Key Elements of a Successful Data Governance Program

Key Elements of a Successful Data
Governance Program
At its core, data governance (DG) is: managing data with
guidance. This immediately provokes the question: would
you tolerate any of your assets to be managed without
guidance? (In all likelihood, your organization has been
managing data without adequate guidance and this accounts
for its current, less-than-optimal state.) This program
provides a practical guide to implementing DG or recharging
your existing program. It provides an understanding of what
data governance functions are required and how they fit with
other data management disciplines. Understanding these
aspects is a necessary pre-requisite to eliminate the
ambiguity that often surrounds initial discussions and
implement effective data governance/stewardship programs
that manage data in support of organizational strategy.
Delegates will understand why data governance can be tricky
for organizations due to data’s confounding characteristics.
Focusing on four key DG elements
•#1 Keeping DG practically focused on strategy
•#2 DG must exist at the same level as HR
•#3 Gradually add ingredients (practicing and getting better)
•#4 Data governance in action: value-focused storytelling
© Copyright 2022 by Peter Aiken Slide # 1
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Date: 10 May 2022 | Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
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1 | Alation Conﬁdential & Proprietary, Internal Use Only
Data Intelligence + Human Brilliance
Key Elements of a Successful Data
Governance Program
Opening Remarks
John Wills, Field CTO, Alation

2 | Proprietary & Confidential
We focus on ‘Catalog Led Governance’
● Unlocking the value of data for a broad array of roles across the enterprise
● Creating a culture of empowerment, democratization, and self-perpetuating value

We focus on guided stewardship and curation at scale
● The old, failed approach to governance was to put stewards in a no win position of
being responsible for the population, curation, and maintenance of all data assets
● Our approach pivots the role of stewards to enabling the crowd and guiding them on
the application of governance standards
Guided Community Driven
Social Collaboration
Centralized
Command & Control

We focus on driving community adoption and participation
● Traditional governance programs struggle with being relevant as rank and ﬁle
information workers go about their daily work
● Alation provides an immersive environment where information worker can ﬁnd,
understand, trust, collaborate, publish, and reuse data assets all while being guided
by governance policies and standards that are co-located with information about
these assets.
Example 1 - User Self-Service Example 2 - Community
Collaboration & Sharing
Example 3 - Stewards as Guides

Active Data Governance: Prescriptive Approach
Monitor & Measure
• Determine policy conformance
• Create curation analysis
• Measure usage & asset creation
• Establish data quality
Establish Governance Framework
• Set mission & vision
• Create policies, standards & glossaries
Populate Data Catalog
• Ingest metadata (technical, business,
lineage)
• Analyze metadata (top users, popular
data)
Empower Data Stewards
• Recognize and assign stewards
• Automate stewardship processes
• Identify reviewers & workflow approvers
Curate Assets
• Describe data, apply quality flags
• Surface descriptions, quality, etc. to
users at point of data use
Apply Policies & Controls
• Implement policies & controls
• Enforce policies & controls
Drive Community Collaboration
• Promote trusted data use
• Leverage community knowledge
• Determine whether data is fit for
purpose / intended use
Supported by Alation Active Data Governance
Service Offering

Two Most Commonly
Asked Questions
1) Will I get copies of the
slides after the event?
2) Is this being recorded?
Peter Aiken, Ph.D.
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Institute for Defense Analyses (ida.org)
• DAMA International (dama.org)
• MIT CDO Society (iscdo.org)
• Anything Awesome (anythingawesome.com)
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– HUD …
• 12 books and
dozens of articles
+
• DAMA International President 2009-2013/2018/2020
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005

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anythingawesome
Successful
Data
Governance
Program© Copyright 2022 by Peter Aiken Slide # 6
peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD
Key Elements of a

• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
© Copyright 2022 by Peter Aiken Slide #
Program
7
Key Elements of a
Successful Data
Governance
Program
Program
Key Elements of a
Successful Data
Governance
Program

Data is not broadly or widely understood
adapted from: http://www.dailymirror.lk/print/opinion/editorial-we-need-to-become-channels-of-peace/172-27164
It is like a fan!
It is like a snake!
It is like a wall!
It is like a rope!
It is like a tree! Blind Persons and the Elephant
It is like a story!
It is like a dashboard!
It is like a pipes!
It is like a warehouse!
It is like statistics!
• IT thinks data is a business problem
– "If they can connect to the server, then my job is done!"
• The business thinks IT is managing data adequately
– "Who else would be taking care of it?"
Confusion as to data responsibility

