Necessary Prerequisites to Data Success

Exorcising the Seven
Deadly Data Sins
© Copyright 2021 by Peter Aiken Slide # 6
peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD
Necessary Prerequisites to Data Success
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)
• 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
https://anythingawesome.com
+
• DAMA International President 2009-2013/2018/2020
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005

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 Debt – Getting Back to Zero
• Data debt
– The time and effort it will take to return
your 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
• At zero-must start from scratch
– Typically requires annual proof of value
– Now you need to get good at both
• Almost all data challenges involve
interoperability
– Little guidance at optimizing data
management practices
– Very little guidance at getting back to
zero

Exorcising
the Seven
Deadly
Data Sins
© Copyright 2021 by Peter Aiken Slide #
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of
Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data
Strategy Implementation
Failing To Address
Cultural And Change
Management Challenges
g Data-
ng
Lacking Qualified Data
Leadership
Failing to Implement a
Programmatic Way to
Share Data
Not Aligning the Data
Program with IT Projects
ailing to Adequately
anage Expectations
Not Sequencing Data
Not Addressing Cultural
and Change
2 3 4
5 6 7
10
Program
Credit: Image credit: Matt Vickers

CIOs
aren't 12
A Single Focus
• Chief
– The head or leader of an organized body of people;
the person highest in authority: the chief of police
• Chief Financial Officer (CFO)
– Individual possessing the knowledge, skills, and abilities to be both
the final authority and decision-maker in organizational financial
matters
• Chief Risk Officer (CRO)
– Individual possessing the knowledge, skills, and abilities makes
decisions and implements risk management
• Chief Medical Officer (CMO)
– Responsible for organizational medical matters. The organization,
and the public, has similar expectations for any of chief officer –
especially after the Sarbanes-Oxley bill.
[dictionary.com]
• Chief
– The head or leader of an organized body of people;
the person highest in authority: the chief of police
• Chief Financial Officer (CFO) ← does not balance books
– Individual possessing the knowledge, skills, and abilities to be both
the final authority and decision-maker in organizational financial
matters
• Chief Risk Officer (CRO) ← does not test software
– Individual possessing the knowledge, skills, and abilities makes
decisions and implements risk management
• Chief Medical Officer (CMO) ← does not perform surgery
– Responsible for organizational medical matters. The organization,
and the public, has similar expectations for any of chief officer –
especially after the Sarbanes-Oxley bill.

Chief Data Officer
Combat
Recasting the executive team. make full use of the most
valuable assets

Change the status quo!
• Keep in mind that the appointment of a
CDO typically comes from a high-level
decision. In practice, it can trigger an
array of problematic reactions within
the organization including:
– Confusion,
– Uncertainty,
– Doubt,
– Resentment and
– Resistance.
• CDOs need to rise to the challenge of
changing the status quo if they expect to
lead the business in making data a
strategic asset.
– from What Chief Data Officers Need to Do to Succeed by Mario Faria
https://www.forbes.com/sites/gartnergroup/2016/04/11/what-chief-data-officers-
need-to-do-to-succeed/#734d53a8434a
Change Management & Leadership

