(chaired
by
IT
DG
Lead)

§  Where
possible,
make
roles
“official”:

§  Job
DescripQons
Updated;
Goal
Alignment


Appendix
2
Metrics

Sample
StaQsQcs

•  Improve
eﬃciencies
by
10%
in
1
year,
building
to
40%
by
year
3

•  Reduce
New
Project
iniQaQon
cost
by
50%

•  Project
delays
due
to
bad
data
resulted
in
cost
and
Qme
increase
1.5x
iniQal
esQmate

•  Reduce
number
of
duplicate
records
by
30%
in
1
year

•  Improve
ability
to
respond
to
regulatory
concerns
thereby
saving
up
to
$5M
in
external
fees
per

incident

§  ConsulQng
fees
are
$9m
for
audit;
40%
of
audit
cost
spent
on
gathering
data

§  Employee
burn
out
was
consistently
cited
as
major
risk
of
maintaining
the
status
quo

§  Credit
Risk
RaQng
remediaQon
project
resulted
in
over
2,500
hours
of
eﬀort,
largely
redundant
data

processing

§  Global
expansion
(addiQonal
markets
and
24x7
processing
)
will
conQnue
to
exert
pressure
on

manual
processes,
further
increasing
the
likelihood
of
an
oversight
leading
to
reporQng
mistakes
or

noncompliance

pg 113

Improved
ProducQvity


Eﬃciency

§  Total
cost
of
IT
/
Party
(Customer,
Member,
etc.)

§  End
User
Labor
/
Number
Users

§  Total
BI/DW
Budget
/
Total
Users,
and
/
or
Support

§  Number
of
interfaces,
File
feeds

§  Cost
per
Interface

§  DG
/
Compliance
cost
divided
by
Total
Income

§  DG
/
Compliance
cost
vs.
risk
reserves
/
premiums

§  Budget
/
TB
(GB)

§  Benchmarks

−  Number
IT
tools

−  Maintenance
budgets

−  License
costs

−  Training
costs

Courtesy
of
John
Ladley,

IMCue
SoluQons

pg 115

Data
Impact
on
ProducQvity

Manual
Data
RemediaQon

Data
Access
and
Retrieval

Project
and
project
iniQaQon
costs

CalculaQng
ProducQvity
Impact

Produc4vity
Loss
Inputs
Quan4ﬁca4on
of
Impact

Manual
Data

ReconciliaQon

• 
Employee
Time

• 
Opportunity
Costs

$
Impact
=
E1(Hours
x
FTE
cost)
+
E2(Hours
x
FTE
cost)
+
...

+En(Hours
x
FTE)
+
[opportunity
cost
of
E1,
2,
3,
...
n]

Data
Access
and

Retrieval

• 
Above

• 
Incremental
costs
of

hardware
sopware
and

process

$
Impact
=
[E1(Hours
x
FTE
cost)
+
E2(Hours
x
FTE
cost)
+
...

+En(Hours
x
FTE)]
+
[opportunity
cost
of
E1,
2,
3,
...
n]
+

(incremental
cost
of
data
sopware,
hardware,
and
support)

Project
Delays
• 
Above

• 
Increased
consulQng

fees

• 
Opportunity
cost
of

delay

$
Impact
=
[E1(Hours
x
FTE
cost)
+
E2(Hours
x
FTE
cost)
+En(Hours
x

FTE)]
+
ΔconsulQng
cost
+
Δsupport
cost
+
opportunity
cost
of
delay

Actual
Value
IdenQﬁed

§  TargeQng
a
10%
producQvity
improvement
in
Year
1
with
the
ulQmate
goal
of

reducing
those
costs
by
40%
over
3
years

−  EsQmate
10
FTEs
allocated
to
reconciling
data

−  EsQmated
5-‐7
addiQonal
FTEs
dedicated
to
manual
data
collecQon/update

processes

−  The
acceleraQng

adopQon
of
regulaQon
will
conQnue
to
increase
the
demands
for

more
transparent
data
and
ﬂexible
reporQng

§  Project
delays
due
to
bad
data
resulted
in
cost
and
Qme
increase
1.5x
iniQal

esQmate:

