Data Strategy Best Practices

Data Strategy
Best Practices
© Copyright 2023 by Peter Aiken Slide # 1
peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD
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 (anythingawesome.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
$1,500,000,000.00 USD

Sean Martin, CTO
Cambridge Semantics Inc.
January 2023
Data Strategy Best Practises
Where do Knowledge Graphs fit in?

Copyright 2023. Cambridge Semantics Inc
Emerging modern data management architectures
What people say..
1
Data Mesh
● a human and product-centric approach
● enable scaling by giveing discrete teams control over how their
data is stored and managed
● experts in certain business domains are in charge of preparing
& hosting specific data sets, governance, management and
processes
● data is seen as a product (value of the integration, SLA’s etc).
● to enable centralized analytics, reporting, BI and ML efforts,
APIs are used to connect to different data sources
● In a data mesh, each business unit is responsible for creating
and maintaining its own data and sharing it with others Instead
of using a single virtual layer to manage disparate sources, a
data mesh uses multiple domains, with each domain specific to
the needs/purpose of the domain
● more completely embraces distributed data governance and
management
● bottom-up process
● automation?!
● abstracts data from the location where it is created or stored
● uses metadata to automatically connect disparate data
● provide a single view of an organization’s data estate
● understand how datasets and metadata are related
● virtualization
● the technology eliminates the need for bulk transfers of data?!
● consists of a single source of truth
● is about eliminating humans from the process as much as possible
● does not require replacement of existing infrastructure but instead
adds a layer of additional technology above the existing
infrastructure that performs metadata management and inference
● data management is unified and not the actual storage
● semantics enable the formation of a knowledge graph that
deepens the connection across datasets
● top-down control
Data Fabric
What people do seem to agree on is that the two approaches are not mutually
exclusive and that customers can combine aspects of each to best suit their needs.
OR

RDBMS/OLTP Big Data / Hadoop Document Repositories
Flat Files
Third Party
Legacy
Mart
Mart
ETL
Data Warehouse
Data Lake
XML • JSON • PDF
DOC * WEB
Traditional BI Cloud
CUSTOMERS
PRODUCTS
CLAIMS
COMPOUNDS
Anzo delivers knowledge
graph as a lightweight data
integration overlay.
2
Copyright 2023. Cambridge Semantics Inc 2

Knowledge graphs
3
Connect siloed data. Simplify complicated data.
Simplifies access to complex data to address
unanticipated questions
Quickly profiles, connects and harmonizes data
from multiple sources, including unstructured
Presents tailored views, services and
experiences to different personas with
conceptual models
Flexibly accommodates new data sources
and use cases on the fly, with minimal impact
Cambridge Semantics enables scalable OLAP
style knowledge graphs capable of accomodating
enterprise data sources and use cases

12.2 Pharmacodynamics
Treatment with glucocorticosteroids, including
UCERIS rectal foam, is associated with a
suppression of endogenous cortisol
concentrations and an impairment of the
hypothalamic-pituitary-adrenal (HPA) axis
function.
Knowledge graphs are connected graphs of data and
metadata that richly model real-world entities
Appl_No Approval_Date Applicant
205613 10/07/2014
Valeant
Pharms
212379 10/18/2019 Foamix
… … …
ApplNo ProductNo DrugName ActiveIngredient
205613 10/07/2014
Valeant
Pharms
HYDROCHLORIDE
212379 10/18/2019 Foamix BUDESONIDE
… … … …
205613
APPLNO
Drug
CONTAINS
BUDESONIDE
ACTIVEINGREDIENT
UCERIS
DRUGNAME
Product
2MG/ACTUATION
STRENGTH
ABOUT
Pharmacodynamics
glucocorticosteriods
endogenous cortisol
SUPRRESION
TREATMENT
10/07/2014
Product
APPLICANT
Valeant Pharms
Application
205613
APPL_NO
isSponsor
Product
(Canonical)
4
DRUGS@FDA - Approved
360 view of every drug submission
○ Integrates 14 source systems
○ >300,000 Drugs
○ 70+ years of historical data
○ >8 million documents

