Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Catwalk

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

3Copyright 2017 by Data Blueprint Slide #
Tweeting now:
#dataed
• Data Management Overview
• Motivation
– Systems/components
– Pervasive, yet not well understood
• Why data modeling & what is it?
– Model represents our understanding
– of the fundamental and foundational aspects of the
system
• Strategies
– The power of the purpose statement
– Understanding how to contribute to organizational
challenges beyond traditional data modeling
– Guiding problem analyses using data analysis
– Using data modeling in conjunction with
architecture/engineering techniques
– How to utilize data modeling in support of business
strategy
• Take Aways, References & Q&A
Data Modeling Strategies: Getting your data ready for the catwalk
 
 
 
UsesUsesReuses
What is data management?
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
Specialized Team Skills
Data Governance
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 practices connect
data sources and uses in an
organized and efficient manner
• Engineering
• Storage
• Delivery
• Governance
When executed,  
engineering, storage, and  
delivery implement governance
Note: does not well-depict data reuse

What is data management?
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
 
Resources 
(optimized for reuse) 
Data Governance
AnalyticInsight
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
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

Maslow's Hierarchiy of Needs
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
Copyright 2017 by Data Blueprint Slide # 8

Data Management
Body of
Knowledge
Data
Management
Functions
DAMA DM
BoK: Data
Development
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Tweeting now:
#dataed
• Motivation
system
• Strategies
strategy
Simon Sinek: How great leaders inspire action
 
 
 
 
 
WHY
HOW
WHAT“…it’s not what you do,
it’s why you do it”
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html

What is a system?
• A set of detailed methods, procedures, and routines established or
formulated to carry out a specific activity, perform a duty, or solve a problem.
• An organized, purposeful structure regarded as a whole and consisting of
interrelated and interdependent elements (components, entities, factors,
members, parts etc.). These elements continually influence one another
(directly or indirectly) to maintain their activity and the existence of the
system, in order to achieve the goal of the system.  
http://www.businessdictionary.com/definition/system.html#ixzz23T7LyAjJ
System
DataHardwareProcessesPeople Software
There will never
be less data
than right now!

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 we are migrating databases, we are not creating new
databases and we don't need organizational data
management knowledge, skills, and abilities (KSAs).
• If we are implementing a new software package, we are
not creating a new database and therefore we do not
need data management KSAs.
• If we are installing an enterprise resource package
(ERP), we are not creating a new database and therefore
we do not need data management KSAs.

Why Modeling
• Would you build a house without an
architecture sketch?
• Model is the sketch of the system to be
built in a project.
• Would you like to have an estimate how
much your new house is going to cost?
• Your model gives you a very good idea of
how demanding the implementation work
is going to be!
• If you hired a set of constructors from all
over the world to build your house, would
you like them to have a common
language?
• Model is the common language for the
project team.
• Would you like to verify the proposals of
the construction team before the work gets
started?
• Models can be reviewed before thousands
of hours of implementation work will be
done.
• If it was a great house, would you like to
build something rather similar again, in
another place?
• It is possible to implement the system to
various platforms using the same model.
• Would you drill into a wall of your house
without a map of the plumbing and electric
lines?
• Models document the system built in a
project. This makes life easier for the
support and maintenance!
Use Models to
• Store and formalize information
• Filter out extraneous detail
• Define an essential set of  
information
• Help understand complex system behavior
• Gain information from the process of developing and
interacting with the model
• Evaluate various scenarios or other outcomes indicated by
the model
• Monitor and predict system responses to changing
environmental conditions

• Goal must be shared IT/business understanding
– No disagreements = insufficient communication
• Data sharing/exchange is largely and highly automated and  
thus dependent on successful engineering
– It is critical to engineer a sound foundation of data modeling basics  
(the essence) on which to build advantageous data technologies
• Modeling characteristics change over the course of analysis
– Different model instances may be useful to different analytical problems
• Incorporate motivation (purpose statements) in all modeling
– Modeling is a problem defining as well as a problem solving activity - both are inherent to
architecture
• Use of modeling is much more important than selection of a specific modeling method
• Models are often living documents
– It easily adapts to change
• Models must have modern access/interface/search technologies
– Models need to be available in an easily searchable manner
• Utility is paramount
– Adding color and diagramming objects customizes models and allows for a more engaging and
enjoyable user review process
Data Modeling for Business Value
Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2
Typical focus of a
database modeling effort
Data Modeling Ensures Interoperability
Program F
Program E
Program D
Program G
Program H
Application
domain 2Application
domain 3
Program I
Typical focus of a
software engineering effort
Program A