The
Princess
on the
Pea 
 
by  
Hans Christian
Andersen
on
Doing a poor job with data governance
• Failure to understand the role of data
governance re: proposed and
existing software/services
– Locks in imperfections for the life of the application
– Restricts data investment benefits
– Decreases organizational data leverage
• Accounts for 20-40% of IT budgets
devoted to evolving
– Data migration (Changing the data location)
– Data conversion (Changing data form, state, or product)
– Data improving (Inspecting and manipulating, or re-keying
data to prepare it for subsequent use)
• Lack of data governance causes everything else to
– Take longer
– Cost more
– Deliver less
– Present greater risk (with thanks to Tom DeMarco)

Separating the Wheat from the Chaff
Organizing the Wheat Separated from the Chaff
Is well organized data worth more?

• Examples of information architecture achievements that happened
well before the information age:
– Page numbering
– Alphabetical order
– Table of contents
– Indexes
– Lexicons
– Maps
– Diagrams
Pre-Information Age Metadata
Example from: How to make sense of any mess
by Abby Covert (2014) ISBN: 1500615994
"While we can arrange things
with the intent to communicate
certain information, we can't
actually make information. Our
users do that for us."
https://www.youtube.com/watch?v=60oD1TDzAXQ&feature=emb_logo
https://www.youtube.com/watch?v=r10Sod44rME&t=1s
https://www.youtube.com/watch?v=XD2OkDPAl6s
Remove the structure and things fall apart rapidly
• Better organized data increases in value

Organizing the Wheat Separated from the Chaff
• Better organized data increases in value
• Poor data management practices are costing
organizations money/time/effort
• 80% of organizational data is ROT
– Redundant
– Obsolete
– Trivial
• The question is which data to eliminate?
– Most enterprise data is never analyzed
Who is qualified to accomplish this?

You must address data debt proactively
https://www.merkleinc.com/blog/are-you-buried-alive-data-debt
https://johnladley.com/a-bit-more-on-data-debt/
https://uk.nttdataservices.com/en/blog/2020/february/how-to-get-rid-of-your-data-debt
Data debt:
• Slows progress
• Decreases quality
• Increases costs
• Presents greater risks
• Data debt
– The time and effort it will take to return your
shared data to a governed state from its
(likely) current state of ungoverned
• Getting back to zero
– Involves undoing existing stuff
– Likely new skills are required
2020 American Airlines market value ~ $6b
AAdvantage valued between $19.5-$31.5b
2020 United market value ~ $9b
MileagePlus ~ $22b
https://www.forbes.com/sites/advisor/2020/07/15/how-airlines-make-billions-from-monetizing-frequent-flyer-programs/?sh=66da87a614e9

Program
Key Elements of a
Successful Data
Governance
Program
Program
Key Elements of a
Successful Data
Governance
Program

How old is your profession?
Augusta Ada King
Countess of Lovelace
(1815-52)
• 8,000+ years
• formalize practices
• GAAP
Current approaches are not and have not been working
Driving Innovation with Data
Competing on data and analytics
Managing data as a business asset
Created a data-driven organization
Forged a data culture
25% 50% 75% 100%
24%
24%
39%
41%
49%
Yes No
Source: Big Data and AI Executive Survey 2021 by Randy Bean and Thomas Davenport www.newvantage.com
2021
0%
25%
50%
75%
100%
technology people/process
92.2%
7.8%
80% of data challenges are people/process based!