Anzo interoperates with the data ecosystem
5

Cambridge Semantics for Industry 4.0
6
Connected Data for Digital Thread & Twins
As Designed As Manufactured As Used
▪ Cambridge Semantics was chosen as knowledge graph provider by
major European manufacturer for a next generation data platform
▪ This was part of a multi-year digital transformation initiative from the
company’s board
▪ The knowledge graph is used as an internal-use platform and
external customer engagement / monetization solution
▪ Helps connect data across operations, manufacturing, etc. for 70%
efficiency gains on initial use cases; Savings >$100M
Diagnosis Claims Operations
Engineering Data ePLM
Industry 4.0
Knowledge
Graph
BOM
SAP_Materials
Drawing
Electronic
Layouts
PLC
Production
Line
Plant
Material
Product
Component
Bin
Supplier
Process
Component Vehicle
Claim
Diagnostic
MACHINES | FACTORY FLOOR
We are at the second wave of the semantic technology. The first wave
began with the semantic web. The tools have matured since then. Today
you can process large knowledge graphs on reasonable hardware.
In the past, you had a graph database and some semantic modeling tool,
and you had to cobble that together. But now that is offered in a platform
fashion by the vendors. You have a lot of capabilities – like connections to
the data sources, modeling, mapping and blending tools, and last but not
least the analytics on top of the data.
It is a huge step forward.
-Customer speaking on a panel of
Cambridge Semantics customers at
The Knowledge Graph Conference
“
“

Further reading
7
https://www.amazon.com/Data-Management-Scale-Modern-Architecture-dp-1098138864/dp/1098138864/
Data Management at Scale: Modern Data
Architecture with Data Mesh and Data Fabric
2nd Edition
by Piethein Strengholt
Publisher(s): O'Reilly Media, Inc.
ISBN: 1098138864
The Rise of the Knowledge Graph
by Sean Martin, Ben Szekely, Dean Allemang
Released March 2021
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781098100391
https://info.cambridgesemantics.com/the-rise-of-the-knowledge-graph-oreilly

Bottom Line Up Front (BLUF)
• Multi-dozen+ page data strategies are less useful than the process
of creating them, especially at first
• Too much time spent writing the perfect plan is accomplished at
the expense of the equal effort required to become proficient
implementing data strategically
• Cycling through a series of improvements is a better way to think
about using data strategically than a grand plan
Context
• Strategy
– Inherently a repetitive process that can be easily improved
• Dependency
– Data strategy exists to support organizational strategy
• Evolution
– Focus on improving data capabilities
• Output
– Plans are of limited value anyway
and always discount obstacles
• Overemphasizing Technology
– People and process challenges are
95% of the problem
• Nirvana
– Q: How do I get to Carnegie Hall?
– A: Practice Practice Practice

Data Strategy Best Practices
Program
https://plusanythingawesome.com
• 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
© Copyright 2023 by Peter Aiken Slide #
https://ctb.ku.edu/en/table-of-contents/structure/strategic-planning/develop-strategies/main 6

https://www.cio.com/article/3088208/leadership-management/how-to-avoid-a-digital-strategy-failure.html
What
Simon Sinek: How great leaders inspire action
• What motivates people
– is not what you do,
– It is why you do it
(for example)
• Rev. Martin Luther King Jr.
gave the
– "I have a dream speech"
(not the)
– "I have a plan speech"
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
How
Why
Why
Strategy
must provide
the

What is Strategy?
• Current use derived from military
- a pattern in a stream of decisions
[Henry Mintzberg]
A thing
Every Day
Low Price
Former Walmart Business Strategy

Wayne
Gretzky’s
Strategy
He skates to where he
thinks the puck will be ...
11
Strategy in Action: Napoleon faces 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”

Supply Line Metadata
(as part of a divide and conquer strategy)
First Divide

Then Conquer
Complex Strategy
• First
– Hit both armies hard
at just the right spot
• Then
– Turn right and
defeat the Prussians
• Then
– Turn left and defeat
the British
W
hile someone is shooting at you!

