In many organizations and functional areas, data has pulled even with money in terms of what makes the proverbial world go ‘round. As businesses struggle to cope with the 21st century’s newfound data flood, it is more important than ever before to prioritize data as an asset that directly supports business imperatives. However, while organizations across most industries make some attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality), the results of these efforts frequently fall far below expectations. At the root of many of these failures is poor organizational data management—which fortunately is a remediable problem. This webinar will cover three lessons, each illustrated with examples, that will help you establish realistic goals and benchmarks for data management processes and communicate their value to both internal and external decision makers: - How organizational thinking must change to include value-added data management practices - The importance of walking before you run with data-focused initiatives - Prioritizing specification and data governance over “silver bullet” analytical tools

1. Data-Centric Strategy & Roadmap
Supercharging Your Organization
Copyright 2017 by Data Blueprint Slide # 1
Peter Aiken, Ph.D.
Peter Aiken, Ph.D.
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
2Copyright 2017 by Data Blueprint Slide #

2. Copyright 2017 by Data Blueprint Slide #Copyright 2017 by Data Blueprint Slide # 3
Excerptedfrom
YourDataStrategy
Empire State Plaza
4Copyright 2017 by Data Blueprint Slide #

3. • A data strategy specifies how data assets are to be used to
support the organizational strategy
– What is strategy?
– What is a data strategy?
– How do they work together?
• A data strategy is necessary for effective data governance
– Improve your organization’s data
– Improve the way people use their data
– Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites
– Lack of organizational readiness
– Failure to compensate for the lack of data competencies
– Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations
– Lather, rinse, repeat
– A balanced approach is required
• Q&A
Data-Centric Strategy & Roadmap
5Copyright 2017 by Data Blueprint Slide #
Tweeting now:
#dataed
Simon Sinek: How great leaders inspire action
6Copyright 2017 by Data Blueprint Slide #
• “It’s not what you do,
it’s why you do it”
• “People don't buy what
you do - they buy why
you do it”
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
What
How
Why

4. What is a Strategy?
7Copyright 2017 by Data Blueprint Slide #
• Current use derived from military
• “a pattern in a stream of decisions” [Henry Mintzberg]
Strategy in Action: Napoleon defeats a larger enemy
• Question?
– How do I defeat the competition when their forces
are bigger than mine?
• Answer:
– Divide
and
conquer!
– “a pattern
in a stream
of decisions”
8Copyright 2017 by Data Blueprint Slide #
– “a pattern
in a stream
of decisions”

5. Wayne Gretzky’s
Definition of Strategy
9Copyright 2017 by Data Blueprint Slide #
He skates to where he
thinks the puck will be ...
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 √ √ √ √
We believe ...
• 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!
• Our mission is to unlock business value by
– Strengthening your data management capabilities
– Providing tailored solutions, and
– Building lasting partnerships
10Copyright 2017 by Data Blueprint Slide #
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]
Data Assets Win!

6. Organizational Assets
• Cash & other financial instruments
• Real property
• Inventory
• Intellectual Property
• Human
– Knowledge
– Skills
– Abilities
• Financial
• Organizational reputation
• Good will
• Brand name
• Data!!!
11Copyright 2017 by Data Blueprint Slide #
CEOs are Recognizing Data as an Asset
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
Copyright 2017 by Data Blueprint Slide # 12

7. 13Copyright 2017 by Data Blueprint Slide #
IT Business
Data
As Is State of Data (as Perceived)
|————— Project-based —————| |——— Program-based ———|
|——————————————— Program-based ——————————————|
14Copyright 2017 by Data Blueprint Slide #
IT Business
Data
|————— Project-based —————|
Desired To Be State of Data (as Understood)

8. There will never
be less data
than right now!
15Copyright 2017 by Data Blueprint Slide #
Your Data Strategy Should ...
• Improve your organization’s data
• Improve the way your people use data
• Improve the way your people use data
to achieve your organizational strategy
• A data strategy is the highest level data guidance available to an
organization, ...
• ... focusing data-related activities on articulated data goal
achievements and ...
• ... providing directional but specific guidance when faced with a
stream of decisions or uncertainties about organizational data
assets and their application toward business objectives.
16Copyright 2017 by Data Blueprint Slide #
Improve your
organization’s data
Improve the way your
people use its data
Improve the way your
data and your people
support your
organizational strategy

9. 17Copyright 2017 by Data Blueprint Slide #
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 supply
Standard data
Data literacy
Making a Better Data Sandwich
18Copyright 2017 by Data Blueprint Slide #
Data literacy
Standard data
Data supply

