DataEd Slides: Data Architecture vs. Data Modeling – Compare and Contrast

Data
Architecture
contrasted with
Data
Modeling
Achieving a common understanding
Copyright 2020 by Data Blueprint Slide # 1Peter Aiken, Ph.D.
• 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.
2Copyright 2020 by Data Blueprint Slide #
Peter Aiken, Ph.D.

Copyright 2020 by Data Blueprint Slide #
Data Architecture contrasted with Data Modeling
X
• Data Maps ➜ Models
– Why do we need them?
– How are they be used?
– Challenges to increased use
(social, political, economic)
• Architecture/Engineering
– Two sides of same data coin
– Must operate on standard,
shared data of known quality
• From the Top
– Means: Forward engineering
– Goal: Composition/Building
• From the Bottom
– Means: Reverse engineering
– Goal: Understanding
• Working Together
– Functions required for
effective data management
– Need for simplicity
• Take Aways/Q&A
Data ...
• As a subject is
– Complex and detailed
– Taught inconsistently, and
– Poorly understood
• Maps are necessary but
insufficient prerequisites to data architectures
– Fully leveraging data assets
• Maps are incomplete without purpose statements
– More powerful than definitions
– Remedy
• Add purpose statements
• Validate resulting model
• Maps are required to share information about data
• Data architectures are comprised of data models
– Data modeling is an engineering activity required to product data maps that are
necessary but insufficient prerequisites to leveraging data assets

The DAMA Guide to
the Data Management
Body of
Knowledge
Data
Management
Practices
fromTheDAMAGuidetotheDataManagementBodyofKnowledge©2009&2017byDAMAInternational
• Analysis
• Database Design
• Implementation
• Additional data
development
https://www.amazon.com/Infonomics-Monetize-Information-Competitive-Advantage/dp/1138090387
Data's Unique Properties
• Does not obey all of the laws of physics
– Not really visible (visualization expertise)
• Non rivalrous
– the cost of providing an additional copy is zero
• Non depleting
– Does not require replenishment
• Regenerative
• Nearly unlimited
• Low inventory and transportation/ transmission costs
• More difficult to control and own
• Eco friendly
• Impossible to clean-up if you spill it

Data modeling
• The process of discovering, analyzing, and
scoping data requirements
– Understand what the data things are?
– What do they do?
– How do they interact?
• Representing/communicating requirements in a
precise form called a data model
– Maps of critical business assets
– Comprise and contain metadata essential to data
consumers
– Function as a kind of sheet music language
– Metadata is essential to other business functions
(definitions for governance, lineage for analytics, etc.)
• The process is iterative and may include a
conceptual, logical, and physical model
process of discovering, analyzing, and scoping data requirements
• List organizational places
• These are called Attributes
– Attributes are characteristics of "things"
• List organizational places that need to be
persons
places
things
created
read
updated
deleted
archived

• An organization might decide to
characterize the parts of a THING as:
– Attributes: ID, description, status,
sex.to.be.assigned, reserve.reason
• Decisions to manage information
about each specific attribute has
direct consequences
– A decision to use the above data
attributes permits the organization to
determine if it has female THINGs are available to be reserved
• Characteristics can be shared
– All THINGs may have a status
– Many THINGs can be assigned to females
• Characteristics may be required to be unique
– ID permits identification every THING as distinct for every other THING
– Description is likely to be unique for each THING
THING
Thing.Id #
Thing.Description
Thing.Status
Thing.Sex.To.Be.Assigned
Thing.Reserve.Reason
Attributes arranged into an
entity named "thing" – the
attribute Thing.Id is the means
used to identify a unique
occurrence of thing
Data modeling
consumers

representing and communicating these in a precise form called a data model
Thing.Id #
Thing.Description
Thing.Status
Thing 1
Thing.Id #
…
Thing 2
Each THING 2 must be accompanied by a THING 1
representing/communicating requirements in a precise form called a data model
• Defines mandatory/optional
relationships between using
minimum/maximum
occurrences from one entity
to another

Data modeling
consumers
!