Contextually Important Strategy Example 1
Good Guys
(Us)
Bad Guys
(Them)
17
Good Guys
(Us)
Bad Guys
(Them)
18

Good Guys
(Us)
Bad Guys
(Them)
19
A pattern
in a stream
of decisions
20
Strategy Guides Workgroup Activities

Strategy that winds up only on a shelf is not useful
Data
Strategy
21
General Dwight D. Eisenhower
“In preparing for battle I have always found that plans
are useless, but planning is indispensable …”
https://quoteinvestigator.com/2017/11/18/planning/
–
“In preparing for battle I have always found that plans
are useless, but planning is indispensable …”
https://quoteinvestigator.com/2017/11/18/planning/

Mike Tyson
“Everybody has
a plan until they
get punched in
the face.”
http://f--f.info/?p=23071
Your Data Strategy
• Highest level data guidance
available ...
• Focusing data activities on
business-goal
achievement ...
• Providing guidance when
faced with a stream of
decisions or uncertainties
• Data strategy most usefully
articulates how data can
be best used to support
organizational strategy
• This usually involves a
balance of remediation and
proactive measures

Data Strategy Measures
• Effectiveness
– Over time
• Volume (length)
– Should be not a whole lot longer than the organizational strategy
https://www.gartner.com/en/webinars/3994588/the-art-of-the-1-page-strategy-storytelling-enables-business-gro
• Versions
– Should be sequential (with score keeping)
• Understanding
– Common agreement can be measured
Information Management Strategy On A Page
Statement of Information Management Strategy: Shift the focus of IT investment and skills toward
information management with the goal of providing employees with attainable and useful information and
boosting their capability to exploit that information for competitive advantage.
State of IM in 20XX Top 5-7 IM Initiatives
1. Launch an information management and analytics
center of excellence.
2. Redesign IT's opportunity-identification process to make it
proactive and informed by observation of distinct employee
segments.
3. Identify analytic capabilities used by employees and offer a
portfolio of tools to meet those needs.
4. Develop and hire usability and interface design skills in IT.
5. Coach employees to boost their analytic capability and
foster informed skepticism.
6. Harmonize and integrate a small subset of information
subjects where there is greatest enterprise need.
Top 5-7 Underlying Beliefs and Assumptions
1. The number of opportunities to drive growth through
information management will equal or outstrip the
opportunities for process automation.
2. Many of our employees lack the skills and judgment to use
information effectively for decision making.
3. Not all information needs to be harmonized or integrated at
enterprise level. Similarly, some information needs higher
levels of quality than others.
4. Our business partners will take the lead in information
stewardship.
5. Employee reliance on external information sources and on
unstructured information will continue to rise.
Top 5-7 Metrics Describing
the Initial State
•Percentage of IT budget
devoted to information and
analytics projects = 23%
•Percentage of budget spent
on employee capability < 5%
•Percentage of information
subjects targeted for
harmonization and
integration > 80%
•Target number of analytic
tools = 1-3
•Percentage of employees
who are informed skeptics =
38%
Top 5-7 Metrics Describing
the End State
•Percentage of IT budget
devoted to information and
analytics projects = 40-50%
•Percentage of budget spent
on employee capability > 10%
•Percentage of information
subjects targeted for
harmonization and integration
< 40%
•Target number of analytic
tools = 8-12
•Percentage of employees
who are informed skeptics =
59%
Example courtesy of Dr. Chris Bradley - chris.bradley@dmadvisors.co.uk
State of IM in 20YY

• Plan the entire
process before
beginning
– One attempt
• Use iterative strategy cycles
– Incorporate corrective feedback on initial assumptions
Planning Options
Note: Numerous upfront
assumptions are required
because plans must be detailed,
specifying end objectives
27
Strategy
Cycle
Strategy
Cycle
Strategy
Cycle
Strategy
Cycle
Over time increase capacity and improve operations
Focus evolves from reactive to proactive
Strategy helps your data program
Strategy
Cycle

Other recent data "strategies"
• Big Data
• Data Science
• Analytics
• SAP
• Microsoft
• Google
• AWS
• ...
These are technologies!
Recap
• A data strategy
specifies how data
assets are to be used
to support strategy
• Strategy evolves periodically
Strategic
Focus
A pattern in a stream of decisions
Constraints limiting goal
achievement