−  2
year
delay

−  $3.1m
of
$5
million
spent

−  50%-‐60%
of
the
funcQonality
delivered


Decreased
Costs

Impacted
Costs

Technology

IT

Resources

MarkeQng
Sales
…

pg 120

Actual
Value
IdenQﬁed

§  Reduce
call
center
agent
Qme
searching
for
client
informaQon
from
5
min
to

2
min

−  Improved
producQvity
could
enable
1-‐2
people
to
be
reassigned
to
other
high

value
acQviQes

−  Improved
Customer
SaQsfacQon
with
improved
call
handling
Qme

§  Improved
markeQng
eﬀecQveness

−  ProducQvity
improvement
of
5–10%
would
enable
markeQng
team
to
increase

number
of
executed
campaigns

§  Reduce
sales
rep
commission
reconciliaQon
from
3
days/month
to
3
hours/
month


Customer
Experience

Direct
Mail

Cost
Savings
=
Cost
per
mail
piece
*
Number
of
returned
mail

pieces

Cost
Savings
=
Time
to
manually
remediate
mailing
list
*
Cost

for
Employee

Email

Cost
Savings
=
Cost
per
outbound
email
*
Number
of
bounced

emails

Cost
Savings
=
Time
to
manually
remediate
mailing
list
*
Cost

for
Employee

Reduced
Risk

Risk
Categories

• Liquidity
Risk

• Credit
Risk

• Market
Risk

• Cash
Flow

• Regulatory

PenalQes

• Cost
to
adhere

• Opportunity
Cost

• CompeQQon

• M&A

• Supplier
exposure

• Employee
turnover

• Loss
of
Info

OperaQonal
Strategic

Financial
Regulatory

Risk
Measurement

§  Threat
metrics

−  Cost
per
downQme
event

−  Loss
of
customer
conﬁdence

§  Financial
Risk

−  Liquidity

−  OperaQonal
costs

−  Equity
/
market
value
reducQon

§  Data
Governance
Compliance

−  “Hits”
on
web-‐based
tools

−  Access
counts
on
repositories

§  Legal
Compliance

−  PotenQal
penalQes
per
subject
area

−  LiQgaQon
fees
over
Qme

Courtesy
of
John
Ladley,

IMCue
SoluQons

Actual
Value
IdenQfied

Value
Iden4fied
Metric
Risk

ConsulQng
fees
are
$9m
for
audit;
40%
of
audit
cost
spent
on

gathering
data

Cost
to
comply,
future
costs
to
respond
to
an

audit

Regulatory

Insurance
premiums
for
error
and
omissions
increased,
unable
to

argue
against
the
premium
change

Insurance
costs

Financial

Credit
Risk
RaQng
remediaQon
project
resulted
in
over
2,500
hours
of

effort,
largely
on
data
issues

Future
Project
costs
Financial,

Regulatory

More
accurate
counterparty
data
improved
risk
assessment
and

enabled
reducQon
in
capital
allocaQon

Counterparty
data
accuracy,
Change
in
Capital

allocaQon
amount

Financial,

Regulatory

Improved
Client
data
protecQon,
auditability,
and
integrity

Decrease
in
number
of
privacy
incidents
or

complaints

Regulatory

Global
expansion
(addiQonal
markets
and
24x7
processing
)
will

conQnue
to
exert
pressure
on
manual
processes,
further
increasing

the
likelihood
of
an
oversight
leading
to
reporQng
mistakes
or

noncompliance

ReporQng
accuracy,
reporQng
mistakes

Strategic,

OperaQonal

Employee
burn
out
was
consistently
cited
as
major
risk
of
maintaining

the
status
quo.

Frequency
of
employee
ciQng
“data

remediaQon”
as
an
issue

OperaQonal

Increased
Revenue

Categories
of
Value
to
Revenue

§  Increased
“walletshare”

§  Improved
"household"
penetraQon

§  Increased
upsell/cross-‐sell

§  Improved
markeQng
-‐
response
rate,
purchase
value
per
response,
campaign

eﬀecQveness
and
opQmized
segmentaQon

§  Increased
sales
eﬀecQveness
and
face
Qme

§  Reduced
churn

§  Improved
lifeQme
value
of
Customers


Focus
on
CreaQng
Business
Value

Issues
Goals
Impact

Common
3rd
Party
Customer
Data

AcquisiQon
&
IntegraQon
processes

Improved
reporQng
and
analyQcs

More
accurate
payment
reconciliaQon

Common
Customer
DefiniQon

Report
inaccuracy

Difficulty

accessing
Data

Difficulty

targeQng
/

profiling

Lack
of
Data

Poor
Data
Quality

&
Lack
of

Governance

Improved
Access
to
accurate
centralized

Customer
Data

360°
View
of
Customer

-‐

“single
source
of
truth”

