DataEd Slides: Data Management + Data Strategy = Interoperability

Data Management
+ Data Strategy =
Interoperability
© Copyright 2021 by Peter Aiken Slide # 1
paiken@plusanythingawesome.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 (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

3 Questions
1. If things are not designed to
work together
– What are the chances that these
things will happen to work together?
2. Data – literally the most atomic
element of the collection of
organizational data assets – is
simultaneously the
– most underutilized
– least well managed
What can be done to increase
organizational data leverage?
3. If we have not been teaching
students to design for
integration for the past 30
years, where must we look to
find expertise in these areas?
G-Army Logistic project
D
e
p
e
n
d
e
n
c
i
e
s
Purposefulness
Intricacies
4
Program
Data
Management
+
Data
Strategy
• Context
– Important data properties
– Reversing data debit
– Lack of correct educational focus
• Data Management
– What is it?
– Why is it important?
– State of the practice
– Functions required for effective data management
• Data Strategy
– Structural Approach
– Need for simplicity
– Foundational prerequisites
– The Theory of Constraints at work improving your data
• Take Aways/Q&A
– In Action In Concert = Interoperability
– Coordination is the necessary prerequisite
© Copyright 2021 by Peter Aiken Slide #
=
Interoperability

How much Data (by the minute?)
For the entirety of 2020, every
minute of every day:
• Zoom hosted 208,000+
participants
• Netflix streamed 400,000+
hours of video (697,000 in 2019)
• YouTube users uploaded 500
hours of video
• Consumers spent $1M online
($3,805 w/ mobile apps)
• LinkedIn users applied for
69,000+ jobs
• Spotify added 28 songs
• Amazon shipped 6,659
packages
https://www.domo.com/learn/data-never-sleeps-8
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 7
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 8

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]
Data Assets Win!
Organizational Data Machine
Inputs
(from Citizens)
Outputs
(to Citizens and others)
Organizational
Data Machine
(ODM)
The Data Machine

All
inputs
are
data
All
outputs
are
data
All
inputs
are
data
All
inputs
are
data
All
inputs
are
data
All
outputs
are
data
All
outputs
are
data
All
outputs
are
data
All
inputs
are
data
All
outputs
are
data
All
inputs
are
data
All
outputs
are
data
All
inputs
are
data
All
outputs
are
data
The Data Machine
How to determine what to manage formally?
Too much requires expensive and slow bureaucracy ––––––––––––––––––––– Too little misses opportunities
Interoperability is the primary value determinant
The Data Machine
The Data Matrix
Inputs
(from data machines)
Outputs
(to data machines)
Data Matrix
(n dimensions)
Process
Data machine-data can be, and often is, analyzed for purposes beyond its original
collection purpose. These unspecified, unknown, complex interactions comprise the data
matrix. Much more of this is type of data sharing is happening than most are aware.

Data Properties that should be more widely known
• General low data literacy
exists
– Even among data specialists
• Lots of data exists
– Most of it is not valuable
– The good stuff is uniquely-valuable
– Most of what exists has been
created is relatively recently
• Organizations have not cared
well for data in the past
– Two worlds exist
– Second world
data challenges
Data Debit – Getting Back to Zero
• Data debit
– 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

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

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?"
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

Put simply, organizations:
• Have little idea what data they have
• Do not know where it is (and)
• Do not know what their knowledge workers do with it
Quality data work products do not happen accidentally!
• Data management happens 'pretty well' at
the workgroup level
– Defining characteristic of a workgroup
– Without guidance, what are the chances that all
workgroups are pulling toward the same objectives?
– Consider the time spent attempting informal practices
• Data chaff becomes sand
– Preventing smooth interoperation and exchanges
– Death by 1,000 cuts that have been difficult to account for
• Organizations and individuals lack
– Knowledge
– Skills
• Data Management (how)
• Data Strategy (why)
– Pain by lots of unnecessary cuts that have been difficult to account for

21
Program
Data
Management
+
Data
Strategy
• Context
• Data Management
– What is it?
• Data Strategy
• Take Aways/Q&A
=
Interoperability
Data Management - Wikipedia Definition
Note: This is a broad definition
and encompasses professions
with no technical contact data
management technologies
such as database
management systems
"Data Resource Management is the development and execution of architectures,
policies, practices and procedures that properly manage the full data lifecycle
needs of an enterprise." http://dama.org

Misunderstanding Data Management
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

Data model focus is typically domain specific
Program A
Program C
Program B
Focus of a software engineering effort
Underutilized
data modeling
effort
Database Architecture Focus Can Vary
Application
domain 1
Program A
Program C
Program B
Focus of a software engineering effort
Underutilized
data modeling
effort
Better utilized
data modeling
effort
ERPs and COTS are marketed
as being similarly integrated!
Program F
Program E
Program G
Program H
Program I
Application
domain 2
Application
domain 3
Program D

D
a
t
a
D
a
t
a
D
a
t
a
D
a
t
a
D
a
t
a
D
a
t
a
D
a
t
a
Program A
Program B
Program C
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 1
Application
domain 2
Application
domain 3
D
a
t
a
D
a
t
a
D
a
t
a
Data Focus has Greater Potential Business Value
• Broader focus than
either software
architecture or
database
architecture
• Analysis scope is
on the system wide
use of data
• Problems caused
by data exchange
or interface
problems
• Architectural goals
more strategic than
operational
Data Management Context
• Organization wide focus
• Requirement is to "understand"
• Understanding is of both current and future needs
• Making data effective and efficient
• Leverage data to support organizational activities
Less ROT
Technologies
Process
People