!

!

!

Organizational Needs
become instantiated
and integrated into a
Data Models
Informa(on)System)
Requirements
authorizes and
articulates
satisfyspecificorganizationalneeds
The process is iterative Data Models are Developed in
Response to Needs

Data modeling
consumers
CONCEPTUAL, LOGICAL, and PHYSICAL model
ANSI-SPARC 3-Layer Schema
1. CONCEPTUAL - Allows independent
customized user views:
– Each should be able to access the same
data, but have a different customized view
of the data.
2. LOGICAL - This hides the physical
storage details from users:
– Users should not have to deal with
physical database storage details. They
should be allowed to work with the data
itself, without concern for how it is
physically stored.
3. PHYSICAL - The database administrator
should be able to change the database
storage structures without affecting the
users’ views:
– Changes to the structure of an
organization's data will be required. The
internal structure of the database should
be unaffected by changes to the physical
aspects of the storage.
For example, a changeover to a new
DBMS technology. The database
administrator should be able to change
the conceptual or global structure of the
database without affecting the users.

Data modeling
consumers
CONCEPTUAL, LOGICAL, and PHYSICAL model
Each data arrangement is a data structure
• Some data structure characteristics
– Grammar for data objects
• Grammar is the principles
or rules of an art, science,
or technique "a grammar
of the theater"
– Constraints for data
objects
– Sequential order
– Uniqueness
– Order
• Hierarchical, relational,
network, lake, other
– Balance
– Optimality
http://www.nist.gov/dads/HTML/datastructur.html
"An organization of information, usually in memory, for better algorithm efficiency,
such as queue, stack, linked list, heap, dictionary, and tree, or conceptual unity,
such as the name and address of a person. It may include redundant
information, such as length of the list or number of nodes in a subtree."

Hierarchy
• A hierarchy is an
arrangement of items
(objects, names, values,
categories, etc.) in which
the items are represented
as being "above", "below",
or "at the same level as"
one another.
• Hierarchy is an important
concept in a wide variety of
fields, such as philosophy,
mathematics, computer
science, organizational
theory, systems theory, and
the social sciences
(especially political
philosophy).
Data Architecture
Data Maps
/Models
Mess
Data Maps
/Models
Data Maps
/Models
Data Maps
/Models
Data Maps
/Models
Model View
Social

Differences between Programs and Projects
• Programs are Ongoing, Projects End
– Managing a program involves long term strategic planning and
continuous process improvement is not required of a project
• Programs are Tied to the Financial Calendar
– Program managers are often responsible for delivering
results tied to the organization's financial calendar
• Program Management is Governance Intensive
– Programs are governed by a senior board that provides direction,
oversight, and control while projects tend to be less governance-intensive
• Programs Have Greater Scope of Financial Management
– Projects typically have a straight-forward budget and project financial management
is focused on spending to budget while program planning, management and
control is significantly more complex
• Program Change Management is an Executive Leadership
Capability
– Projects employ a formal change management process while at the program level,
change management requires executive leadership skills and program change is
driven more by an organization's strategy and is subject to market conditions and
changing business goals
Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management
Your data program
must last at least as
long as your HR
program!
What do we teach knowledge workers about data?
What percentage of the deal with it daily?

Political
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

If the only tool you
know is a hammer
you tend to see
every problem as a
nail (slightly reworded
from Abraham Maslow)
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

Economic
Tacoma Narrows Bridge/
Gallopin' Gertie

• World's 3rd longest suspension span
• Slender, elegant and graceful
• Opened on July 1st, collapsed in a windstorm on 7 Nov1940
• "The most dramatic failure in
bridge engineering history"
• Changed forever how engineers
design suspension bridges leading
to safer spans today.
Tacoma Narrows Bridge/Gallopin' Gertie
Similarly data failures cost organizations
minimally 20-40% of their IT budget