Corporate Governance
• "Corporate governance - which
can be defined narrowly as the
relationship of a company to its
shareholders or, more broadly,
as its relationship to society….",
Financial Times, 1997.
• "Corporate governance is about
promoting corporate fairness,
transparency and accountability"
James Wolfensohn, World Bank,
President Financial Times, June 1999.
• “Corporate governance deals
with the ways in which suppliers
of finance to corporations assure
themselves of getting a return
on their investment”,
The Journal of Finance, Shleifer and Vishny, 1997.
IT Governance
• "Putting structure around how organizations align IT strategy with business
strategy, ensuring that companies stay on track to achieve their strategies
and goals, and implementing good ways to measure IT’s performance.
• It makes sure that all stakeholders’ interests are taken into account and
that processes provide measurable results.
• Framework should answer some key
questions, such as how the IT
department is functioning overall,
what key metrics management needs
and what return IT is giving back to
the business from the investment
it’s making." CIO Magazine (May 2007)
• IT Governance Institute, 5 foci:
– Strategic Alignment
– Value Delivery
– Resource Management
– Risk Management
– Performance Measures
• "Putting structure around how organizations align IT strategy with business
strategy, ensuring that companies stay on track to achieve their strategies
and goals, and implementing good ways to measure IT’s performance.
• It makes sure that all stakeholders’ interests are taken into account and
that processes provide measurable results.
• Framework should answer some key
questions, such as how the IT
department is functioning overall,
what key metrics management needs
and what return IT is giving back to
the business from the investment
it’s making." CIO Magazine (May 2007)
• IT Governance Institute, 5 foci:
– Strategic Alignment
– Value Delivery
– Resource Management
– Risk Management
– Performance Measures

7 Data Governance Definitions
• The formal orchestration of people, process, and technology to enable
an organization to leverage data as an enterprise asset – The MDM Institute
• A convergence of data quality, data management, business process
management, and risk management surrounding the handling of data in an
organization – Wikipedia
• A system of decision rights and accountabilities for information-related
processes, executed according to agreed-upon models which describe who can
take what actions with what information, and when, under what circumstances,
using what methods – Data Governance Institute
• The execution and enforcement of authority over the management of data
assets and the performance of data functions – KiK Consulting
• A quality control discipline for assessing, managing, using, improving,
monitoring, maintaining, and protecting organizational
information – IBM Data Governance Council
• Data governance is the formulation of policy to optimize, secure,
and leverage information as an enterprise asset by aligning the
objectives of multiple functions – Sunil Soares
• The exercise of authority and control over the
management of data assets – DM BoK
What is Data Governance?
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
Managing
Data
with
Guidance

Data Governance is
Managing
Data Decisions
with
Guidance
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
Bad Data Decisions Spiral

Bad Data Decisions Spiral
Bad data decisions
Poor organizational outcomes
Technical decision
makers are not data
knowledgable
Business decision
makers are not data
knowledgable
Poor treatment
of organizational
data assets
Poor
quality
data
Keep the proper focus
• Wrong question:
– How should we govern all this data?
• Right question:
– Should we include this
data item within the
scope of our current
data govenance
practices?
• Regardless of the decision, document why!

What is Strategy?
• Current use derived from military
- a pattern in a stream of decisions
[Henry Mintzberg]
A thing
Every Day
Low Price
Former Walmart Business Strategy

Wayne
Gretzky’s
Strategy
He skates to where he
thinks the puck will be ...
35
Strategy Example 3
Good Guys
(Us)
Bad Guys
(Them)
36

Strategy Example 3
Good Guys
(Us)
Bad Guys
(Them)
37
Strategy Example 3
Good Guys
(Us)
Bad Guys
(Them)
38

Strategy Guides Workgroup Activities
A pattern
in a stream
of decisions
39
Executive data board
Oversee council
• Translate commonwealth goals to agency performance targets
• provide resources
• Remove organizational obstacles
• appoint data governance council members
• Oversee the data governance council
• Oversee data sharing & analytics projects
Complex Data Governance Environment
Complex
Data Governance Council
• Liaise between agency operations & CDO
• Advise CDO on technology, policy, and governance strategies
• Administer data governance policies set by the board
• implement data sharing & analytics projects
• Review open data assets
• Report progress & compliance to the Board
Advise CDO
40
Data sharing and analytics
• Identify goals and objectives
• Prioritize initiatives
• Study & report
• Recommend changes to budget and code
DATA COMMISSION
LEGISLATIVE
Region representatives
EXECUTIVE
JUDICIAL
Advise Governor
TRUSTEE
execute
• Define, approve, and communicate data strategies, policies,
standards, rules, guidelines, & best practices
• Provide a governance, policy, and technology framework
• Define agency data governance responsibilities
• Encourage & facilitate data sharing
• Facilitate coordination to prevent duplication
• Coordinate policy and technology proposals and recommendations
• Administer and manage the commonwealth data trust
• Track and enforce compliance and conformance
• Oversee dissemination of open data

