DataEd Slides: Data Governance Strategies

Peter Aiken, Ph.D.
Data Governance Strategies
Copyright 2019 by Data Blueprint Slide # !1
• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001  
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
Peter Aiken, Ph.D.
!2Copyright 2019 by Data Blueprint Slide #
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Founder, Data Blueprint (datablueprint.com)
• DAMA International (dama.org)
• 10 books and dozens of articles
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.

Infogix Confidential Copyright 2019
About Infogix
• Innovating data solutions since 1982
• Data Governance; Data Quality and Data Analytics
• Large and mid-size customers world-wide:
• Organizations rely on Infogix so they can trust their
data
• Average customer tenure > 18 years
“ I n d u s t r i e s t h a t t h r i v e o n d a t a ”

• For many a “strategy” happens the second time around
• Little grounding in the reality of business and related business
proposition
• A strategy perspective promotes new operational model around
data
• A long trail of woes…
Governance Strategy – Some Observations

When you are starting, what does “good” look like?
Industry Frameworks
Data Frameworks
Relatively few frameworks anchored in
data best practices
Governance Framework

• Data's Confounding Characteristics
– General low understanding leads to uneven application
– Data is uniquely-valuable
– Organizational data is largely comprised of ROT
– The case for data governance
• Strategy #1: Keep DG practically focused
– Discipline is immature
– "By the book" is not a good starting place
– A more targeted approach to DG
• Strategy #2: DG = HR at the programmatic level
– DG is central to DM
– Must be de-coupled from IT strategy
– Directly supportive of organizational strategy
• Strategy #3: Gradually add ingredients
– Frameworks/Stewards
– Checklists/Scorecards
– Avoid worst practices
• Data Governance in Action (Storytelling)
• Take Aways/References/Q&A
Confusion
• 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?"

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
As a topic, Data has confounding characteristics

Bad Data Decisions Spiral
Bad data decisions
Technical deci-
sion makers are not
data knowledgable
Business decision
makers are not
data knowledgable
Poor organizational outcomes
Poor treatment of
organizational data
assets
Poor 
quality 
data

Separating the Wheat from the Chaff
Separating the Wheat from the Chaff
• Better organized data increases in value
• Poor data management practices are costing
organizations much money/time/effort
• Minimally 80% of organizational data is ROT
– Redundant
– Obsolete
– Trivial
• The question is
– Which data to eliminate?
Incomplete

• Reduce the amount of
organizational data ROT
– Redundant, obsolete, trivial
• Recycle
– Methods, best practices,
successful approaches
• Reuse the remainder
– Fewer vocabulary items to
resolve
– Greater quality engineering
leverage
Reduce-Recycle-Reuse … Data?
Data Assets Win!
Data  
Assets
Financial  
Assets
Real 
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be  
used up
Can be  
used up
Non-
degrading √ √ Can degrade 
over time
Can degrade 
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
Data Assets Win!
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depletable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• As such, data deserves:
– It's own strategy
– Attention on par with similar organizational assets
– Professional ministration to make up for past neglect
Asset: A resource controlled by the organization as a result of past events or
transactions and from which future economic benefits are expected to flow [Wikipedia]

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

What is the world's oldest profession?
Augusta Ada King 
Countess of Lovelace 
(1815-52)
• 8,000+ years
• formalize practices
• GAAP
It is appropriate that we (data professionals)
acknowledge that we are currently not as mature a
discipline as we would like to be but it is not okay for
our discipline to remain in its current state of maturity
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

Managing
Data with
Guidance
What is Data Governance?
Ask anyone ...
• Would you want
your sole, non-
depletable, non-
degrading,
durable asset
managed without
guidance?