Program
• Q&A
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

An elevator pitch, elevator speech,
or elevator statement is a short
description of an idea, product, or
company that explains the concept in a
way such that any listener can
understand it in a short period of time.
(Wikipedia)
Managing
What is Data Governance?
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
34
Guidance
with
Data

What is Data Governance?
Managing
Data
Decisions with
Guidance
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
35
Data Assets Win!
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole (only)
– 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
2020 American Airlines market value ~ $6b
AAdvantage valued between $19.5-$31.5
2020 United market value ~ 9$b
MileagePlus ~ $22b
https://www.forbes.com/sites/advisor/2020/07/15/how-airlines-make-billions-from-monetizing-frequent-flyer-programs/?sh=66da87a614e9
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 √ √ √ √
36
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!

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 at getting back to zero
You must address data debt
• Slows progress
• Decreases quality
• Increases costs
https://www.merkleinc.com/blog/are-you-buried-alive-data-debt
https://johnladley.com/a-bit-more-on-data-debt/
https://uk.nttdataservices.com/en/blog/2020/february/how-to-get-rid-of-your-data-debt

Separating the Wheat from the Chaff
Is well organized data worth more?

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

• Better organized data 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
Who is uniquely qualified to accomplish this?

Data Strategy and Governance in Strategic Context
Data asset support for
What the data assets do to
better 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
better support strategy
How well the data strategy is working
Organizational
Strategy
Data
Governance
Data Strategy
Data
Stewards
What is the most
effective use of steward
investments?
(Metadata/
Business Goals)
Progress,
plans, problems

Program
• Q&A
• Benefits & Success Criteria
• Capability Targets
• Solution Architecture
• Organizational Development
Solution
• Organization Mission
• Strategy & Objectives
• Organizational Structures
• Performance Measures
Business Needs
Business
Needs
Data Strategy Framework (Part 1)
x

Business Needs
Business
Needs
• Organizational / Readiness
• Business Processes
• Data Management Practices
• Data Assets
• Technology Assets
Current State
• Business Value Targets
• Tactics
• Data Strategy Vision
Strategic Data Imperatives
Existing
Capabilities
Execution
Data Strategy is Implemented in 2 Phases
Data Strategy
What the
data assets do to
support strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determine 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)

Data Strategy
What the
data assets do to
support strategy
CIOs
aren't 52

Chief Data Officer
Combat
Recasting the executive team. make full use of the most valuable assets
54

Chief Data Officer
Combat
Recasting the executive team. make full use of the most valuable assets
Credit: Image credit: Matt Vickers
https://plusanythingawesome.com 55
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!
58

• Download
– http://dl.acm.org/citation.cfm?doid=2888577.2893482
or
http://tinyurl.com/PeterStudy
• Download Here!
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.
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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.
Data Strategy
What the
data assets do to
support strategy

Enron
• Fortune named Enron "America's Most Innovative Company"
for six consecutive years
• Suffered the largest Chapter 11 bankruptcy in history
(up to that time)
• August 2001: $90.00 → $42.00 → $0.26
• Dynegy (several $ billion) attempted rescue
• Enron spends entire amount in 1 week
– Any person can write a check at Enron for
– Any amount of money for
– Any purchase at
– Any time ...
• Enron goes back to Dynegy for more $?
• Dynegy: What happened to the
several $ billion I gave you
last week?
• Enron:
http://en.wikipedia.org/wiki/Enron
CFO Necessary Prerequisites/Qualifications
• CPA
• CMA
• Masters of Accountancy
• Other recognized
degrees/certifications
• These are necessary but
insufficient prerequisites/
qualifications

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

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

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.
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