Diagnosing Organizational Readiness
adapted from the Managing Complex Change model by Lippitt, 1987
Culture is the biggest impediment to a
shift in organizational thinking about data!
18
No cost, no registration case study download
8
EXPERIENCE: Succeeding at Data Management—BigCo Attempts to
Leverage Data
PETER AIKEN, Virginia Commonwealth University/Data Blueprint
In a manner similar to most organizations, BigCompany (BigCo) was determined to benefit strategically from
its widely recognized and vast quantities of data. (U.S. government agencies make regular visits to BigCo to
learn from its experiences in this area.) When faced with an explosion in data volume, increases in complexity,
and a need to respond to changing conditions, BigCo struggled to respond using a traditional, information
technology (IT) project-based approach to address these challenges. As BigCo was not data knowledgeable,
it did not realize that traditional approaches could not work. Two full years into the initiative, BigCo was
far from achieving its initial goals. How much more time, money, and effort would be required before results
were achieved? Moreover, could the results be achieved in time to support a larger, critical, technology-driven
challenge that also depended on solving the data challenges? While these questions remain unaddressed,
these considerations increase our collective understanding of data assets as separate from IT projects.
Only by reconceiving data as a strategic asset can organizations begin to address these new challenges.
Transformation to a data-driven culture requires far more than technology, which remains just one of three
required “stool legs” (people and process being the other two). Seven prerequisites to effectively leveraging
data are necessary, but insufficient awareness exists in most organizations—hence, the widespread misfires
in these areas, especially when attempting to implement the so-called big data initiatives. Refocusing on
foundational data management practices is required for all organizations, regardless of their organizational
or data strategies.
Categories and Subject Descriptors: H.2.0 [Information Systems]: Database Management—General; E.0
[Data]: General
General Terms: Management, Performance, Design
Additional Key Words and Phrases: Data management, data governance, data stewardship, organizational
design, CDO, CIO, chief data officer, chief information officer, data, data architecture, enterprise data exec-
utive, IT management, strategy, policy, enterprise architecture, information systems, conceptual modeling,
data integration, data warehousing, analytics, and business intelligence, BigCo
ACM Reference Format:
Peter Aiken. 2016. Experience: Succeeding at data management—BigCo attempts to leverage data. J. Data
and Information Quality 7, 1–2, Article 8 (May 2016), 35 pages.
DOI: http://dx.doi.org/10.1145/2893482
1. CASE INTRODUCTION
Good technology in the hands of an inexperienced user rarely produces positive
results.
Everyone wants to “leverage” data. Today, this is most often interpreted as invest-
ments in warehousing, analytics, business intelligence (BI), and so on. After all, that
is what you do with an asset—you leverage it—so the asset can help you to attain
strategic objectives; see Redman [2008] and Ladley [2010]. Widespread and pervasive
Author’s address: P. Aiken, 10124C West Broad Street, Glen Allen, VA 23060; email: peter.aiken@vcu.edu.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted
without fee provided that copies are not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. Copyrights for components of this work owned
by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request
permissions from Permissions@acm.org.
2016 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 1936-1955/2016/05-ART8 $15.00
DOI: http://dx.doi.org/10.1145/2893482
ACM Journal of Data and Information Quality, Vol. 7, No. 1–2, Article 8, Publication date: May 2016.
• Download
– http://dl.acm.org/citation.cfm?doid=2888577.2893482
or
http://tinyurl.com/PeterStudy
• Download Here!

Exorcising
the Seven
Deadly
Data Sins
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
g Data-
ng
Leadership
Programmatic Way to
Share Data
anage Expectations
Not Sequencing Data
and Change
2 3 4
5 6 7
Not Understanding Data-
Centric Thinking
Leadership
Programmatic Way to
Share Data
Failing to Adequately
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
20
Program
Metadata
Management
21
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

Enforced sequencing
• Before further construction could proceed
• No IT equivalent
You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Practices however
this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
(with thanks to
Tom DeMarco)
Unenforced sequencing
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
T
e
c
h
n
o
l
o
g
i
e
s
C
a
p
a
b
i
l
i
t
i
e
s
23

Q1
Organizations
without
a formalized
data strategy
Q3
Data Strategy: Use data
to create strategic
opportunities
Q4
Data Strategy: both
Improve Operations
Innovation
Data focus should be sequenced
Only 1 is 10 organizations has a board
approved data strategy!
Q2
Data Strategy: Increase
organizational efficiencies/
effectiveness
X
X
Exorcising
the Seven
Deadly
Data Sins
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
g Data-
ng
Leadership
Programmatic Way to
Share Data
anage Expectations
Not Sequencing Data
and Change
2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
25
Program

• Benefits & Success Criteria
• Capability Targets
• Solution Architecture
• Organizational Development
Solution
• Leadership & Planning
• Project Dev. & Execution
• Cultural Readiness
Road Map
• Organization Mission
• Strategy & Objectives
• Organizational Structures
• Performance Measures
Business Needs
• Organizational / Readiness
• Business Processes
• Data Management Practices
• Data Assets
• Technology Assets
Current State
• Business Value Targets
• Capability Targets
• Tactics
• Data Strategy Vision
Strategic Data Imperatives
Business
Needs
Existing
Capabilities
Execution
Business
Value
New
Capabilities
Getting Started with Data
Data Program Expenses
• 5 Data Professionals
– Each paid $100,000/year
– Overhead?
– Do they feel obligated to demonstrate
$500,000 in benefits annually?
• When will you be done?
– "It's okay my CIO gave me 5 years!"
– Revised benefits goal is $2.5 million