Managing
Data with
Guidance
Managing
Data Decisions
with
Guidance

The DAMA Guide to the Data Management Body of Knowledge
• Published by
DAMA
International
– The professional
association for
Data Managers (40
chapters
worldwide)
• DM BoK organized
around
– Primary data
management
functions focused
around data
delivery to the
organization
– Organized around
several
environmental
elements
Data  
Management
Functions
By the Book
Data
Strategy
Data
Governance 
Strategy
Metadata 
Strategy
Data  
Quality 
Strategy
BI/
Warehouse  
Strategy
Data 
Architecture 
Strategy
Master/ 
Reference  
Data 
Strategy
Document/ 
Content 
Strategy
Database 
Strategy
Data 
Acquisition 
Strategy
X

Version 1
Data
Strategy
Data
Governance 
Strategy
Data  
Quality 
Strategy
Master/ 
Reference  
Data 
Strategy
Perfecting
operations in
3 data
management
practice areas
Version 2
Data
Strategy
Data
Governance 
Strategy
BI
Warehouse
Strategy
Data 
Operations 
Strategy
Perfecting
operations in
3 data
management
practice areas

Data Governance from the DMBOK
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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.

Definition of IT Governance
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.
• An IT governance 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, five areas of focus:
• Strategic Alignment
• Value Delivery
• Resource Management
• Risk Management
• Performance Measures
Organizational Data Governance Purpose Statement
• What does data governance
mean to my organization?
– Managing data with guidance
– Getting some individuals
(whose opinions matter)
– To form a body (needs a
formal purpose/authority)
– Who will advocate/evangelize
for (not dictate, enforce, rule)
– Increasing scope and rigor of
– Data-centric development
practices

Organizational 
Strategy
Data Strategy
IT Projects
Organizational Operations
Data
Governance
Data Strategy and Data Governance in Context
Data
asset support for  
organizational
strategy
What the
data assets do to
support strategy
How well the data
strategy is working
Operational
feedback
How data is
delivered by IT
How IT
supports strategy
Other
aspects of
organizational
strategy
Data Strategy
Data
Governance
Data Strategy & Data Governance
What the data
assets do to support
strategy 
How well the data
strategy is working 
(Business Goals)
(Metadata)

What is the Difference Between DG and DM?
• Data Governance
– Policy level guidance
– Setting general guidelines and
direction
– Example: All information not
marked public should be
considered confidential
• Data Management
– The business function of
planning for, controlling and
delivering data/information
assets
– Example: Delivering data  
to solve business challenges
DIP Implementation
DataLeadership
Feedback
Feedback
Data
Governance
Data
Improvement
DataStewards
DataCommunityParticipants
DataGenerators/DataUsers
Data 
Things  
Happen
Organizational 
Things  
Happen
DIPs
Data
Improves
Over 
Time
Data
Improves As
A Result of
Focus

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
Your data program
must last at least as
long as your HR
program!

IT Project or Application-Centric Development
Original articulation from Doug Bagley @ Walmart
Data/
Information
IT 
Projects
 
Strategy
• 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
Data-Centric Development
Original articulation from Doug Bagley @ Walmart
IT 
Projects
Data/ 
Information
 
Strategy
• 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

Data Strategy in Context
Organizational 
Strategy
IT Strategy
Data Strategy
Organizational 
Strategy
IT Strategy
Data Strategy
This is wrong!
Organizational 
Strategy
IT Strategy
Data Strategy

Organizational 
Strategy
IT Strategy
This is correct …
Data Strategy
Data is not a Project
• Durable asset
– An asset that has a usable  
life more than one year
• Reasonable project  
deliverables
– 90 day increments
– Data evolution is measured in years
• Data
– Evolves - it is not created
– Significantly more stable
• Readymade data architectural components
– Prerequisite to agile development
• Only alternative is to create additional data siloes!

