DataEd Slides: Growing Practical Data Governance Programs

Growing Practical Data
Governance Programs
© Copyright 2021 by Peter Aiken Slide # 1
paiken@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
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 (plusanythingawesome.com)
• 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 …
https://plusanythingawesome.com

Practical Data Governance
with Knowledge Graphs

TOPQUADRANT COMPANY
FOUNDATION
§ TopQuadrant was founded in 2001
§ Strong commitment to standards-based approaches to data / metadata
semantics
FOCUS
§ Making Enterprise information meaningful
§ Provide comprehensive metadata management and governance solutions
using knowledge graph technologies
Using knowledge graphs for data governance provides
a more expansive view of the most important things to an enterprise.
Irene Polikoff

© Copyright TopQuadrant Inc.
Summary of this “Enlightening Talk”
§ Practical Data Governance requires the right tools to:
– Enable connections between any kind of information
– Capture and preserve meaning
– Support the business value of data
§ Knowledge Graphs are a “smart” approach to meaningfully and flexibly
bridging together all aspects of data governance

Framework for a Data Governance Program

Governance Role Assignment
Governance Model in TopBraid EDG

Capturing Asset Information in TopBraid EDG
Fully Extendable by
Customer Organizations
• Automated Capture
• Enrichment through
Reasoning and Stewardship

Exploring Data Lineage in TopBraid EDG
Data flows
Logical
flows
Info
Exchanged

§ A Knowledge Graph represents a knowledge domain
– With “Active Models” that can be consulted in real-time
§ It represents knowledge as a graph
– A network of nodes and links
– Not tables of rows and columns
§ It represents facts (data) and models (metadata) in the
same way
– Rich rules and inferencing
§ It is based on open standards, from top to bottom
– Readily connects to knowledge in private and public clouds
Knowledge Graphs as a Foundation
A knowledge graph, with its Active Models, grows and evolves over time.

Benefits of a Knowledge Graph based
Platform for Data Governance
TopBraid Enterprise Data Governance (EDG):
§ Bridges data and metadata “silos” for
seamless metadata management
§ Integrates reasoning and machine learning
§ Is flexible and extensible, based on standards
§ Enables people (UI) and software (APIs/web
services) to view, follow and query
§ Delivers Knowledge-driven data governance
For more go to: http://www.topquadrant.com/products/topbraid-enterprise-data-governance

Vocabulary Management
lets organizations create, connect
and use taxonomies and ontologies
Metadata Management
lets organizations govern technical
metadata
Reference Data Management
makes it easy to bring consistency
and accuracy to the use of reference
data across enterprise applications
Business Glossaries
provides a simple starting place for a
data governance initiative
TopBraid Explorer lets a broader
community of data stakeholders
explore information governed by
TopBraid EDG
Customer Defined Asset
Collection Type is and add-on
module that can be added to any of
the packages within TopBraid EDG.
TopBraid Tagger and
AutoClassifier links controlled
vocabulary terms to content. Content
resources can be tagged manually or
auto-tagged using Tagger's AutoClassifier
capabilities.
TopBraid Data Platform provides
high availability TopBraid servers for
continuous operation of business
functions by replicating data across a
cluster of servers
TOPBRAID EDG - GOVERNANCE PACKAGES
TOPBRAID EDG – ADD ON MODULES
TopBraid EDG - the Flexible Way to Grow Your
Data Governance Program
Contact us at: info@topquadrant.com

3
© Copyright 2021 by Peter Aiken Slide #
Program
Growing
Practical Data
Governance
Programs
• 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
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?"

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
• Lack of standards/
poor literacy/
unknown
dependencies
• (Re)learned by
every
workgroup
Wally Easton Playing Piano
https://www.youtube.com/watch?v=NNbPxSvII-Q
6

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
45%
50%
55%
60%
65%
2019 2020 2021
49%
64%
60%
35%
38%
42%
45%
48%
2019 2020 2021
41%
45%
48%
34%
38%
42%
46%
50%
2019 2020 2021
39%
50%
47%
17%
22%
28%
33%
38%
2019 2020 2021
24%
38%
31%
17%
22%
28%
33%
38%
2017 2018 2019 2020 2021
24%
38%
31%
32%
37%
Source: Big Data and AI Executive Survey 2021 by Randy Bean and Thomas Davenport www.newvantage.com
2021
0%
25%
50%
75%
100%
% of challenges: technology % of challenges: people/process
92.2%
7.8%
No significant changes

Root cause analysis is part of data governance
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
Pre-Information Age Metadata
• Examples of information architecture achievements that happened
well before the information age:
– Page numbering
– Alphabetical order
– Table of contents
– Indexes
– Lexicons
– Maps
– Diagrams
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
https://plusanythingawesome.com 11
Separating the Wheat from the Chaff
• Data that is better organized 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
https://plusanythingawesome.com 12

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

15
Program
Growing
Practical Data
Governance
Programs
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.

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?
Metadata
Management
24
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
Perfecting
operations in 3
data management
practice areas
Metadata
2X
2X
1X

Iteration 3
Data
Strategy
Data
Governance
BI/
Warehouse
Reference &
Master Data
Perfecting
operations in 3
data management
practice areas
1X
3X
3X
(Things that further)
Organizational Strategy
Lighthouse Project 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
28

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
30
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 31
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
Data Strategy in Context – THIS IS WRONG!
IT Strategy
Data Strategy
x 32

IT Strategy
This is correct …
Data Strategy
33
Data-Centric Development
Original articulation from Doug Bagley @ Walmart 34
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

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
Data
Leadership
Feedback
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
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
X
$
X
$
X
$
X
$
X
$
X
$
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 driving IT programs
Common Organizational Data
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Create
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
Data programmes driving IT programs
42
Program
Growing
Practical Data
Governance
Programs

Making a Better
Data Sandwich
Standard data
Data supply
Data literacy
Making a Better Data Governance Sandwich
Data literacy
Standard data
Data supply
44

Making a Better Data Governance Sandwich
Standard data
Data supply
Data literacy
45
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!
46

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!
47
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
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)
Guidance
Decisions
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
IT/Systems
Development
Data/feedback
Changes
Action
R
e
s
o
u
r
c
e
s
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
e
s
o
u
r
c
e
s
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
62

Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
63
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
64

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
68

Evolve
69
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
72
Program
Growing
Practical Data
Governance
Programs

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
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

Long Tail
– Establish optimal monitoring targets
– Finer tuned and safer maintenance
– Mission Readiness ???
– Storage $$$
– Handling $$$
– Opportunity $$$
– Systemic $$$
– Maintenance $$$
– Total > $1.5 Billion
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 …

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."
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

83
Program
Growing
Practical Data
Governance
Programs
84
Program
Growing
Practical Data
Governance
Programs

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

Data Management + Data Strategy = Interoperability
13 April 2021
Data Architecture and Data Modeling Differences:
Achieving a common understanding
11 May 2021
Why Data Modeling
Is Fundamental
8 June 2021
Upcoming Events
Brought to you by:
Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
peter@plusanythingawesome.com +1.804.382.5957
Questions?
Thank You!
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DataEd Slides: Growing Practical Data Governance Programs

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