Your Data Strategy
• Highest level data guidance
available ...
• Focusing data activities on
business-goal achievement ...
• Providing guidance when
faced with a stream of
decisions or uncertainties
• Data strategy most usefully
articulates how data can be
best used to support
organizational strategy
• This usually involves a
balance of remediation
and proactive measures
Data Strategy and Governance in Strategic Context
Data asset support for
Organizational
Strategy
Data Strategy
Data
Governance
What is the most
effective use of steward
investments?
What the data assets do to
better support strategy
How well the data strategy is working
Data
Stewards
Progress,
plans, problems
(Business Goals)
(Metadata)
(Metadata/
Business Goals)

≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
Data Governance Role: Produce systemic organizational changes that impact
data and work practices over time
Data
Leadership
Feedback
Data
Governance
Data
Improvement
Data
Stewards
Data
Community
Participants
Data
Generators/Data
Users
Data
Things
Happen
Organizational
Things
Happen
DIPs
Data
Improves
Over
Time
Data
Improves As
A Result of
Focus
Feedback
X
$
X
$
X
$
X
$
X
$
X
$
X
$
X
$
X $
Support for
Organizational
Strategy
Better
IT
Outcomes
Better Managed/
Higher Quality Data
Assets
Improved
Knowledge Worker
Productivity

Multiple Dependencies
Better Managed/
Higher Quality Data
Assets
Better
IT
Outcomes
Improved
Knowledge Worker
Productivity
Support for
Organizational
Strategy
Poor data manifests as multifaceted organizational challenges
IT
System
Business
Challenge
Business
Process
Business
Challenge
IT
Process
Business
Challenge
Business
System
Business
Challenge
IT
Process
Business
Challenge
IT
System
Business
Challenge
Business
Process
Business
Challenge
Poor results
Root cause
analysis is
part of data
governance

Consistency Encourages Quality Analysis
IT
System
Business
Challenge
Business
Process
Business
Challenge
IT
Process
Business
Challenge
Business
System
Business
Challenge
IT
Process
Business
Challenge
IT
System
Business
Challenge
Business
Process
Business
Challenge
Eliminating data debt
requires a team with
specialized skills
deployed to create a
repeatable process
and develop sustained
organizational
skillsets

The MacGyver approach to DG uses paperclips and duct tape

Why is Data Governance important?
• Cost organizations millions each year in
– Productivity
– Redundant and
siloed efforts
– Poorly thought out
hardware and software
purchases
– Delayed decision making
using inadequate information
– Reactive instead of
proactive initiatives
– 20-40% of IT spending
can be reduced through
better data governance
Program
Key Elements of a
Successful Data
Governance
Program

Program
Key Elements of a
Successful Data
Governance
Program
Mismatched railroad tracks non aligned
Data programmes driving IT programs

Data is not a Project
Common Organizational Data
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Create
Evolve
Future State
(Version +1)
Data programmes drive IT programs
Data evolution is separate from,
external to, and precedes system
development life cycle activities!
What is the Difference Between DG and DM?
Data Governance
• Policy level guidance
• Setting general guidelines/direction
• Example: All information not marked
public should be considered confidential
• Keep in mind "firehouse" metaphore
Data Management
• The business function of
– planning for,
– controlling and
– delivering
– data/information assets
• Example: Delivering data to solve business challenges

Data Management Data Governance Program
External Comprehension
Sample from: https://artist.com/kathy-linden/on-outside-looking-in/?artid=4385
Everything Else Data
Data (blah blah blah)
Data Program
Most do not appreciate the
difference between Data
Governance and the other data
stuff that needs to be done
Differences between Programs and Projects
• Programs are Ongoing, Projects End
– Managing a program involves long term strategic planning and
continuous process improvement is not required of a project
• Programs are Tied to the Financial Calendar
– Program managers are often responsible for delivering
results tied to the organization's financial calendar
• Program Management is Governance Intensive
– Programs are governed by a senior board that provides direction,
oversight, and control while projects tend to be less governance-intensive
• Programs Have Greater Scope of Financial Management
– Projects typically have a straight-forward budget and project financial
management is focused on spending to budget while program planning,
management and control is significantly more complex
• Program Change Management is an Executive Leadership
Capability
– Projects employ a formal change management process while at the program
level, change management requires executive leadership skills and program
change is driven more by an organization's strategy and is subject to market
conditions and changing business goals
Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management
58
Your data program
must last at least as
long as your HR
program!

