DataEd Slides: Approaching Data Governance Strategically

Approaching Data
Governance Strategically
© Copyright 2020 by Peter Aiken Slide # 1paiken@plusanythingawesome.com+1.804.382.5957 Peter Aiken, PhD
“If you don't know where you are going, any road will get you there.” - Lewis Carroll
https://plusanythingawesome.com
• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
• 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)
• CDO Society (iscdo.org)
• 11 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.
Peter Aiken, Ph.D.
© Copyright 2020 by Peter Aiken Slide # 2https://plusanythingawesome.com

Program
• 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 better data decisions
• 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/Approaches
– Avoid worst practices
• Data Governance in Action (Storytelling)
• Take Aways/References/Q&A
Approaching
Data
Governance
Strategically
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?"
© Copyright 2020 by Peter Aiken Slide # 4

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]
As a topic, Data has confounding characteristics
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
© Copyright 2020 by Peter Aiken Slide #
Wally Easton Playing Piano
https://www.youtube.com/watch?v=NNbPxSvII-Q
6

https://plusanythingawesome.com © Copyright 2020 by Peter Aiken Slide # 7
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
Incomplete
https://plusanythingawesome.comhttps://plusanythingawesome.com
The question is which data to eliminate?
• 60-73% of enterprise data is never analyzed
• 54% of data is unidentified (plus)
• 32% ROT =
• 14% business critical
Reduce-Recycle-Reuse … Data?
• 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

Bad Data Decisions Spiral
• =
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

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
Approaching
Data
Governance
Strategically

How old is your 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
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.

https://plusanythingawesome.com © Copyright 2020 by Peter Aiken Slide # 17https://plusanythingawesome.com
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 areas of focus:
• 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 areas of focus:
• Strategic Alignment
• Value Delivery
• Resource Management
• Risk Management
• Performance Measures

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

Data
Management
Functions
DataManagementBodyofKnowledge
Version 1
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
1X
1X
1X
Metadata
Data
Quality

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

(Things that further)
Organizational Strategy
Lighthouse Project Provides Focus
(OpportunitiestoPractice)
NeededDataSkills
(Opportunitiestoimprove)
Datausebythebusiness
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
• Data Management
– The business function of planning
for, controlling and delivering data/
information assets
– Example: Delivering data
to solve business challenges

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
30
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 Strategy in Context
IT Strategy
Data Strategy
x
IT Strategy
This is correct …
Data Strategy

Data-Centric Development
Original articulation from Doug Bagley @ Walmart
33
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 and Data Governance in Context
Organizational
Strategy
Data Strategy
IT Projects
Organizational Operations
Data
Governance
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)
Data Governance & Data Stewards
Data Strategy Data Governance
What the data assets do
to support strategy
How well the data
strategy is working
(Business Goals)
(Metadata)
Data Stewards
What is the
most effective use
of steward
investments?
(Metadata)
Progress,
plans, problems

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
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
X
$
X
$
X
$
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 IT 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 IT must be
separated and
sequenced
Mismatched railroad tracks non aligned
Data programmes preceding IT development

Program
Approaching
Data
Governance
Strategically
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 data engineering and architecture!
Quality data engineering/
architecture work products
do not happen accidentally!

Our barn had to pass a foundation inspection
• Before further construction could proceed
• 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
• A system of ideas for guiding analyses
• A means of organizing project data
• Data integration priorities decision making framework
• A means of assessing progress
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
Encountergoverneddatamoredirectly←|→Encountergoverneddatalessdirectly
Moretimeisdedicated←|→Lesstimeisdedicated
IT/Systems Development
Leadership
(data decision makers)
Stewards
(data trustees)
Guidance
Decisions
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
IT/SystemsDevelopment
Data/feedback
Changes
Action
R
esources
Ideas
Data/Feedback
Components comprising the data community

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
R
esources
Ideas
Data/Feedback
Components comprising the data community
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
Steward
• one who actively directs
, Data
Getting Started with Data Governance
Assess context
Define DG roadmap
Secure executive mandate
Assign Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once) (Repeats)

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
59
Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
60

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
61
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
• 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
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

Getting Started with Data Governance (2nd look)
Assess context
Define DG roadmap
Secure executive mandate
Assign Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once) (Repeats)
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”
https://youtu.be/9E3hm6q3OuA

Program
Approaching
Data
Governance
Strategically
• Getting access to data around here is like that Catherine Zeta
Jones scene where she is having to get thru all those lasers …
Use Their Language ...

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

Army Data Governance
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
8,535 Total items
16 Attributes/item
136,560 Total attributes
System 5
15,959 Total items
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

Program
Approaching
Data
Governance
Strategically
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
• Learn the value of stories

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!

Upcoming Events
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14 July 2020 @ UTC -5 (2:00 PM ET)
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Business Outcomes
8 August 2020 @ UTC -5 (2:00 PM ET)
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