• GDIP
improving how the state prices and sells its goods and services, and more efficiently matching
citizens to benefits when they enroll.
“The first year of our data internship partnership has been a success,” said Governor McAuliffe.
“The program has helped the state save time and money by making some of our internal
processes more efficient and modern. And it has given students valuable real-world experience. I
look forward to seeing what the second year of the program can accomplish.”
“Data is an important resource that becomes even more critical as technology progresses,” said
VCU President Michael Rao, Ph.D. “VCU is uniquely positioned, both in its location and
through the wealth of talent at the School of Business, to help state agencies run their data-
centric systems more efficiently, while giving our students hands-on practice in the development
of data systems.”
During their internships, pairs of VCU students work closely with state agency CIOs to identify
specific business cases in which data can be used. Participants gain practical experience in using
data to drive re-engineering, while participating CIOs have concrete examples of how to make
better use of data to provide innovative and less costly services to citizens.
"Working with the talented VCU students gave us a different perspective on what the data was
telling us,” said Dave Burhop, Deputy Commissioner/CIO of the Virginia Department of Motor
Vehicles.
“The VCU interns provided an invaluable resource to the Governor’s Coordinating Council on
Homelessness,” said Pamela Kestner, Special Advisor on Families, Children and Poverty.
“They very effectively reviewed the data assets available in the participating state agencies and
identified analytic content that can be used to better serve the homeless population.”
“It's always useful to have ‘fresh eyes’ on data that we are used to seeing,” said Jim Rothrock,
Commissioner of the Department for Aging and Rehabilitative Services. “Our interns challenged
us and the way we interpret data. It was a refreshing and useful, and we cannot wait for new
experiences with new students.”
The data internships support Governor McAuliffe’s ongoing initiative to provide easier access to
open data in Virginia. The internships also support treating data as an enterprise asset, one of
four strategic goals of the enterprise information architecture strategy adopted by the
Commonwealth in August 2013. Better use of data allows the Commonwealth to identify
opportunities to avoid duplicative costs in collecting, maintaining and using information; and to
integrate services across agencies and localities to improve responses to constituent needs and
optimize government resources.
Virginia Secretary of Technology Karen Jackson and CIO of the Commonwealth Nelson Moe
are leading the effort on behalf of the state. Students who want to apply for internships should
contact Peter Aiken (peter.aiken@vcu.edu) for additional information.
28
Commonwealth
Data Interns Program
Exorcising
the Seven
Deadly
Data Sins
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
g Data-
ng
Leadership
Programmatic Way to
Share Data
anage Expectations
Not Sequencing Data
and Change
2 3 4
5 6 7
acking Qualified Data
Leadership
Programmatic Way to
Share Data
ately
tions
Not Sequencing Data
and Change
2 3 4
6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
29
Program

IT Project or Application-Centric Development
Original articulation from Doug Bagley @ Walmart 30
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
Development Under the Data Doctrine®
Original articulation from Doug Bagley @ Walmart 31
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 Governance in Strategic 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
IT Projects
Organizational Operations
Data Strategy and Governance in Strategic Context
(Business Goals)
(Metadata)
Data asset support for
What the data assets do to
support strategy
How well the data strategy is working
Organizational
Strategy
Data
Governance
Data Strategy

Exorcising
the Seven
Deadly
Data Sins
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
g Data-
ng
Leadership
Programmatic Way to
Share Data
anage Expectations
Not Sequencing Data
and Change
2 3 4
5 6 7
t Understanding Data-
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Leadership
Programmatic Way to
Share Data
ately
tions
Not Sequencing Data
and Change
2 3 4
6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
34
Program
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!

Design
Requirements
Implementation
Verification
Maintenance
Develop/Implement
Software
Develop/Implement Data
Project Implementation
Data management and software development
must be separated and sequenced
This approach can only work,
when no sharing of data occurs!
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Projects Are Silos
Project 1 Project 2
Shared data structures require programmatic
development and evaluation
Project 3
X X
X X X X
X
X X
X X
Design
Requirements
Implementation
Verification
Maintenance
Design
Requirements
Implementation
Verification
Maintenance

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 Human Resources
(HR) program!
Exorcising
the Seven
Deadly
Data Sins
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
g Data-
ng
Leadership
Programmatic Way to
Share Data
anage Expectations
Not Sequencing Data
and Change
2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Leadership
Programmatic Way to
Share Data
ately
tions
Not Sequencing Data
and Change
2 3 4
6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
39
Program

What do we teach knowledge workers about data?
What percentage of the deal with it daily?
What do we teach IT professionals about data?
• 1 course
- How to build a
new database
• What
impressions do IT
professionals get
from this
education?
- Data is a technical
skill that is needed
when developing
new databases

Hiring Panels Are Often Challenged to Help
Top Data Job
• Dedicated solely to data asset leveraging
• Unconstrained by an IT project mindset
• Reporting to the business
Top
Operations
Job
Top Job
Top
Finance
Job
Top
IT
Job
Top
Marketing
Job
Data Governance Organization
Top
Data
Job
Enterprise
Data
Executive
Chief
Data
Officer

The Enterprise Data Executive Takes One for the Team
Exorcising
the Seven
Deadly
Data Sins
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
Not Sequencing Data
Failing To Address
Cultural And Change
g Data-
ng
Leadership
Programmatic Way to
Share Data
anage Expectations
Not Sequencing Data
and Change
2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Leadership
Programmatic Way to
Share Data
ately
tions
Not Sequencing Data
and Change
2 3 4
6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
Programmatic Way to
Share Data
Manage Expectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
45
Program

Data is a hidden IT Expense
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
Organizations spend between 20 -
40% of their IT budget evolving their
data - including:
• Data migration
- Changing the location from one place to another
• Data conversion
- Changing data into another form, state, or product
• Data improving
- Inspecting and manipulating, or re-keying data to
prepare it for subsequent use
– Source: John Zachman
Data –
Driven?
Centric?
Focused?
First?
Provocateur?
…?