DataModel
DataModel
DataModel
DataModel
DataModel
DataModel
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
DataModel
DataModel
DataModel
Data Model Focus has Great Potential Business Value
• How are decisions
about the range and
scope of common data
usage, made?
• Analysis scope is on
use of data to support a
process
• Problems caused by
data exchange or
interface problems
• Goals often connect
strategic and
operational
• One data model is ideal
DataModel
Program A
Primary Deliverables become Reference Material

Data Modeling Definition
• Modeling = Analysis and design
method used to
– Define and analyze data requirements
– Design data structures that support these
requirements
• Model = set of data specifications
and related diagrams that reflect
requirements and designs
– Representation of something in our
environment
– Employs standardized text/symbols to
represent data attributes (grouped into
data elements) and the relationships
among them
– Integrated collection of specifications and
related diagrams that represent data
requirements and design
Data Modeling and Data Architecture
• Data modeling is used to articulate data  
architecture components
• Data architectures are comprised of  
components – usually expressed as models
• Styles of data modeling exist
– IE or information engineering
– IDEF1X used by DoD
– ORM or object role modeling
– UML or unified modeling language
• Challenging - doctrinal arguments are unproductive
• Data models are useful
– In stand-alone mode
– As components of a larger architecture

Tweeting now:
#dataed
• Motivation
system
• Strategies
strategy
• Models
– are usually for the  
purpose of  
understanding
• Can be
– Equations
– Simulations  
including video games
– Physical models
– Mental models
Models as an Aid to Understanding

What is a model?
draw
critique
test
dialog
select
decide
filter
summarize
design
rank
review cluster
generate evaluate
list
visible to
participants
Structure for
organizing things
Framework for
decision making
Requires tools for problem solving and
decision making
Easy to review and
validate
graphic
text
Prototype and mockupFramework for understanding and design
Source: Ellen Gottesdiener www.ebgconsulting.com
As Is Information 
Requirements 
Assets
As Is Data Design Assets As Is Data Implementation  
Assets
ExistingNew
Modeling in Various Contexts
O2 Recreate 
Data Design
Reverse Engineering
Forward engineering
O5 Reconstitute 
Requirements
O9
Reimplement
Data
To Be Data  
Implementation  
Assets
O8  
Redesign 
Data
O4 
Recon- 
stitute 
Data  
Design
O3 Recreate 
Requirements
O6
Redesign
Data
To Be 
Design  
Assets
O7 Re- 
develop 
Require- 
ments
To Be
Requirements
Assets
O1 Recreate Data 
Implementation
Metadata

Copyright 2013 by Data Blueprint
Information Architecture Component Reengineering Options
O-1 data implementation (e.g., by recreating descriptions of implemented file layouts);
O-2 data designs (e.g., by recreating the logical system design layouts); or
O-3 information requirements (e.g., by recreating existing system specifications and
business rules).
O-4 data design assets by examining the existing data implementation (when
appropriate O-1 can facilitate O-4); and
O-5 system information requirements by reverse engineering the data design O-4.
(Note: if the data design doesn't exist O-4 must precede O-5.)
O-6 transforming as is data design assets, yielding improved to be data designs that
are based on reconstituted data design assets produced by O-2 or O-4 and
(possibly O-1);
O-7 transforming as is system requirements into to be system requirements that are
based on reconstituted system requirements produced by O-3 or O-5 and
(possibly O-2);
O-8 redesigning to be data design assets using the to be system requirements based
on reconstituted system requirements produced by O-7; and
O-9 re-implementing system data based on data redesigns produced by O-6 or O-8.
29
Don’t Tell Them You Are Modeling!
• Just write some stuff down
• Then arrange it
• Then make some appropriate
connections between your
objects