Data programmes preceding software development
Common Organizational Data  
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Build
Evolve
Future State
(Version +1)
Data evolution is separate from,
external to, and precedes system
development life cycle activities!
Data management
and software
development must
be separated and
sequenced
Mismatched railroad tracks non aligned
Copyright 2019 by Data Blueprint Slide #
!42
Data programmes preceding software development

• Before further construction could proceed
• No IT equivalent
Our barn had to pass a foundation inspection
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
Data Governance Institute
• A system of ideas for guiding analyses
• A means of organizing project data
• Data integration priorities decision making framework
• A means of assessing progress
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
Making a Better  
Data Governance Sandwich

Standard data
Data supply
Data literacy
Making a Better Data Governance Sandwich
Data literacy
Standard data
Data supply
Making a Better Data Governance Sandwich
Standard data
Data supply
Data literacy

Making a Better Data Sandwich
Standard data
Data supply
Data literacy
This cannot happen without engineering and architecture!
Quality engineering/ 
architecture work products  
do not happen accidentally!
Making a Better Data Sandwich
Standard data
Data supply
Data literacy
This cannot happen without data engineering and architecture!
Quality data engineering/ 
architecture work products  
do not happen accidentally!

Getting Started
Assess context
Define DG roadmap
Secure executive mandate
Assign Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once) (Repeats)
Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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
– DM Executive
– CIO
Practices and Techniques
• Data Value
• Data Management Cost
• Achievement of Objectives
• # of Decisions Made
• Steward Representation/Coverage
• Data Professional Headcount
• Data Management Process Maturity

4 four up
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.
132Copyright 2017 by Data Blueprint Slide #
Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
Roles and Responsibilities
• Suppliers:
– Business Executives
– IT Executives
– Data Stewards
– Regulatory Bodies
• Consumers:
– Data Producers
– Knowledge Workers
– Managers and Executives
– Customers
• Participants:
– Executive Data
Stewards
– Coordinating Data
Stewards
– Business Data
Stewards
– DM Executive
– CIO
Practices and Techniques
• Data Value
• Data Management Cost
• Achievement of Objectives
Encountergoverneddatamoredirectly←|→Encountergoverneddatalessdirectly
Moretimeisdedicated←|→Lesstimeisdedicated
Copyright 2019 by Data Blueprint Slide #
Components comprising the data community
IT/Systems Development
Leadership
Stewards
Domain expertise is less ← | → Domain expertise is greater
Roles more formally defined ← |→ Roles less formally defined
Guidance
Decisions
 
Data Community
Participants (DCPs)/
Subject Matter
Experts (SMEs)
Generators/ 
Users
IT/SystemsDevelopment
Ideas
Data/feedback
Action
Changes
Data/Feedback
R
esources

Data Steward
• Business data steward
– Manage from the perspective of business elements (i.e. business definitions  
and data quality)
• Technical data steward
– Focus on the use of data by systems and models (i.e. code operation)
• Project data steward
– Gather definitions, quality rules and issues for referral to business/technical stewards
• Domain data steward
– Manage data/metadata required across multiple business areas (i.e. customer data)
• Operational data steward
– Directly input data or instruct those who do; aid business  
stewards identifying root cause and addressing issues
• Metadata Data Steward
– Manage metadata as an asset
• Legacy Data Steward
– Manage legacy data as an asset
• Data steward auditor
– Ensures compliance with data guidance
• Data steward manager
– Planning, organizing, leading and controlling
(list adapted from Plotkin, 2014)
one who actively directs the use of  
organizational data assets in support  
of specific mission objectives
• one who actively directs
Steward, Data

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.
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

Scorecard: Data Governance Practices/Techniques
• Data Value
• Data Management  
Cost
• Achievement of  
Objectives
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

In what order do I incorporate in these into my DG Program?
1. Risk Management
2. Security and Privacy of Data
3. Content Valuation
4. Quality of Data
5. Life Cycle Management
6. Standards (Data Design, Models and Tools)
7. Governance Tool Kits and Case Studies
10 DG Worst Practices
1. Buy-in but not Committing:
Business vs. IT
2. Ready, Fire, Aim
3. Trying to Solve World Hunger or
Boil the Ocean
4. The Goldilocks Syndrome
5. Committee Overload
6. Failure to Implement
7. Not Dealing with Change
Management
8. Assuming that Technology Alone
is the Answer
9. Not Building Sustainable and
Ongoing Processes
10. Ignoring “Data Shadow Systems”