Data is a hidden IT Expense
• Organizations spend between 20 -
40% of their IT budget evolving
their data - including:
– Data migration
• Changing the location from one place to
another
– Data conversion
• Changing data into another form, state, or
product
– Data improving
• Inspecting and manipulating, or re-keying data
to prepare it for subsequent use
– Source: John Zachman
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
Doing a poor job with data
• Takes longer
• Costs more
• Delivers less
• Presents greater risk (with thanks to Tom DeMarco)

X
• From the Top
• From the Bottom
• Take Aways/Q&A
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
Application
domain 1
Program A
Program C
Program B
DataData
DataData
Data
Data
Data
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
Data
Data
Data
Data Architecture Focus has Greater Potential 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

The DAMA Guide to
the Data Management
Body of
Knowledge
Data
Management
Practices
fromTheDAMAGuidetotheDataManagementBodyofKnowledge©2009byDAMAInternational
• Enterprise data modeling
• Value chain analysis
• Related data architecture
How are components expressed as architectures?
• Details are
organized into
larger components
• Larger
components are
organized into
models
• Models are
organized into
architectures
(comprised of
architectural
components)
A B
C D
A B
C D
A
D
C
B
Intricate
Dependencies
Purposefulness

How are data structures expressed as architectures?
• Attributes are organized into entities/objects
– Entitles/objects are "things" whose
information is managed in support of strategy
– Example(s)
• Entities/objects are organized into models
– Combinations of attributes and entities are
structured to represent information requirements
– Poorly structured data, constrains organizational
information delivery capabilities
– Example(s)
• Models are organized into architectures
– When building new systems, architectures are used to plan development
– More often, data managers do not know what existing architectures are and -
therefore - cannot make use of them in support of strategy implementation
– Why no examples?
Intricate
Dependencies
Purposefulness
THING
Thing.Id #
Thing.Description
Thing.Status
Data architectures are comprised of data models

Architecture is about ...
• Things
– (components)
• The functions of the things
– (individually)
• How the things interact
– (as a system,
– towards a goal)
• Business
• Process
• Systems
• Security
• Technical
• Data / Information
4 Minute Architecture Lesson from Steve Jobs, Introducing iCloud

Typically Managed Architectures
• Business Architecture
– Goals, strategies, roles, organizational structure, location(s)
• Process Architecture
– Arrangement of inputs -> transformations = value -> outputs
– Typical elements: Functions, activities, workflow, events, cycles, products, procedures
• Systems Architecture
– Applications, software components, interfaces, projects
• Security Architecture
– Arrangement of security controls relation to IT Architecture
• Technical Architecture/Tarchitecture
– Relation of software capabilities/technology stack
– Structure of the technology infrastructure of an enterprise, solution or system
– Typical elements: Networks, hardware, software platforms, standards/protocols
• Data/Information Architecture
– Arrangement of data assets supporting organizational strategy
– Typical elements: specifications expressed as entities, relationships, attributes,
definitions, values, vocabularies
1 in 10 organizations manage 1
or more of the formally
Data Architectures: here, whether you like it or not
deviantart.com
• All organizations
have data
architectures
– Some are better
understood and
documented (and
therefore more
useful to the
organization) than
others Business
Process
Systems
Security
Technical
Data/Information

Data
Data
Data
Information
Fact Meaning
Request
A model defining 3 important concepts comprising a data architecture
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
Data
Data
Data Data
“You can have data without information, but
you cannot have information without data”
— Daniel Keys Moran, Science Fiction Writer
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING.
5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES.
6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.
Wisdom & knowledge are
often used synonymously
Useful Data
Data Architectures Determine
Interoperability
• Required to enable
self-correction/generation
capabilities
• Permits governance of data as an
asset
• Prerequisite to meaningful data
exchanges
• Lowers costs of organization-wide
and extra-organizational data
sharing
• Permits managed evolution - rapidly
responding to changing needs, new
partners, time criticality's
• Required for (full) role-based
security implementation
• Decreases the cost of maintaining
data inventories
Data Architectures
• Capture the business meaning of the
data required to run the organization
• Living document – constantly
evolving to meet upcoming and
discovered business requirements
• A potential entry point for
architecture engagements
• Validated data architectural
components can be used to
populate a business glossary
• Major collection of metadata