Bed 
Entity: BED
Purpose: This is a substructure within the room 
substructure of the facility location. It  
contains information about beds within rooms.
Attributes: Bed.Description 
Bed.Status 
Bed.Sex.To.Be.Assigned 
Bed.Reserve.Reason
Associations: >0-+ Room
Status: Validated
Keep them focused on data model purpose
• The reason we are locked in
this room is to:
– Mission: Understand formal
relationship between soda and
customer
• Outcome: Walk out the door with a
data model this relationship
– Mission: Understand the
characteristics that differ
between our hospital beds
• Outcome: We will walk out the door
when we identify the top three traits that
represent the brand.
– Mission: Could our systems
handle the following business
rule tomorrow?
– "Is job-sharing permitted?"
• Outcomes: Confirm that it is possible to
staff a position with multiple employees
effective tomorrow
selects and pays forgiven to
Soda
Customer
selects
can be filled by zero or 1
Employee Position
has exactly 1
How does our
perspective change:  
the primary means of
tracking a patient
Tweeting now:
#dataed
• Motivation
system
• Strategies
strategy

Entity Relationship View
C U S T O M E R
coins
soda
machine
(adapted from [Davis 1990])
Entity Relationship View
(adapted from [Davis 1990])
entity thing about which we maintain
information
object entity encapsulated with attributes
and functions
C U S T O M E R soda
machine
coin
return
deposits
selects
given to
dispenses
coins

Modeling In Support of Requirements
Person Job Class
Employee Position
BR1) Zero, one, or more
EMPLOYEES can be associated
with one PERSON
BR2) Zero, one, or more EMPLOYEES
can be associated with one POSITION
Job Sharing
Moon Lighting
Tweeting now:
#dataed
• Motivation
system
• Strategies
strategy

Data Modeling
• Modeling = complex process involving interaction
between people and with technology that don’t
compromise the integrity or security of the data
– Good data models accurately  
express and effectively communicate  
data requirements and  
quality solution design
• Modeling approach  
(guided by 2 formulas):
– Purpose + audience = deliverables
– Deliverables + resources + time = approach
Data Models Facilitate
• Formalization
– Data model documents a single,  
precise definition of data requirements  
and data-related business rules
• Communication
– Data model is a bridge to understanding data  
between people with different levels and types of experience.
– Helps understand business area, existing application, or impact of
modifying an existing structure
– May also facilitate training new business and/or technical staff
• Scope
– Data model can help explain the data concept and scope of
purchased application packages

ANSI-SPARK 3-Layer Schema
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.
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.
Conceptual Models
• Business
focused
• Entity level
• Provides focus,
scope, and
guidance to
modeling effort
• Sometimes
thrown away -
rarely maintained

Logical Models
• Required to achieve the transition  
from conceptual to physical
• Developed to the attribute level via  
3rd normal form - to a define level  
of understandability
• Logical models are developed to be  
refined to until it becomes a  
solution - sometimes purchased (as  
in EDW) always requires tailoring
• Used to guarantee the rigor of the  
data structures by formally describing the relationship between data
items in a strong fashion - more often maintained
Physical Models
• Becomes the blueprints for
physical construction of the
solution
• Blueprints are used for future
maintenance of the solution

Tweeting now:
#dataed
• Motivation
system
• Strategies
strategy
Model Evolution (better explanation)
As-is To-be
Technology
Independent/
Logical
Technology
Dependent/
Physical
abstraction
Other logical
as-is data
architecture
components

Model Evolution Framework
Conceptual Logical Physical
 
 
 
Goal
Validated
Not Validated
Every change can
be mapped to a
transformation in
this framework!
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

Standard definition reporting does not provide conceptual context
Bed
Something you sleep in
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.Sex.To.Be.Assigned 
Bed.Reserve.Reason
Associations: >0-+ Room
Status: Validated
The Power of the Purpose Statement
• 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
• Associations with other data
items; this one is read as "One
room contains zero or many
beds"