Improved
Customer
Data
Maintenance

Processes

Enable
Sales
Force
AutomaQon
and
other

tools

Improved
customer
experience

Increased
business
effecQveness
and
agility

Improved
ability
to
measure
success

Improved
ProducQvity

Reduced
Risk

Support
ERP
roll
out
and
other
system

changes

Automated
customer
engagement

Faster
Qme
to
market

Facilitate
Customer-‐centricity

Increased
up-‐selling
and
cross-‐selling

Appendix
3
OrganizaQonal
Change
Management


Agenda

§ Quick
terminology
review

§ Obstacles
to
successful
data

governance
change

§ OrganizaQonal
Change
Management

(OCM)

§ 5
Key
factors
for
successful
DG
change

131© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Terminology

132

Data
Governance
DeﬁniQon

§  Data
Governance
is
the
organizing

framework
for
establishing
the

strategy,
objecQves
and
policy
for

eﬀecQvely
managing
corporate
data.

§  It
consists
of
the
processes,
policies,

organizaQon
and
technologies
required

to
manage
and
ensure
the
availability,

usability,
integrity,
consistency,

auditability
and
security
of
your
data.

CommunicaQon

and
Metrics

Data

Strategy

Data
Policies

and
Processes

Data

Standards

and

Modeling

A
Data

Governance

Program
consists
of

the
inter-‐workings

of
strategy,

standards,
policies

and
communicaQon

pg 133

Data
Governance
Framework

§  Vision & Mission
§  Objectives & Goals
§  Alignment with Corporate
Objectives
§  Alignment with Business
Strategy
§  Guiding Principles
§  Statistics and Analysis
§  Tracking of progress
§  Monitoring of issues
§  Continuous Improvement
§  Score-carding
§  Policies & Rules
§  Processes
§  Controls
§  Data Standards & Definitions
§  Metadata, Taxonomy,
Cataloging, and Classification
§  Operating Model
§  Arbiters & Escalation points
§  Data Governance Organization
Members
§  Roles and Responsibilities
§  Data Ownership & Accountability
§  Collaboration & Information
Life Cycle Tools
§  Data Mastering & Sharing
§  Data Architecture & Security
§  Data Quality & Stewardship
Workflow
§  Metadata Repository
§  Communication Plan
§  Mass Communication
§  Individual Updates
§  Mechanisms
§  Training Strategy
•  Business Impact & Readiness
•  IT Operations & Readiness
•  Training & Awareness
•  Stakeholder Management & Communication
•  Defining Ownership & Accountability
Change

Management

pg 134

OrganizaQonal
Change
Management

135
§  Successful
OCM
is

−  A
structured,
organized
means
to

address
the
people
side
of
change

−  Helping
people
through
change

quickly

and
successfully
so
that

business
value
is
achieved

§  ProacQvely
idenQfying
and
dealing

with
change
issues
will
allow
the

data
governance
team
to

−  Address
risk
areas

−  Increase
the
speed
of
change

adopQon


Data
Governance
Impact

136

Obstacles
to
Data
Governance
Change

§  CompeQng
prioriQes
and
lack
of
resources

§  Data
Ownership
and
other
territorial
issues

§  Lack
of
cross-‐business
unit
coordinaQon

§  Lack
of
data
governance
understanding

§  Resistance
to
change
or
transformaQon

§  Lack
of
execuQve
sponsorship
and
buy-‐in

§  Resistance
to
accountability

§  Lack
of
business
jusQﬁcaQon

§  Inexperience
with
cross-‐funcQonal
iniQaQves

§  Change
of
personnel


Obstacles


Why
is
Data
Governance
Important?

Internal
pressures:

§  Desire
to
understand
customer
at
any

Qme
from
any
channel

§  Data
Quality
issues
are
persistent

§  Balance
of
old
mainframe
systems
with

new
technologies

§  Movement
to
the
cloud
and
losing
control

of
data

§  Data
Volumes
are
increasing

§  Mobile
apps
enabling
data
to
be
created

and
accessed
anywhere

§  Project
oriented
approach
to
addressing

issues/opportuniQes

External
pressures:

§  Greater
amounts
of
new
regulaQons

§  Increasing
Customer
Demands
–
my

informaQon
anywhere
at
any
Qme

§  Technology
and
market
changes
outpacing

ability
to
respond

Ensures
the
right
people
are
involved
in

determining
standards,
usage
and

integraQon
of
data
across
projects,
subject

areas
and
lines
of
business.