Levels of Abstraction, Completeness and Utility
• Models more downward facing - detail
• Architecture is higher level of abstraction - integration
• In the past architecture attempted to gain complete (perfect)
understanding
– Not timely
– Not feasible
• Focus instead on
architectural components
– Governed by a framework
– More immediate utility
• http://www.architecturalcomponentsinc.com
Data structures organized into an Architecture
• How do data structures support strategy?
• Consider the opposite question?
– Were your systems explicitly designed to be
integrated or otherwise work together?
– If not then what is the likelihood that they will
work well together?
– In all likelihood your organization is spending
between 20-40% of its IT budget compensating
for poor data structure integration
– They cannot be helpful as long as their
structure is unknown
• Two answers/two separate strategies
– Achieving efficiency and
effectiveness goals
– Providing organizational dexterity
for rapid implementation

Engineering
Architecture
Engineering/Architecting
Relationship
• Architecting is used to
create and build systems too
complex to be treated by
engineering analysis alone
– Require technical details as the
exception
• Engineers develop the
technical designs for
implementation
– Engineering/Crafts-persons
deliver work product
components supervised by:
• Manufacturer
• Building Contractor
You cannot architect after implementation!

USS Midway
& Pancakes
What is this?
• It is tall
• It has a clutch
• It was built in 1942
• It is cemented to the floor
• It is still in regular use!
Definition of Bed

• An organization might decide to
characterize the parts of a BED as:
– Attributes: ID, description, status,
sex.to.be.assigned, reserve.reason
• Decisions to manage information
about each specific attribute has
direct consequences
– A decision to use the above data
attributes permits the organization to
determine if it has female beds are available to be reserved
• Characteristics can be shared
– All beds may have a status
– Many beds can be assigned to females
• Characteristics may be required to be unique
– ID permits identification every bed as distinct for every other bed
– Description is unlikely to be the same for each bed
BED
Bed.Id #
Bed.Description
Bed.Status
Bed.Sex.To.Be.Assigned
Bed.Reserve.Reason
Attributes arranged into an
entity named "bed" – the
attribute Bed.Id is the means
used to identify a unique
occurrence of bed
Q: What is the proper relationship for these entities?
ROOMBED

Bed Room
Data Maps at the Entity Level ➜ Stored Facts
Bed Room
a BED is related to a ROOM
More precision:
many BEDS are related to many ROOMS
Bed Room
Better information:
many BEDS may be contained in each ROOM and each room may contain many beds
What if beds can
be moved?
Eventually One or Many (optional)
Eventually One (optional)
Zero, or Many (optional)
One or Many (mandatory)
Exactly One (mandatory)
Possible Entity Relationship Cardinality Options

Families of Modeling Notation Variants
Information Engineering
What is a Relationship?
• Natural associations between two or more entities

Ordinality & Cardinality
• Defines mandatory/optional relationships using minimum/
maximum occurrences from one entity to another
A BED is placed
in one and only
one ROOM A ROOM
contains zero
or more BEDS
A BED is occupied by zero or more
PATIENTS
A PATIENT
occupies at least
one or more BEDS
ROOM
BED
PATIENT
Standard definition reporting does not provide conceptual context
BED
Something you sleep in

Purpose statement incorporates motivations
Entity: BED
Data Asset Type: Principal Data Entity
Purpose: This is a substructure within the Room
substructure of the Facility Location. It contains
information about beds within rooms.
Source: Maintenance Manual for File and Table
Data (Software Version 3.0, Release 3.1)
Attributes: Bed.Description
Bed.Status
Bed.Reserve.Reason
Associations: >0-+ Room
Status: Validated
Draft
A purpose statement describing
– Why the organization is maintaining information about this business concept;
– Sources of information about it;
– A partial list of the attributes or characteristics of the entity; and
– Associations with other data items(read as "One room contains zero or many beds.")
X
• From the Top
• From the Bottom
• Take Aways/Q&A

BUILD?WHAT? HOW?
As Is Requirements
Assets WHAT?
As Is Design Assets
HOW?
As Is Implementation
Assets AS BUILT
Forward Engineering
New
Building new stuff - in this case, new databases
Systems Development Life Cycle (SDLC)
• Data management and software development
must be separated and sequenced.
64
WHAT?
HOW?
BUILD?