?
?
?
?
?
?
What?
Does?
Any?
OF?
This?
Mean?

https://agilemanifesto.org https://thedatadoctrine.com
the Data Doctrine® (V2)
We are uncovering better ways of developing
IT systems by doing it and helping others do it.
Through this work we have come to value:
data programmes driving IT programs
informed information investing over technology acquisition activities
stable, shared organizational data over IT component evolution
data reuse over the acquisition of new data sources
That is, while there is value in the items on
the right, we value the items on the left more.
Inspiration from: https://agilemanifesto.org

data programmes driving IT programs
D
Data programmes driving IT programs
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 and IT must be
separated and
sequenced

Running
Query
Optimized Query

Data Footprints
• SQL Server
– 47,000,000,000,000 bytes
– Largest table 34 billion records 3.5 TBs
• Informix
– 1,800,000,000 queries/day
– 65,000,000 tables / 517,000 databases
• Teradata
– 117 billion records
– 23 TBs for one table
• DB2
– 29,838,518,078 daily queries
Repeat 100s, thousands, millions of times ...

Death by 1000 Cuts
W o r k i n g
W h i l e
B l e e d i n g
P r o f u s e l y
D E A T H
B Y A
T H O U S A N D
C U T S
58
Working While Bleeding
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bleeding
unnecessarily
from a lots of
cuts
Poor data manifests as multifaceted organizational challenges

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
Consistency Encourages Quality Data Gathering
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

data programs driving IT programs
BR2) One EMPLOYEE can be
associated with one POSITION
Stable shared data structures over IT component evolution
Person Job Class
Position
BR1) One EMPLOYEE
can be associated with one
PERSON
Manual
Job Sharing
Manual
Moon Lighting
Employee
65

Person Job Class
Employee Position
BR1) Zero, one, or more
EMPLOYEES can be associated
with one PERSON
BR2) Zero, one, or more EMPLOYEES
can be associated with one POSITION
Job Sharing
Moon Lighting
66
Data structures must be specified prior
software development/acquisition!
software development/acquisition!
IT development/acquisition
(Requires 2 structural loops more than the
more flexible data structure)
More flexible data structure Less flexible data structure
67

Results
Increasing utility of organizational data
Individual IT Project
Requirements
Design
Implement
Requests Results
Requirements
Design
Implement
Requests
Results
Requirements
Design
Implement
Requests
Organized,
shared data
Organized,
shared data
Organized,
shared data
Shared data driving IT component evolution
• Over time the:
– Number of requests increase
– Utility of the results increase
– Data's contribution increases
– and is recognized!
Shared data structures cannot
exist without programmatic
development and evaluation

My most profound lesson! (so far)
Garbage In ➜ Garbage Out!
Perfect
Model
Garbage
Data
Garbage
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Data
Governance
Analytics
Technology
GI➜GO!

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

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

Perfect
Model
Quality
Data
Good
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
Quality In ➜ Quality Out!
Insufficient
Quality and
Quantity of
Data
No
Results
Machine
Learning
Today

Data reuse preceding new data acquisition
• Reusable software has been valued more than data
• Who makes decisions about the range and scope of
common data usage?
• Change a program
- 9 max changes
• Change data
- Worst case
- (N * (N - 1)) / 2
- (9 * 8)/2 = 36
Program F
Program E
Program H
Program I
domain 2
Application
domain 3
79
Program D
Program G
Application

IT Business
Data
Perceived State of Data
Data
Desired To Be State of Data
IT Business

The Real State of Data
Data
IT Business
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Decades in the making, how we ended up with data
chaos…
and how to fix it!
Proprietary and confidential, Validity, Inc.

Proprietary and confidential, Validity, Inc. 2

Database Marketing
Customer Relationship
Management
The Dark Ages

Implementation Ecosystem
The
Enlightenment

Scale Automation
The Industrial
Revolution

Leadership Prioritization
Cross-Functional Data Operations
Ongoing Data Governance
The Modern
Age’s “Elite 8”

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Necessary Prerequisites to Data Success

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