Metadata
Management
59
Data
Management
Body
of
Knowledge
(DM
BoK
V2)
Practice
Areas
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
Iteration 1
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
1X
1X
1X
Metadata
Data
Quality

Iteration 2
Data
Strategy
Data
Governance
BI/
Warehouse Metadata
2X
2X
1X
Perfecting
operations in 3
data management
practice areas
Iteration 3
Data
Strategy
Data
Governance
BI/
Warehouse
Reference &
Master Data
1X
3X
3X
Perfecting
operations in 3
data management
practice areas

(Things that further)
Organizational Strategy
DG Lighthouse Metaphors Provides Focus
(
O
c
c
a
s
i
o
n
s
t
o
P
r
a
c
t
i
c
e
)
N
e
e
d
e
d
D
a
t
a
S
k
i
l
l
s
(
O
p
p
o
r
t
u
n
i
t
i
e
s
t
o
i
m
p
r
o
v
e
)
D
a
t
a
u
s
e
b
y
t
h
e
b
u
s
i
n
e
s
s
63
IT Project or Application-Centric Development
Original articulation from Doug Bagley @ Walmart 64
Data/
Information
IT
Projects
• In support of strategy, organizations
implement IT projects
• Data/information are typically considered
within the scope of IT projects
• Problems with this approach:
– Ensures data is formed to the applications and
not around the organizational-wide information
requirements
– Process are narrowly formed around
applications
– Very little data reuse is possible
Strategy

This is the wrong way to think about data strategy
IT Strategy
Data Strategy
x65
IT Strategy
This is correct …
Data Strategy
66

Data-Centric Development
Original articulation from Doug Bagley @ Walmart 67
Data/
Information
IT
Projects
• In support of strategy, the organization
develops specific, shared data-based
goals/objectives
• These organizational data goals/
objectives drive the development of
specific IT projects with an eye to
organization-wide usage
• Advantages of this approach:
- Data/information assets are developed from an
organization-wide perspective
- Systems support organizational data
needs and compliment organizational
process flows
- Maximum data/information reuse
Strategy
Program
Key Elements of a
Successful Data
Governance
Program

Program
Key Elements of a
Successful Data
Governance
Program
Digital Insight
• Subtract data from digital and what do you have?
• Subtract digital from data and you still have data
https://www.linkedin.com/in/mark-johnson-518a752/
DIGITAL
DATA
?
DIGITAL
DATA
DATA

It isn't possible to go digital
Digital
a
By just spelling 'data'
Dat

It requires more work
Data
a
73
Garbage
Digital
Results
74
Perfect
Model
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Data
Governance
Analytics
Technology
GI➜GO!
Garbage
Data
Garbage In ➜ Garbage Out!

Garbage
Digital
Results
75
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Machine
Learning
Block Chain
Business
Intelligence
Garbage
Digital
Results
76
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Machine
Learning
Block Chain
Business
Intelligence

Garbage
Digital
Results
77
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Machine
Learning
Block Chain
Business
Intelligence
Quality
Digital
Results
78
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
QI➜QO!
Machine
Learning
Block Chain
Business
Intelligence

Quality
Digital
Results
79
Quality
Data
Today
Machine
Learning
Data
Sandwich

Data supply
Data literacy
Standard data
Standard data
Leverage point - high performance automation
Data literacy
81
Data supply
Standard data
Data supply
Data literacy
82

This cannot happen without investments in
engineering and architecture!
83
Data supply
Data literacy
Standard data
Quality engineering/
architecture work products
do not happen accidentally!
Quality data engineering/
architecture work products
do not happen accidentally!
This cannot happen without investments in
data engineering and architecture!
84
Data supply
Data literacy
Standard data

Our barn had to pass a foundation inspection
• Before further construction could proceed
• It makes good business sense
• No IT equivalent
Data Governance Frameworks
• A system of ideas for
guiding analyses
• A means of organizing
project data
• Priorities for data
decision making
• A means of assessing
progress
– Don’t put up walls until
foundation inspection is
passed
– Put the roof on ASAP
• Make it all dependent
upon continued funding

Data Governance from the DMBOK
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Data Governance Institute
http://www.datagovernance.com/

KiK Consulting
http://www.kikconsulting.com/
IBM Data Governance Council
http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html

Elements of Effective Data Governance
See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.
Baseline Consulting (sas.com)