pg 139

140
Success
Factors
for
Sustainable
Governance

§  Clear
understanding
of
the
business
drivers

−  MoQvaQon

−  Metrics

§  Clear
business
case
for
DG
AND
DG
change
management

§  ProacQve,
realisQc
change
management

§  EﬀecQve
organizaQon

−  Clear
deﬁniQon
of
ownership
and
accountability

−  Strong
leadership

−  Sponsorship
from
key
execuQves

§  Project
management

−  Development
and
implementaQon
of
InformaQon

Management
and
Data
Governance


141
Data
Governance
Means
Change

§  Successful
DG
means
a
change
to
the
informaQon
management

culture,
processes
and
policies

§  Changing
that
culture
means
that
you
are
asking
people
to
think
and

behave
diﬀerently
about
how
data
is
accessed
and
used

§  You
need
an
organized
and
systemaQc
way
to
manage
and
sustain

those
changes
–
or
there
is
marginal
likelihood
of
success


OCM
Basics

142

143
Two
Sides
to
Change
Management

WHO?
WHAT?
WHEN?

WHERE?
WHY?

ü  Something
old
stops,
and

something
new
starts

ü  RelaQvely
easy
to
plan
for

and
anQcipate

SITUATIONAL REORIENTATION
PEOPLE
GO
THROUGH
AS

THEY
COME
TO
TERMS
WITH
THEIR
NEW

SITUATION

ü  It’s
important
to
help
aﬀected
individuals
let
go

of
the
old
situaQon
and
get
comfortable
with

the
new
way

ü  Everyone
processes
at
a
diﬀerent
rate
and
are

rarely
aligned
with
the
milestones
of
the

implementaQon
plan

PSYCHOLOGICAL

144
Two
Sides
to
Change
Management

For
change
to
be

successful,
BOTH
sides

need
to
be
addressed


Why
Do
People
Resist
Change?

§  Loss
of
idenQty
and
their
familiar
world

−  Loss
Analysis

§  DisorienQng
experience
of
the
transiQon
between
the
old
and
the
new

§  Weak/no
sponsorship
by
execuQve
leaders
and
managers

−  Lack
of
alignment

−  No
involvement

§  Overloaded
with
current
responsibiliQes

§  No
answer
to
WIIFM

§  No
involvement
in
the
craping
the
soluQon

§  Each
individual’s
capacity
to
handle
change

§  Other
work
and
personal
issues

§  How
well
an
organizaQon
has
handled
changes
in
the
past

145
145

146
What
Might
Resistance
Look
Like?

§  Trying
to
outlast
the
changes:
bargaining
for
exempQon
from
new
policies
or
processes

§  ReducQon
in
producQvity
and
missed
deadlines

§  Going
back
to
the
old
way
of
doing
things

§  Lack
of
ahendance
in
project
status
meeQngs
and
events,
or
training

§  Higher
absenteeism

§  Open
expression
of
negaQve
emoQon

§  From
execuQves,
resistance
could
be:

−  No
visible
sponsorship
of
data
governance;
no
open
endorsements

−  Refusal
or
reluctance
to
provide
needed
resources
and/or
informaQon

−  Repeatedly
canceling
or
refusing
to
ahend
criQcal
meeQngs

146

147
GeTng
People
through
Change
Successfully
Requires….

§  Clear
deﬁniQon
of
what
is
changing

−  Make
sure
the
new
behaviors,
skills
and
aTtudes
are
clearly
deﬁned
and

communicated

−  Provide
examples,
training
and
allow
Qme
for
pracQce

§  AhenQon
to
feedback:

−  What
are
people
saying
and
how
are
you
addressing
it?

−  You
must
respond
to
feedback;
be
sure
and
ahach
the
acQons
you
take
to

the
feedback
you
received
so
that
associates
know
you
are
listening

§  Some
reward
or
recogniQon
structure
to
encourage
new
behaviors

§  Measurement
and
performance
management

147

CommunicaQon
Framework
for
DG
Leaders

148
§ Explain
why
we’re

doing
what
we’re

doing
-‐
the
purpose

behind
the

outcome

§ What
is/was
the

problem?

§ Who
said
so
and
on

what
evidence?

§ What
could
occur
if

no
one
acts
to

solve
it?

§ What
could
happen

if
that
occurs?

