DataEd Slides: Data Management vs. Data Strategy

Data Management
+ Data Strategy =
Interoperability
© Copyright 2020 by Peter Aiken Slide # 1paiken@plusanythingawesome.com+1.804.382.5957 Peter Aiken, PhD
?
• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Founder, Data Blueprint (datablueprint.com)
• DAMA International (dama.org)
• CDO Society (iscdo.org)
• 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 … PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
Peter Aiken, Ph.D.
© Copyright 2020 by Peter Aiken Slide # 2https://plusanythingawesome.comhttps://plusanythingawesome.com

3
Program
Data
Management
+
Data
Strategy
• Context
– Important data properties
– Lack of correct educational focus
– Confusion: IT - Data - Business?
• 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 2020 by Peter Aiken Slide #https://plusanythingawesome.com
=
Interoperability
How much Data,
by the minute!
For the entirety of 2019, every minute
of every day:
• #love was posted 23K/minute
• Netflix streams almost 700,000
hours of video (100,000 in 2018)
• YouTube users watch 4.5 mil videos
• 180 mil emails are sent
• Instagram users post 55,000 photos
• Almost 10,000 Uber rides (1,300 in 2018)
• Tinder users swipe almost 1.5
million times (7,000 2019 Tinder matches)
• more than 18 millions texts are sent
• 2018 1.25 new cryptocurrencies are created
© Copyright 2020 by Peter Aiken Slide # 4https://plusanythingawesome.com
https://www.domo.com/learn/data-never-sleeps-7

Domo Analysis
Searches conducted by GOOGLE
0
1,250,000
2,500,000
3,750,000
5,000,000
2013 2014 2015 2016 2017 2018 2019
In modern capitalist society,
technology was, is, and always will be
an expression of the economic objectives
that direct it into action.
Global Information Storage Capacity
https://www.martinhilbert.net/worldinfocapacity-html/
Beginning
of the
digital age

Governmental Statistics
• Prose literacy
– The knowledge and skills needed to perform prose tasks (to search,
comprehend, and use continuous texts). Examples include editorials, news
stories, brochures, and instructional materials.
• Document literacy
– The knowledge and skills needed to perform document tasks (to search,
comprehend, and use non-continuous texts in various formats). Examples
include job applications, payroll forms, transportation schedules, maps, tables,
and drug or food labels.
• Quantitative literacy
– The knowledge and skills required
to perform quantitative tasks (to
identify and perform computation,
either alone or sequentially, using
numbers embedded in printed materials).
Examples include balancing a checkbook,
figuring out how much to tip, completing
an order form or determining an amount.
How Literate are we?
What is NAAL?
• a Nationally representative Assessment of English Literacy
among American Adults age 16 and older
PIAAC assesses three key competencies for 21st-century society and the global economy:
• Scale 1-500 – no statistically significant differences from 2012/14 to 2017
• Literacy: the ability to understand, use, and respond appropriately to written texts.
• Numeracy: the ability to use basic mathematical and computational skills.
• Digital Problem Solving: the ability to access and interpret information in digital
environments to perform practical tasks. Referred to as “problem-solving in technology-rich
environments (PS-TRE)” in supporting documentation and in previous publications.
https://nces.ed.gov/surveys/piaac/current_results.asp

Separating the Wheat from the Chaff
https://plusanythingawesome.com
Separating the Wheat from the Chaff
• Better organized data increases in value
• Poor data management
practices are costing
organizations much money/time/effort
• Minimally 80% of organizational data is ROT
– Redundant
– Obsolete
– Trivial
• The question is
– Which data to eliminate?
Incomplete

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!
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
© Copyright 2020 by Peter Aiken Slide #
The disks no longer rotate!
14https://plusanythingawesome.comhttps://plusanythingawesome.com

Data Governance, Data Quality,
Data Security, Analytics, Data Compliance,
Data Mashups, Business Rules (more ...)
Data
Management
(DM)
≈
2000-
Organization-wide DM coordination
Organization-wide data integration
Data stewardship, Data use
Enterprise
Data
Administration
(EDA)
≈
1990-2000
Data requirements analysis
Data modeling
Data
Administration
(DA)
≈ 1970-1990
Expanding DM Scope
DataBase Administration (DBA) ≈1950-1970
Database design
Database operation
15https://plusanythingawesome.com
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)
© Copyright 2020 by Peter Aiken Slide # 20
Important Data Properties
• 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

Second world data challenges
• 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
22
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

DataManagement
BodyofKnowledge(DMBoKV2)
Practice
Areas
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
DataManagement
Practice
Areas

DataManagement
Practice
Areas
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
DataData
DataData
Data
Data
Data
Program A
Program B
Program C
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 1
Application
domain 2Application
domain 3
Data
Data
Data
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!
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 ChainAIMDM
Data
Governance
AnalyticsTechnology
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!

1
2
3
4
5
DataManagementStrategy
DataQuality
DataGovernance
DataPlatform/Architecture
DtataOperations
2007 Maturity Levels 2012 Maturity Levels
Comparison of DM Maturity 2007-2012
State of the practice!
• 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

41
Program
Data
Management
+
Data
Strategy
• Context
• Data Management
– What is it?
• Data Strategy
• Take Aways/Q&A
=
Interoperability
• 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
• ...
undefinedtechnologies
Data Strategy in Context – THIS IS WRONG!
Organizational Strategy
IT Strategy
Data Strategy
x

IT Strategy
This is correct …
Data Strategy
What is a Strategy?
• Current use derived from military
• "a pattern in a stream of decisions" [Henry Mintzberg]
• Take what you have and make it better

Former Walmart Business Strategy
Every
Day Low
Price
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
Technologies
Capabilities
56https://plusanythingawesome.comhttps://plusanythingawesome.com

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$Areas
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
QualityData$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
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
33
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
© Copyright 2020 by Peter Aiken Slide # 59https://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
(Things that further)
Lighthouse Project Provides Focus
(OccasionstoPractice)
NeededDataSkills
(Opportunitiestoimprove)
Datausebythebusiness

Version 1
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
1X
1X
1X
Metadata
Data
Quality
Version 2
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
2X
2X
1X
Metadata

Version 3
Data
Strategy
Data
Governance
BI/
Warehouse
Reference &
Master Data
Perfecting
operations in 3
data management
practice areas
3X
3X
1X
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

67
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

Expressing Data Improvements as Business Outcomes
11 August 2020
Getting (Re)started with Data Stewardship
8 September 8, 2020
Essential Metadata Strategies
13 October 13, 2020
Getting Data Quality Right - Success Stories
10 November 2020
Necessary Prerequisites to Data Success:
Exorcising the Seven Deadly Data Sins
8 December 2020
Brought to you by:
Upcoming Events (All webinars begin @ 17:00 UTC/2:00 PM NYC)
paiken@plusanythingawesome.com +1.804.382.5957
Questions?
Thank You!
+ =

DataEd Slides: Data Management vs. Data Strategy

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DataEd Slides: Data Management vs. Data Strategy