X
• From the Top
• From the Bottom
• Take Aways/Q&A
Data Representation is the Essence of Programming
• Mythical Man Month ➜ 9 parallel effort x 1 month each ≠ baby
• Fred Brooks Jr.'s observation
– Data representation is the essence of programming
– "Show me your flowchart and
conceal your tables, and
I shall continue to be mystified.
– Show me your tables, and
I won't usually need your flowchart;
it'll be obvious."

As Is Requirements
Assets WHAT?
As Is Design Assets
HOW?
Assets AS BUILT
Existing
Reverse Engineering
A structured technique aimed at recovering rigorous knowledge
of the existing system to leverage enhancement efforts
[Chikofsky & Cross 1990]
X
• From the Top
• From the Bottom
• Take Aways/Q&A

As Is Requirements
Assets WHAT?
As Is Design Assets
HOW?
Assets AS BUILT
ExistingNew
Reengineering
Reverse Engineering
Forward engineering
Reimplement
To Be
Implementation
Assets
To Be
Design
Assets
To Be Requirements
Assets
• First, reverse engineering the existing system to understand its strengths/weaknesses
• Next, use this information to inform the design of the new system
Data Modeling Process
1. Identify entities
2. Identify key for
each entity
3. Draw rough draft
of entity
relationship data
model
4. Identify data
attributes
5. Map data
attributes to
entities

Model evolution is good, at first ...
1. Identify entities
2. Identify key for
each entity
3. Draw rough draft
of entity
relationship data
model
4. Identify data
attributes
5. Map data
attributes to
entities
Preliminary
activities Modeling
cycles
Wrapup
activities
Evidence
collection &
analysis
Project
coordination
requirements
Target
system
analysis
Modeling
cycle
focus
Activity
Refinement
Collection
Analysis
Validation
Declining coordination requirements
Increasing amounts of targetsystem analysis
Preliminary
activities Modeling
cycles
Wrapup
activities
Evidence
collection &
analysis
Project
coordination
requirements
Target
system
analysis
Modeling
cycle
focus
Activity
Refinement
Collection
Analysis
Validation
Preliminary
activities Modeling
cycles
Wrapup
activities
Evidence
collection &
analysis
Project
coordination
requirements
Target
system
analysis
Modeling
cycle
focus
Activity
Refinement
Collection
Analysis
Validation
Preliminary
activities Modeling
cycles
Wrapup
activities
Evidence
collection &
analysis
Project
coordination
requirements
Target
system
analysis
Modeling
cycle
focus
Activity
Refinement
Collection
Analysis
Validation
Relative use of time allocated to tasks during Modeling
Preliminary
activities Modeling
cycles
Wrapup
activities
Evidence
collection &
analysis
Project
coordination
requirements
Target
system
analysis
Modeling
cycle
focus
Activity
Refinement
Collection
Analysis
Validation

X
• From the Top
• From the Bottom
• Take Aways/Q&A
How are data structures expressed as architectures?
• Attributes are organized into entities/objects
– Entitles/objects are "things" whose
information is managed in support of strategy
– Example(s)
• Entities/objects are organized into models
– Combinations of attributes and entities are
structured to represent information requirements
– Poorly structured data, constrains organizational
information delivery capabilities
– Example(s)
• Models are organized into architectures
– When building new systems, architectures are used to plan development
– More often, data managers do not know what existing architectures are and -
therefore - cannot make use of them in support of strategy implementation
– Why no examples?
Entity: BED
Attributes: Bed.Description
Bed.Status
Bed.Reserve.Reason
Intricate
Dependencies
Purposefulness

+ =
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