Data Strategy
What the
data assets do to
support strategy
Exorcising the Seven Deadly Data Sins
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
Not Understanding Data-Centric Thinking
g Data-
ng
Lacking Qualified Data
Leadership
Failing to Implementa
ProgrammaticWay to
ShareData
Not Aligning theData
Program with ITProjects
ailing to Adequately
anageExpectations
Not Sequencing Data
Not Addressing Cultural
and Change
2 3 4
5 6 7
Not Understanding Data-
Centric Thinking
Leadership
ProgrammaticWay to
ShareData
Failing to Adequately
ManageExpectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
ProgrammaticWay to
ShareData
ManageExpectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
t Understanding Data-
Centric Thinking
Leadership
ProgrammaticWay to
ShareData
ManageExpectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
acking Qualified Data
Leadership
ProgrammaticWay to
ShareData
ately
tions
Not Sequencing Data
and Change
2 3 4
6 7
Centric Thinking
Leadership
ProgrammaticWay to
ShareData
ManageExpectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
Centric Thinking
Leadership
ProgrammaticWay to
ShareData
ManageExpectations
Not Sequencing Data
and Change
1 2 3 4
5 6 7
72

Data –
Driven?
Centric?
Focused?
First?
Provocateur?
…?
What?
Does?
Any?
OF?
This?
Mean?

the Data Doctrine® (V2)
75
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.
Source: theagiledoctrine.org

Program
• Q&A
Data Strategy
2) Recruit a qualified, knowledgeable enterprise data executive
(and other qualified talent)
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

• Benefits & Success Criteria
• Solution Architecture
• Organizational Development
Solution
• Leadership & Planning
• Project Dev. & Execution
• Cultural Readiness
Road Map
Business Needs
• Organizational / Readiness
• Business Processes
• Data Management Practices
• Data Assets
• Technology Assets
Current State
• Business Value Targets
• Tactics
• Data Strategy Vision
Strategic Data Imperatives
Business
Needs
Existing
Capabilities
Execution
Business
Value
New
Capabilities
By the (other) Books
Data
Strategy
Data
Governance
Strategy
Metadata
Strategy
Data
Quality
Strategy
BI/
Warehouse
Strategy
Data
Architecture
Strategy
Master/
Reference
Data
Strategy
Document/
Content
Strategy
Database
Strategy
Data
Acquisition
Strategy
x 80

Recap
• A data strategy
specifies how data
assets are to be used to
support strategy
• Strategy evolves
periodically
– As data debt is reduced
– Determine the most
fixable/highest value
constraint
– Cycle through
– If constraint not addressed,
start over
A pattern in a stream of decisions
Constraints limiting goal
achievement
Strategy
Cycle 1
Strategy
Cycle 2
https://en.wikipedia.org/wiki/Theory_of_constraints
(TOC)
• A management paradigm that views any
manageable system as being limited in
achieving more of its goals by a small
number of constraints(Eliyahu M. Goldratt)
• There is always at least one constraint, and
TOC uses a focusing process to identify the
constraint and restructure the rest of the
organization to address it
• TOC adopts the common idiom "a chain
is no stronger than its weakest link,"
processes, organizations, etc., are
vulnerable because the weakest
component can damage or break them or
at least adversely affect the outcome
82

Each cycle has an articulated purpose
Identify the
Constraint
Exploit the
Constraint
Subordinate all
non-
Constraints
Alleviate the
Constraint
Repeat the
Process
Theory of Constraints - Generic
Identify the current constraints,
the components of the system
limiting goal realization
Make decisions
about how best
to exploit
Organizationally–work effectively (not efficiently)
Attempt to increase
capacity in light of
new system
demands and non-
constraints
Repeat until the
constraint is
eliminated
https://en.wikipedia.org/wiki/Theory_of_constraints

Data Strategy Cycle
Determine how
best to exploit
the constraint
Harvest/harness excess capacity of non-constraints
Restructure to
further increase
supporting
capacities
Repeat until data
better supports
strategy
Identify the
Constraint
Exploit the
Constraint
Subordinate all
non-
Constraints
Alleviate the
Constraint
Repeat the
Process
In your analysis of how
organization data can best
support organizational
strategy one thing is blocking
you most - identify it!
Metadata
Management
86
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
Over time increase capacity and improve operations
Focus evolves from reactive to proactive
Strategy helps your data program
Strategy
Cycle

Increasing Implementation Effectiveness
Strate
Strate
Strate

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