"Understanding the
current and future data
needs of an enterprise
and making that data
effective and efficient in
supporting business
activities"
Aiken, P, Allen, M. D., Parker, B., Mattia, A., "Measuring Data
Management's Maturity: A Community's Self-Assessment" IEEE
Computer (research feature April 2007)
Data Management
"Understanding the
current and future data
needs of an enterprise
and making that data
effective and efficient in
supporting business
activities"
Blind Persons and the Elephant
http://www.dailymirror.lk/print/opinion/editorial-we-need-to-become-channels-of-peace/172-27164
It is like a fan!
It is like a snake!
It is like a wall!
It is like a rope!
It is like a tree!

31
Unrefined
data management
definition
Sources
Uses
Data Management
More refined
data management
definition
Sources
Reuse
Data Management
➜ ➜

Data Management
Sources
➜
Use
➜
Reuse
➜
Formal Data Reuse Management
Data
Why is data management so important?
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
Good
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
Quality In ➜ Quality Out!

Data challenges
• Data management is consumed with interoperability
• We assume all datasets to be perfect - just as in class
• We have not been teaching the skills required to undo the mess that we
were left with
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

41
Program
Data
Management
+
Data
Strategy
• Context
• Data Management
– What is it?
• Data Strategy
• Take Aways/Q&A
=
Interoperability
Strategy challenges
• Most attempt to write the world's best pop song on the very first try
– To much focus on the document
– Not enough on the processes required
• TOC Cycle scope should be correcting an interoperability challenge

Recent data "strategies"
• Data science
• Big data
• Analytics
• SAP
• Microsoft
• Google
• AWS
• ...
undefined
technologies
Data Strategy in Context – THIS IS WRONG!
Organizational Strategy
IT Strategy
Data Strategy
x 44

Organizational Strategy
IT Strategy
This is correct …
Data Strategy
45
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
Definition of Strategy
He skates to where he
thinks the puck will be ...

Strategy in Action: Napoleon defeats a larger enemy
• Question?
– How to 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

First Divide
Then Conquer

Strategy that winds up only on a shelf is not useful
Data
Strategy
Strategy
A pattern
in a stream
of decisions

Our barn had to pass a foundation inspection
• 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)
Data Management Practices Hierarchy
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
56

Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
57
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data architecture
implementation
DMM℠ Structure of
5 Integrated
DM Practice Areas
Data architecture
implementation
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
58
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
Data
Governance
Data
Quality
Platform
Architecture
Data
Operations
Data
Management
Strategy
3 3
3
3
1
Supporting
Processes
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Your data foundation
can only be as strong
as its weakest link!
Optimized
Measured
Defined
Managed
Initial

• A management paradigm that views any
manageable system as being limited in
achieving more of its goals by a small
number of constraints
• 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
https://plusanythingawesome.com https://en.wikipedia.org/wiki/Theory_of_constraints
(TOC)
Theory of Constraints - Generic
Identify the current constraints,
the components of the system
limiting goal realization
Make quick
improvements
to the constraint
using existing
resources
Review other activities in the process facilitate proper alignment and support of constraint
If the constraint
persists, identify other
actions to eliminate
the constraint
Repeat until the
constraint is
eliminated

Theory of Constraints at work improving your data
In your analysis of how
organization data can best
support organizational strategy
one thing is blocking you most -
identify it!
Try to fix it
rapidly with out
restructuring
(correct it
operationally)
Improve existing data evolution activities to ensure singular focus on the current objective
Restructure to
address constraint
Repeat until data better
supports strategy
Data Management + Data Strategy = Interoperability
Organizational
Strategy
Data Strategy
IT Projects
Organizational Operations
Data
Management
Data
asset support for
organizational
strategy
What the data
assets need to do to
support strategy
How well data is
supporting strategy
Operational
feedback
How IT
supports strategy
Other
aspects of
organizational
strategy

63
Program
Data
Management
+
Data
Strategy
• Context
• Data Management
– What is it?
• Data Strategy
• Take Aways/Q&A
=
Interoperability
• This discipline has not had 8,000 years
to formalize practices ➡ GAAP
• Your data is a mess and requires professional
ministration to make up for past neglect
• Your folks don't know how to use or improve it effectively
• You likely require a new business data program
• Data strategy and data management are major data program
components, in concert, they must focus on
1. Improving organizational data
2. Improving the way people use data
3. Improving how people use better data to support strategy
Take Aways
This can only be accomplished incrementally using an
iterative, approach focusing on one aspect at a
time and applying formal transformation methods
data program!
business

Data Architecture and Data Modeling Differences:
Achieving a common understanding
11 May 2021
Why Data Modeling
Is Fundamental
8 June 2021
Business Value through Reference &
Master Data Strategies
13 July 2021
Upcoming Events
Brought to you by:
Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
paiken@plusanythingawesome.com +1.804.382.5957
Questions?
Thank You!
+ =

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DataEd Slides: Data Management + Data Strategy = Interoperability