10. Making a Better Data Sandwich
19Copyright 2017 by Data Blueprint Slide #
Standard data
Data supply
Data literacy
Making a Better Data Sandwich
20Copyright 2017 by Data Blueprint Slide #
Standard data
Data supply
Data literacy

11. 21Copyright 2017 by Data Blueprint Slide #
• A data strategy specifies how data assets are to be used to
support the organizational strategy
– What is strategy?
– What is a data strategy?
– How do they work together?
• A data strategy is necessary for effective data governance
– Improve your organization’s data
– Improve the way people use their data
– Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites
– Lack of organizational readiness
– Failure to compensate for the lack of data competencies
– Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations
– Lather, rinse, repeat
– A balanced approach is required
• Q&A
Data-Centric Strategy & Roadmap
Tweeting now:
#dataed
22Copyright 2017 by Data Blueprint Slide #
Data Science
The Sexiest Job
of the 21st
Century

12. Data Scientist?
23Copyright 2017 by Data Blueprint Slide #
Data science is a redundant term,
since all science involves data; it's
like saying, "book librarian."
Eric Siegel, Ph.D., author of Predictive
Analytics: The Power to Predict Who
Will Click, Buy, Lie, or Die
0
25
50
75
100
Current Improved
Manipulation Analysis
Data Scientist Productivity
24Copyright 2017 by Data Blueprint Slide #
A 20% improvement results in a doubling of productivity!
• Currently:
– 80% of their time manipulating data and 20% of their time analyzing data
– Hidden productivity bottlenecks
• After rearchitecting:
– Less time manipulating data and more of their time analyzing data
– Significant improvements in all knowledge worker productivity

13. Welcome to the Post-Big Data Era!
25Copyright 2017 by Data Blueprint Slide #
Data Velocity
Data Volume
Data Variety
Big Data: Expanding on 3
Fronts at an Increasing Rate
Big Data(has something to do with Vs - doesn't it?)
• Volume
– Amount of data
• Velocity
– Speed of data in and out
• Variety
– Range of data types and sources
• 2001 Doug Laney
• Variability
– Many options or variable interpretations confound analysis
• 2011 ISRC
• Vitality
–A dynamically changing Big Data environment in which analysis and predictive models
must continually be updated as changes occur to seize opportunities as they arrive
• 2011 CIA
• Virtual
– Scoping the discussion to only include online assets
• 2012 Courtney Lambert
• Value/Veracity
• Stuart Madnick (John Norris Maguire Professor of Information Technology, MIT Sloan School of
Management & Professor of Engineering Systems, MIT School of Engineering)
26Copyright 2017 by Data Blueprint Slide #

14. The 13 V’s of Big Data
• Vast Volume of Vigorously, Verified, Vexingly, Variable,
Verbose yet Valuable, Vital, Visualized, high Velocity and
Veracity data that encourages the Vanity of the big data
experts
– Original from John Marshey – Sillicon Graphics 1998
(with contributed extensions)
27Copyright 2017 by Data Blueprint Slide #
• We have no objective
definition of big data!
– Any measurements,
claims of success,
quantifications, etc.
must be viewed
skeptically and with
suspicion!
I shall not today
attempt further to
define the kinds of
material but I know
it when I see it ...
(Justice Potter Stewart)
28Copyright 2017 by Data Blueprint Slide #

15. Big Data
29Copyright 2017 by Data Blueprint Slide #
Big Data
30Copyright 2017 by Data Blueprint Slide #

16. [ Techniques /
Technologies ]
31Copyright 2017 by Data Blueprint Slide #
Big Data
32Copyright 2017 by Data Blueprint Slide #
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)

17. Reasons for a Data Strategy
33Copyright 2017 by Data Blueprint Slide #
Improve your
organization’s data
Improve the way your
people use its data
Improve the way your
data and your people
support your
organizational strategy
• Because data
points to where
valuable things
are located
• Because data has
intrinsic value by
itself
• Because data
has inherent
combinatorial
value
• Valuing Data
– Use data to
measure change
– Use data to
manage change
– Use data to
motivate change
• Creating a
competitive
advantage with
data
• Old model
– Sell jet engines
• New model
– Sell hours of thrust power
– No payment for down time
– Wing to wing
What can Rolls Royce Learn
34Copyright 2017 by Data Blueprint Slide #
From Nascar?