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 …

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

Formalizing the
Role of U.S.
Army Data
Governance
Suicide Mitigation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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20
21
22
23
24
25
26
27
28
29
30

Senior Army Official
• Room full of Colonels
• A very heavy dose of management support
• Advised the group of his opinion on the matter
• Any questions as to future direction
– "They should make an appointment to speak
directly with me!"
• Empower the team
– The conversation turned from "can this be done?" to "how
are we going to accomplish this?"
– Mistakes along the way would be tolerated
– Implement a workable solution in prototype form
Communication Patterns
•
Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide
and Saving Lives - The Final Report of the Department of Defense Task Force on the
Prevention of Suicide by Members of the Armed Forces - August 2010

Vocabulary is Important-Tank, Tanks, Tankers, Tanked
How one inventory item proliferates data throughout the chain
555 Subassemblies & subcomponents
17,659 Repair parts or Consumables
System 1: 
18,214 Total items 
75 Attributes/ item 
1,366,050 Total attributes
System 2 
47 Total items 
15+ Attributes/item 
720 Total attributes
System 3
16,594 Total items
73 Attributes/item
1,211,362 Total attributes
System 4 
16 Attributes/item 
136,560 Total attributes
System 5 
22 Attributes/item 
351,098 Total attributes
Total for the five systems show above: 
59,350 Items 
179 Unique attributes 
3,065,790 values

Business Implications
• National Stock Number (NSN)  
Discrepancies
– If NSNs in LUAF, GABF, and RTLS are  
not present in the MHIF, these records  
cannot be updated in SASSY
– Additional overhead is created to correct  
data before performing the real  
maintenance of records
• Serial Number Duplication
– If multiple items are assigned the same  
serial number in RTLS, the traceability of  
those items is severely impacted
– Approximately $531 million of SAC 3  
items have duplicated serial numbers
• On-Hand Quantity Discrepancies
– If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can
be no clear answer as to how many items a unit actually has on-hand
– Approximately $5 billion of equipment does not tie out between the LUAF and
RTLS
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
• 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 …

Mizuho Securities
 
 
CLUMSY typing cost a Japanese bank at
least £128 million and staff their
Christmas bonuses yesterday, after a
trader mistakenly sold 600,000 more
shares than he should have. The trader at
Mizuho Securities, who has not been
named, fell foul of what is known in
financial circles as “fat finger syndrome”
where a dealer types incorrect details into
his computer. He wanted to sell one
share in a new telecoms company called
J Com, for 600,000 yen (about £3,000).
Possibly the Worst Data Governance Example
Mizuho Securities
• Wanted to sell 1 share
for 600,000 yen
• Sold 600,000 shares
for 1 yen
• $347 million loss
• In-house system did
not have limit checking
• Tokyo stock exchange
system did not have
limit checking ...
• … and doesn't allow
order cancellations

Take Aways
• Need for DG is increasing
– Increase in data volume
– Lack of practice improvement
• DG is a new discipline
– Must conform to constraints
– No one best way
• DG must be driven by a data
strategy complimenting
organizational strategy
• DG Strategy #1: Keep it
practically focused
• DG Strategy #2: Implement
DG (and data) as a program
not a project
• DG Strategy #3: Gradually
add ingredients
IT Business
Data
Perceived State of Data

Data
Desired To Be State of Data
IT Business
The Real State of Data
Data
IT Business

References
Websites
• Data Governance Book
Data Governance Book
Compliance Book
IT Governance Books

+ =
Questions?
It’s your turn!  
Use the chat feature or
Twitter (#dataed) to submit
your questions now!
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DataEd Slides: Data Governance Strategies

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DataEd Slides: Data Governance Strategies