American College Personnel Association
Domain expertise is less ← | → Domain expertise is greater
Roles more formally defined ← | → Roles less formally defined
Encounter
governed
data
more
directly
←
|
→
Encounter
governed
data
less
directly
More
time
is
dedicated
←
|
→
Less
time
is
dedicated
IT/Systems Development
Leadership
(data decision makers)
Stewards
(data trustees)
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
IT/Systems
Development
Guidance
Decisions
Data/feedback
Changes
Action
R
e
s
o
u
r
c
e
s
Ideas
Data/Feedback
Components comprising the data community

R
e
s
o
u
r
c
e
s
Leadership
(data decision makers)
Stewards
(data trustees)
Guidance
Decisions
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
Data/feedback
Changes
Action
Ideas
Data/Feedback
Components comprising the data community
Getting Started with Data Governance
Assess context
DG roadmap
Policy/Standards Reqs.
Data Stewards
Operational processes
Executive mandate

Goals and Principles
• To define, approve, and communicate data
strategies, policies, standards, architecture,
procedures, and metrics.
• To track and enforce regulatory compliance
and conformance to data policies,
standards, architecture, and procedures.
• To sponsor, track, and oversee the delivery
of data management projects and services.
• To manage and resolve data related issues.
• To understand and promote the value of
data assets.
Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
97
Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
98

Roles and Responsibilities
• Suppliers:
– Business Executives
– IT Executives
– Data Stewards
– Regulatory Bodies
• Consumers:
– Data Producers
– Knowledge Workers
– Managers and Executives
– Data Professionals
– Customers
• Participants:
– Executive Data Stewards
– Coordinating Data Stewards
– Business Data Stewards
– Data Professionals
– DM Executive
– CIO
99
Scorecard: Data Governance Practices/Techniques
• Data Value
• Data Management
Cost
• Achievement of
Objectives
• # of Decisions Made
• Steward Representation/Coverage
• Data Professional Headcount
• Data Management Process Maturity
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Data Governance Checklist
✓ Decision-Making Authority
✓ Standard Policies and Procedures
✓ Data Inventories
✓ Data Content Management
✓ Data Records Management
✓ Data Quality
✓ Data Access
✓ Data Security and Risk Management
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
DG Components
• Practices and Techniques
– Data Value
– Data Management Cost
– Achievement of Objectives
– # of Decisions Made
– Steward Representation/Coverage
– Data Professional Headcount
– Data Management Process
Maturity
• What do I include in my Data
Governance Program?
– Security and Privacy of Data
– Quality of Data
– Life Cycle Management
– Risk Management
– Content Valuation
– Standards (Data Design, Models
and Tools)
– Governance Tool Kits and Case
Studies

Evolve
https://www.google.com/url?sa=i&url=https%3A%2F%2Fprezi.com%2Fel_1jxok7t-x%2Fevolution-is-not-goal-oriented%2F&psig=AOvVaw3rFrerRanKFatDfESqab4C&ust=1591700086354000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCODBiIyH8ukCFQAAAAAdAAAAABAD
Evolve

Getting Started with Data Governance
Assess context
DG roadmap
Policy/Standards Reqs.
Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once)
(Repeats)
Get better at this!
Operational processes
Executive mandate
https://en.wikipedia.org/wiki/PDCA
Program
Key Elements of a
Successful Data
Governance
Program

Program
Key Elements of a
Successful Data
Governance
Program
Non-Fungible Tokens
$48M. $280

Use Their Language ...
• Getting access to data around here is like that Catherine Zeta
Jones scene where she is having to get thru all those lasers …
Barclays Excel Spreadsheet Horror
• Barclays preparing to buy Lehman’s
Brothers assets.
• 179 dodgy Lehman’s contracts were
almost accidentally purchased by
Barclays because of an Excel
spreadsheet reformatting error
• A first-year associate reformatted an
Excel contracts spreadsheet
– Predictably, this work was done long after
normal business hours, just after 11:30 p.m...
• The Lehman/Barclays sale closed on
September 22nd, 2008
• the 179 contracts were marked as
“hidden” in Excel, and those entries
became “un-hidden” when when
globally reformatting the document …
• … and the sale closed …
https://www.businessinsider.com/2008/10/barclays-excel-error-results-in-lehman-chaos