18. 35Copyright 2017 by Data Blueprint Slide #
• A data strategy specifies how data assets are to be used to
support the organizational strategy
– What is strategy?
– What is a data strategy?
– How do they work together?
• A data strategy is necessary for effective data governance
– Improve your organization’s data
– Improve the way people use their data
– Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites
– Lack of organizational readiness
– Failure to compensate for the lack of data competencies
– Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations
– Lather, rinse, repeat
– A balanced approach is required
• Q&A
Data-Centric Strategy & Roadmap
Tweeting now:
#dataed
36Copyright 2017 by Data Blueprint Slide #
Data Strategy
Data Strategy is Implemented in 2 Phases
What the
data assets do to
support strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determined how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive (and
other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)

19. What do we teach IT professionals about data?
37Copyright 2017 by Data Blueprint Slide #
• 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
• If we are migrating databases, we are not creating new
databases and we don't need organizational data
management knowledge, skills, and abilities (KSAs).
• If we are implementing a new software package, we are
not creating a new database and therefore we do not
need data management KSAs.
• If we are installing an enterprise resource package
(ERP), we are not creating a new database and therefore
we do not need data management KSAs.
Hiring Panels Are Often
Not Qualified to Help
38Copyright 2017 by Data Blueprint Slide #

20. 39Copyright 2017 by Data Blueprint Slide #
Without foundational
practices everything:
• Takes longer
• Costs more
• Delivers less
• Presents
greater
risk (with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
40Copyright 2017 by Data Blueprint Slide #

21. 41Copyright 2017 by Data Blueprint 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
Exorcising the Seven Deadly Data Sins
42Copyright 2017 by Data Blueprint Slide #
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
Manage Expectations
Not Sequencing Data
Strategy Implementation
Not Addressing Cultural
and Change
Management Challenges
2 3 4
5 6 7
Not Understanding Data-
Centric Thinking
Lacking Qualified Data
Leadership
Failing to Implement a
Programmatic Way to
Share Data
Not Aligning the Data
Program with IT Projects
Failing to Adequately
Manage Expectations
Not Sequencing Data
Strategy Implementation
Not Addressing Cultural
and Change
Management Challenges
1 2 3 4
5 6 7
Not Understanding Data-
Centric Thinking
Lacking Qualified Data
Leadership
Failing to Implement a
Programmatic Way to
Share Data
Not Aligning the Data
Program with IT Projects
Failing to Adequately
Manage Expectations
Not Sequencing Data
Strategy Implementation
Not Addressing Cultural
and Change
Management Challenges
1 2 3 4
5 6 7t Understanding Data-
Centric Thinking
Lacking Qualified Data
Leadership
Failing to Implement a
Programmatic Way to
Share Data
Not Aligning the Data
Program with IT Projects
Failing to Adequately
Manage Expectations
Not Sequencing Data
Strategy Implementation
Not Addressing Cultural
and Change
Management Challenges
1 2 3 4
5 6 7
acking Qualified Data
Leadership
Failing to Implement a
Programmatic Way to
Share Data
Not Aligning the Data
Program with IT Projects
ately
tions
Not Sequencing Data
Strategy Implementation
Not Addressing Cultural
and Change
Management Challenges
2 3 4
6 7
Not Understanding Data-
Centric Thinking
Lacking Qualified Data
Leadership
Failing to Implement a
Programmatic Way to
Share Data
Not Aligning the Data
Program with IT Projects
Failing to Adequately
Manage Expectations
Not Sequencing Data
Strategy Implementation
Not Addressing Cultural
and Change
Management Challenges
1 2 3 4
5 6 7
Not Understanding Data-
Centric Thinking
Lacking Qualified Data
Leadership
Failing to Implement a
Programmatic Way to
Share Data
Not Aligning the Data
Program with IT Projects
Failing to Adequately
Manage Expectations
Not Sequencing Data
Strategy Implementation
Not Addressing Cultural
and Change
Management Challenges
1 2 3 4
5 6 7

22. The Enterprise Data Executive Takes One for the Team
43Copyright 2017 by Data Blueprint Slide #
Unicorn License There Are No Unicorns
44Copyright 2017 by Data Blueprint Slide #

23. There will never
be less data
than right now!
45Copyright 2017 by Data Blueprint Slide #
the Data Doctrine
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 preceding software development
Stable data structures preceding stable code
Shared data preceding completed software
Data reuse preceding reusable code
That is, while there is value in the items on
the right, we value the items on the left more.
46Copyright 2017 by Data Blueprint Slide #