Purpose
Picture
Plan
Par4cipa4on

§  Paint
the
picture

of
how
the

future
will
look

and
feel
once
DG

is
implemented.

§  How
are
people

going
to
get
their

work
done
and

interact
with

each
other?

§  How
will
a
day

be
organized?

§  Lay
out
the
plan

for
achieving
the

future
state

-‐
the

steps
and
Qmeline

in
which
people

will
receive

informaQon,

training,
and
the

support
they
need

to
transiQon
to
the

future

§  Get
managers

oriented
to
tell

employees
how

and
when
their

worlds
are
going
to

change

§  Start
with
where

people
are
and

work
forward

§  Establish
each

person’s
part
in
both

the
future
state
and

the
plan
to
get
there

§  Show
associates

their
roles
and

relaQonships
to
each

other
in
the
future

§  Show
associates

what
part
they
play

in
achieving
the

future
and
the

transiQon
process
to

get
there

§  All
this
helps
them

let
go
of
the
past

and
focus
on
the

future

Courtesy
of
William
Bridges,
Managing
Transi.ons


149
Change
Management
Phases

Implemen4ng/

Sustaining

Change

Managing

Change

Planning

for

Change

ü  OrganizaQonal
alignment
implemented

§  Structure

§  Jobs/people

§  Policies/procedures

§  IncenQves

§  Performance
management

ü  Change
integraQon/adopQon
assessment

ü  CommunicaQon
plan
execuQon

ü  Training
development
and
delivery

ü  Feedback
and
analysis
of
results

ü  Leadership
alignment
checkpoint

ü  Measurement
approach
&
metrics

ü  OrganizaQonal
impact
analysis

ü  Resistance
management

ü  ImplementaQon
checklist
development

ü  InformaQon
gathering
and
analysis

ü  Stakeholder
Analysis

ü  Sponsorship
development

ü  Change
plan
development

ü  Leadership
alignment
checkpoint

ü  CommunicaQons
planning

ü  Training
needs
assessment
and
planning


Meet
with
Program/
Project
Manager,
and
lay

out
CM
Approach
for
the

Program/Project

Monitor & RefineExtend
Change
Management
Alignment
to
DG
Phases

****Communica4on
Launch

Informa4on
Gathering
and
Analysis

Stakeholder
Analysis/Loss
Analysis

Change
Readiness
Assessment

Leadership
Alignment

Sponsorship
Development:
Assessment

and
Road
Map

Detailed
Change
Planning

Communica4on
Plan

Operationalize
ImplementStrategize & PlanAssess & Align
Project
Initiation
Planning
for
Change

****Collect,
Analyze
and
Report
on
Feedback

Implementa4on
Checklist

More
Leadership
Alignment

Metrics
and
measurement

Org
Impact
Analysis:
structure,
jobs,
training,
policies

Managing
Change

****Lesson
Learned
Assessment

Organiza4onal
Alignment
Ac4on
Plan

Change
Integra4on
Checklist

Transi4oning
to
the
Business

Implemen4ng/
Sustaining
Change

Key
Success
Factors

151

Five
Key
Factors
for
DG
Success

§  ExecuQve
Sponsorship

§  Aligned
leadership

§  Clear
communicaQon
(early
and
open)

§  Stakeholder
Engagement

§  Measurement


ExecuQve
Sponsorship

§  Has
been
the
#1
success
factor
for
successful
change
in
the
Prosci
studies

since
the
late
1990s

§  Determine
the
sponsor’s
level
of
comfort
with
leading
change

−  Provide
coaching
as
needed

§  Develop
a
“roadmap”
of
events
and
communicaQons
to
keep
the
sponsor

engaged
in
the
data
governance
eﬀort


Sponsorship
“Roadmap”

§  The
roadmap
shows
the
key
acQviQes
and
responsibiliQes
of
the
project

sponsor
in
support
of
DG
changes.

§  One
of
the
key
roles
is
to
engage
other
leaders
in
support
of
the
changes

§  Why:
Clariﬁes
the
roles
of
the
project
sponsor
throughout
the
project
life

cycle

−  Helps
idenQfy
the
acQons
needed
to
build
successful
sponsorship
of
the
project

changes
across
all
leadership
levels

−  IdenQﬁes
the
acQons
needed
to
demonstrate
acQve
and
visible
parQcipaQon

throughout
the
enQre
project

§  Review
the
roadmap
open:
in
light
of
DG
progress,
what
needs
to
be
added

or
changed?