Excel spreadsheet error blamed for UK’s 16,000 missing coronavirus cases
The UK failed to add
nearly 16,000 confirmed
cases of coronavirus to its
national track and trace
system due to an Excel
error. A number of reports,
including from The
Guardian, Sky News, and
The Daily Mail, say the
mistake was caused when
an Excel spreadsheet
used to track confirmed
cases of the virus reached
its maximum file size and
failed to update.
"Failure to upload these cases to the national database meant anyone who came into contact with these individuals was not informed. It’s
an error that may have helped spread the virus further through the country as individuals exposed to the virus continued to act as normal."
Fan Blade Sensor
• 1 Sensor
– Probabilistic (generalist) maintenance forecasts
• 100 Sensors
– Establish optimal monitoring targets
– Finer tuned and safer maintenance
– Mission Readiness ???
– Storage $$$
– Handling $$$
– Opportunity $$$
– Systemic $$$
– Maintenance $$$
– Total > $1.5 Billion

Program
113
Key Elements of a
Successful Data
Governance
Programs
Take Aways
• Need for DG is increasing
– Increase in data volume
– Lack of rigorous practice improvement
• DG is a new discipline
– Must conform to constraints
– No one best way
• DG must be driven by 4 key elements
1. Keep DG practically focused on strategy
2. Implement DG (and data) as a program not a project
3. Gradually add ingredients
4. Learn the value of stories/storytelling
• The goal is to improve DG effectiveness and efficiencies (and the
data itself) over time
• The more data literate the organization, the easier the
transformation
Image adapted from: https://www.everythingneon.com/proddetail.php?prod=n105-0826-take-away-neon-sign

By the book
• Data Governance: How to
Design, Deploy, and
Sustain an Effective Data
Governance Program
• John Ladley
• Amazon Best Sellers
Rank: #641,937 in Books
(See Top 100 in Books)
– #242 in Management Information
Systems
– #209 in Library Management
– #380 in Database Storage &
Design
References
Websites
• Data Governance Book
Data Governance Book
Compliance Book

IT Governance Books
Event Pricing
• 20% off
directly from the
publisher
on select titles
• My Book Store @
http://anythingawesome.com
• Enter the code
"anythingawesome" at
the Technics bookstore
checkout where it says to
"Apply Coupon"
anythingawesome

[ Clicking any webinar title will link directly to the registration page ]
Upcoming Events
Data Preparation Fundamentals
14 June 2022
Conceptual vs. Logical vs. Physical Data
12 July 2022
The Importance of Metadata
9 August 2022
Brought to you by:
Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
Peter.Aiken@AnythingAwesome.com +1.804.382.5957
Thank You!
Book a call with Peter to discuss anything - https://anythingawesome.com/OfficeHours.html
Critical Design Review?
Hiring Assistance?
Reverse Engineering Expertise?
Executive Data
Literacy Training?
Mentoring?
Tool/automation evaluation?
Use your data more strategically?

As a topic, data is ...
Complex &
detailed
• Outsiders do not
want to hear about
or discuss any
aspects of
challenges/solutions
• Most are unqualified
re: architecture/
engineering
Taught
inconsistently
• Focus is on
technology
• Business impact is
not addressed
Not well
understood
• (Re)learned by
every
workgroup
• Lack of standards/
poor literacy/
unknown
dependencies
Wally Easton Playing Piano
https://www.youtube.com/watch?v=NNbPxSvII-Q
122

What is the most
effective use of steward
investments?
Data Strategy and Governance in Strategic Context
(Business Goals)
(Metadata)
Data asset support for
What the data assets do to
better support strategy
How well the data strategy is working
Organizational
Strategy
Data
Governance
Data Strategy
Data
Stewards
(Metadata/
Business Goals)
Progress,
plans, problems

Toyota versus Detroit Engine Mounting (Circa 1994)
• Detroit
– 3 different
bolts
– 3 different
wrenches
– 3 different
bolt
inventories
• Toyota
– 1 bolt used
for all three
assemblies
– 1 bolt
inventory
– 1 type of
wrench
Toyota versus Detroit Engine Mounting (Circa 1994)
• Detroit
– many
different
bolts
– many
different
wrenches
– many
different bolt
inventories
• Toyota
– same bolts
used for all
three
assemblies
– same 1 bolt
inventory
– same 1 type
of wrench

Key Elements of a Successful Data Governance Program

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Key Elements of a Successful Data Governance Program