24. Introducing The Data Doctrine
Copyright 2017 by Data Blueprint Slide #
47
http://www.thedatadoctrine.com
48Copyright 2017 by Data Blueprint Slide #
• A data strategy specifies how data assets are to be used to
support the organizational strategy
– What is strategy?
– What is a data strategy?
– How do they work together?
• A data strategy is necessary for effective data governance
– Improve your organization’s data
– Improve the way people use their data
– Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites
– Lack of organizational readiness
– Failure to compensate for the lack of data competencies
– Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations
– Lather, rinse, repeat
– A balanced approach is required
• Q&A
Data-Centric Strategy & Roadmap
Tweeting now:
#dataed

25. 49Copyright 2017 by Data Blueprint Slide #
Data Strategy
Data Strategy is Implemented in 2 Phases
Phase I-Prerequisites
1) Prepare for dramatic change and determined how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive
(and other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)
You
are
here
1) Identify the primary constraint keeping data from fully supporting strategy
2) Exploit organizational efforts to remove this constraint
3) Subordinate everything else to this exploitation decision
4) Elevate the data constraint
5) Repeat the above steps to address the new constraint
Theory of Constraints
50Copyright 2017 by Data Blueprint Slide #

26. QR Code for PeterStudy
• Free Case Study Download• Free Case Study Download
– http://dl.acm.org/citation.cfm?doid=2888577.2893482
or
http://tinyurl.com/PeterStudy
or scan the QR Code at the right
51Copyright 2017 by Data Blueprint Slide #
• First, fix the prerequisites!
• The problem:
– Issuing new store numbers
– Spreadsheet based
– Pervasive
– Not governed
– Would issue the last available store number is this calendar year
• The solution:
1. Identify-data/systems inventory
2. Exploit-3 digit expanded to 10 digits
3. Subordinate-prioritize and sequence remediation
4. Elevate-EXECUTE!
5. Repeat the above steps to address the new constraint
Sample: Reengineering the Location Data Element
52Copyright 2017 by Data Blueprint Slide #

27. Why a Data Strategy?
53Copyright 2017 by Data Blueprint Slide #
Managing
Data with
Guidance
Managing Data with Guidance
• How should data be used and in which business
processes?
• How is data shared among users, divisions, geographies
and partners?
• What processes and
procedures allow for
data to be changed?
• Who manages
approval processes?
• What processes
ensure compliance?
54Copyright 2017 by Data Blueprint Slide #

28. Data Strategy Framework
55Copyright 2017 by Data Blueprint Slide #
• 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
ExecutionBusiness
Value
New
Capabilities
V1
Organizations
without
a formalized
data strategy
V3
Data Strategy: Use data
to create strategic
opportunities
V4
Data Strategy: Get good
at both V2 and V3
Improve Operations
Innovation
The focus of data strategy should be sequenced
56Copyright 2017 by Data Blueprint Slide #
Only 1 is 10 organizations has a board
approved data strategy!
V2
Data Strategy: Increase
organizational efficiencies/
effectiveness

29. Changing is Hard
Culture is the biggest impediment to a shift
in organizational thinking about data
57Copyright 2017 by Data Blueprint Slide #
adapted from the Managing Complex Change model by Dr. Mary Lippitt, 1987
Discussion
58Copyright 2017 by Data Blueprint Slide #
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions now.
+ =

30. • February 14, 2017 @ 2:00 PM ET/11:00 AM PT
• Sign up here:
– www.datablueprint.com/webinar-schedule or www.dataversity.net
• Data architecture is foundational to an information-based operational
environment. Without proper structure and efficiency in organization,
data assets cannot be utilized to their full potential, which in turn harms bottom-
line business value. When designed well and used effectively, however, a strong
data architecture can be referenced to inform, clarify, understand, and resolve
aspects of a variety of business problems commonly encountered in
organizations.
• The goal of this webinar is not to instruct you in being an outright data architect,
but rather to enable you to envision a number of uses for data architectures that
will maximize your organization’s competitive advantage. With that being said,
we will:
– Discuss data architecture’s guiding principles and best practices
– Demonstrate how to utilize data architecture to address a broad variety of organizational challenges
and support your overall business strategy
– Illustrate how best to understand foundational data architecture concepts based on the DAMA
International Guide to Data Management Body of Knowledge (DAMA DMBOK)
Data Architecture Strategies: Constructing Your Data Garden
59Copyright 2017 by Data Blueprint Slide #
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056
Copyright 2017 by Data Blueprint Slide # 60