Sponsorship
Roadmap
Template


Leadership
Alignment

§  Why
do
it?

−  Alignment
a
criQcal
success
factor
validated
by

the
Prosci
best
pracQces
studies

−  EssenQal
to
take
the
Qme
to
assess
alignment
and

address
any
disconnects

−  When
leaders
are
not
aligned,
change
is
derailed

−  Provides
the
opportunity
for
sponsoring
senior

execuQve
to
reinforce
the
expectaQon
for
support

and
commitment

§  Repeat
at
reasonable
intervals
throughout
the

iniQaQve


Leadership
Alignment
Example


CommunicaQon

§  Start
your
communicaQon
process
early

§  Create
an
idenQty
for
your
iniQaQve
that
the

organizaQon
can
grab
onto

§  Build
repeatable
key
messages
around
it

−  Be
prepared
to
repeat
those
messages
open

§  Remember
to
customize
your
messages
by

the
stakeholder
groups
impacted;
diﬀerent

groups
may
require
diﬀerent
messages

§  Test
to
make
sure
that
your
messages
are

geTng
out
there
and
that
awareness
and

understanding
are
building


CommunicaQon
Plan
Example


Stakeholder
Analysis

§  Early
on,
idenQfy
who
will
be
impacted
by
the
DG
program

§  Determine
what
is
needed
from
each
of
them,
how
they
are
likely
to
react
to

data
governance
changes,
and
what
sorts
of

issues/
concerns
they
are
likely

to
have


Stakeholder
Analysis
Guide


Engagement
Strategy:

•  Focused
effort
must

be
given
to
high

priority
groups

•  Provide
sufficient

level
of
informaQon

to
less
influenQal

groups
to
ensure

buy-‐in

Stakeholder
Engagement
Strategy

Meet

Their
Needs

Key

Player

Least

Important

Show

Considera4on

Stakeholder

Influence

Stakeholder

Influence
Stakeholder
Interest

Engage
your
Stakeholders

Data
Governance
Steering
Commihee

Data
Governance
Oﬃce

Data
Governance
Working
Group

Business
Stakeholders
IT
Enablement

Data Governance Organization
Key
Players

Key
Players,

Interested

Stakeholders

Key
Players

Inﬂuence,
Accountability,
Enforcement

Measuring
Progress/AdopQon

§  Governance
is
not
sustainable

without
monitoring

−  The
“nuisance”
of
change
will

overwhelm
perceived
subjecQve

beneﬁts

164
– There will be no defense if there are complaints of
longer cycle times or interference

What
Do
You
Measure?

§  AdopQon

−  How
well
is
the
data
governance
rollout
going?

−  How
are
people
adapQng
to
the
necessary
changes
that
DG
has
brought?

§  Amount
of
improvement
/
delta
from
a
“state”

−  Measure
“enabling”
aspect
of
DG
–
how
well
does
DG
inﬂuence
soluQons
with

measurable
results?

§  Improved
processes,
projects

§  Improved
idenQﬁcaQon
and
reacQon
to
risk

−  Measure
“innovaQon”
aspect
of
DG
–
how
well
does
DG
fundamentally
change
how

business
is
conducted?

§  Trusted
AnalyQcs


Pre
and
Post
Change
Feedback

§  Series
of
quesQonnaires
or
surveys

−  Collect
data
on
how
well
prepared
your
target
audiences
are
for
data
governance

implementaQon

§  Why
?
EssenQal
to
know
how
well
changes
are
progressing.

−  Without
this
insight,
problems
(and
resentment)
can
fester
and
issues
are

compounded

§  Can
be
conducted
as
interviews,
surveys
or
focus
groups

§  When

−  The
pre-‐change
survey
should
be
administered
within
3-‐4
weeks
of
a
change

−  The
post-‐change
survey
should
be
administered
at
regular
intervals
(30-‐60-‐90

days)


Pre-‐change
Survey

167

Post-‐change
Survey


Final
Thoughts

§ Be
absolutely
clear
and
speciﬁc
about
what
is

changing
and
what
that
will
mean
in
terms
of
required

behavior
changes:
people
can’t
change
behavior
if

they
don’t
know
what
they’re
supposed
do

diﬀerently.

§ Appreciate
that
there
is
a
psychology
to
change:

understanding
how
people
react
is
essenQal
to

structuring
your
iniQaQve
to
deal
with
it.


Sustaining Data Governance and Adding Value for the Long Term

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