11
DISPOSITION Data Map
Data map of DISPOSITION
• At least one but possibly more system USERS enter the DISPOSITION facts into the system.
• An ADMISSION is associated with one and only one DISCHARGE.
• An ADMISSION is associated with zero or more FACILITIES.
• An ADMISSION is associated with zero or more PROVIDERS.
• An ADMISSION is associated with one or more ENCOUNTERS.
• An ENCOUNTER may be recorded by a system USER.
• An ENCOUNTER may be associated with a PROVIDER.
• An ENCOUNTER may be associated with one or more DIAGNOSES.
ADMISSION Contains information about patient admission
history related to one or more inpatient episodes
DIAGNOSIS Contains the International Disease Classification
(IDC) of code representation and/or description of a
patient's health related to an inpatient code
DISCHARGEA table of codes describing disposition types
available for an inpatient at a FACILITY
ENCOUNTER Tracking information related to inpatient
episodes
FACILITY File containing a list of all facilities in regional health
care system
PROVIDER Full name of a member of the FACILITY team
providing services to the patient
USER Any user with access to create, read, update, and
delete DISPOSITION data

Tweeting now:
#dataed
• Motivation
system
• Strategies
strategy
How do Data Models Support Organizational 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
– Achieving efficiency and effectiveness goals
– Providing organizational dexterity for rapid implementation

Design Styles – 3NF
• A mathematical data design technique founded in the early 70s by E.F.
Codd.
• Organizes data in simple  
rows and columns - Entities
• Creates connections  
between the entities called  
relationships to show how  
the data is inter-related
• 3NF removes data  
redundancies – a piece of  
data is stored only once
• 3NF is based on mathematics, give the same facts to different
modelers; the models they produce should be very similar.
• Creates a visual (Entity Relation Diagram - ERD) which may be
understood by less technical personnel
• 3NF is the modeling style most popularly used for operationally focused
data stores.
Design Styles – Dimensional
• Created and refined by Ralph  
Kimball in the 80s.
• Organizes data in Facts  
and Dimensions. Fact  
tables record the events  
(what) within the business domain  
and the Dimension tables describe  
who, when, how and where.
• The data design style was created to  
exploit the capabilities of the relational database to retrieve
and report against large volumes of data.
• Dimensional modeling sacrifices storage efficiency for
analytical processing speed
• There are 2 variations to Dimensional Modeling: Star Schema
and Snowflake

Design Styles – Data Vault
• One of the newer relational database modeling techniques
• Data Vault modeling was conceived in the 1990s by Dan
Linstedt
• Data Vault models are designed for central data
warehouses that store non-volatile, time-variant, atomic
data
• Relationships are defined through Link structures which
promote flexibility and extensibility
Tweeting now:
#dataed
• Motivation
system
• Strategies
strategy

Data Models Used to Support Strategy
• Flexible, adaptable data structures
• Cleaner, less complex code
• Ensure strategy effectiveness measurement
• Build in future capabilities
• Form/assess merger and acquisitions strategies
Employee 
Type
Employee
Sales 
Person
Manager
Manager 
Type
Staff 
Manager
Line 
Manager
Adapted from Clive Finkelstein Information Engineering Strategic Systems Development 1992
Mission and Purpose
• Develop, deliver and support products and services which
satisfy the needs of customers in markets  
where we can achieve  
a return on investment  
at least 20% annually  
within two years of  
market entry

Mission Model Analysis
Identify Potential Goals
G1.Market Analysis
G2.Market Share
G3.Innovation
G4.Customer Satisfaction
G5.Product Quality
G6.Product Development
G7.Staff Productivity
G8.Asset Growth
G9.Profitability

Mission Model Analysis
Next Step
Market
Market 
Customer
Product 
Need
Need
Customer 
Product
Market 
Need
ProductCustomer
Customer 
Need
Market 
Product

Subsequent Step for Business Value
Market
Market 
Performance
Product 
Performance
Need
Customer 
Performance
Need 
Performance
ProductCustomer
Performance
Questions?
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now!
+ =

10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056
Copyright 2017 by Data Blueprint Slide # 65

Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Catwalk

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