Data-Ed Online: A Practical Approach to Data Modeling

Welcome!
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Practical Data Modeling

Date: March 13, 2012
Time: 2:00 PM ET
Presenter: Dr. Peter Aiken
Twitter: #dataed

PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2/14/2012 1
© Copyright this and previous years by Data Blueprint - all rights reserved!

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Meet Your Presenter: Dr. Peter Aiken
• Internationally recognized thought-leader in
the data management field with more than 30
years of experience
• Recipient of the 2010 International Stevens
Award
• Founding Director of Data Blueprint
(http://www.datablueprint.com)
• Associate Professor of Information Systems
at Virginia Commonwealth University
(http://vcu.edu)

• President of DAMA International (http://dama.org)
• DoD Computer Scientist, Reverse Engineering Program Manager/
Office of the Chief Information Officer
• Visiting Scientist, Software Engineering Institute/Carnegie Mellon
University
• 7 books and dozens of articles
• Experienced w/ 500+ data management practices in 20 countries
#dataed

Practical Data
Modeling

Dr. Peter Aiken: Practical Data Modeling
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2/14/2012

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Practical Data Modeling
This presentation provides you with an understanding of the data
modeling and data development components of data
management. Participants will understand how the analysis,
design, implementation, deployment, and maintenance of data
solutions should be approached in order to maximize the full
value of the enterprise data resources and activities. Architecting
in quality is imperative at this level and complements a subset of
project activities within the system development lifecycle (SDLC)
focused on defining data requirements, designing data solution
components, and implementing these components. Participants
will understand the difficulties organizations experience when
interacting with data development efforts and how to best
incorporate these efforts into specific data projects.

#dataed

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Outline
1. Data Management Overview
2. What is Data Modeling? What is Data
Development?
3. Why are they important and how do they
compare?
4. Where do they fit in as data management
components?
5. Data Modeling/Development Frameworks
6. Data/Information Architecture Building
Blocks
7. Guiding Principles & Best Practices
8. Improving Data Modeling and Data
Development within Your Organization: 7
Mistakes You Cannot Afford to Make Tweeting now:
9. Take Aways, References & Q&A #dataed


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The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data delivery
to the organization
• Organized around
several
environmental
elements

Data Management Functions

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The DAMA Guide to the Data Management Body of Knowledge

Amazon:
http://
www.amazon.com/
DAMA-Guide-
Management-
Knowledge-DAMA-
DMBOK/dp/
0977140083
Or enter the terms
"dama dm bok" at the
Amazon search
engine

Environmental Elements

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What is the CDMP?
• Certified Data Management
Professional
• DAMA International and ICCP
• Membership in a distinct group made
up of your fellow professionals
• Recognition for your specialized
knowledge in a choice of 17 specialty
areas
• Series of 3 exams
• For more information, please visit:
– http://www.dama.org/i4a/pages/
index.cfm?pageid=3399
– http://iccp.org/certification/
designations/cdmp
#dataed

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Data Management

#dataed

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Data Management
Manage data coherently.

Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration

Data Stewardship Data Development

Assign responsibilities for data.
Engineer data delivery systems.

Data Support
Operations

Maintain data availability.

#dataed

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Outline
Development?
compare?
components?
Blocks


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Summary: Data Development

#dataed from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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Data Development Definition
• Analysis, design, implementation, deployment, and
maintenance of data solutions to maximize the value of
the data resources to the enterprise
• Subset of SDLC – defining and implementing data
solution components
– Primarily databases and data structures but includes screens,
reports, interfaces
– Now is recognized to include data virtualization, portals, XML
delivery, etc.
• Example:
data
definition
language
(DDL)
#dataed

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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

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Outline
Development?
compare?
components?
Blocks


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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
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1. Formalization
o Data model documents a single,
precise definition of data
requirements and data-related
business rules

2. Communication
o Data model is a bridge to understanding data between people
with different levels and types of experience.
o Helps understand business area, existing application, or impact
of modifying an existing structure
o May also facilitate training new business and/or technical staff

3. Scope
o Data model can help explain the data concept and scope of
purchased application packages

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Data Models: Same But Different
• Models that include the same data may differ by
• Scope: Express a perspective about data in
terms of:
– Function: business view vs. application view
– Realm: process, department, division, enterprise or
industry
– Time: current state, short-term future, long-term future
• Focus:
– Conceptual view: Basic and critical concepts
– Logical view: Detailed but independent of context
– Physical view: Optimized for a specific technology/use

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Data Model Uses
• Use data models to specify the data required for
information needs
• Data flows through business processes
packaged in information products
• Data contained in these products must meet
business requirements


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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 Employee

Sales Manager Manager

Staﬀ Line
#dataed Adapted from Introduction to Data Modeling by Clive Finkelstein in Information Engineering Strategic Systems Development 1992

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Data Models and Business Rules
BR1) Zero, one, or more
Person EMPLOYEES can be associated Job
Class
with one PERSON

BR4) One or
more
BR2) Zero, one, or more
POSITIONS
EMPLOYEES can be associated
can be
Moonligh:ng

with one JOB CLASS;
associated
with one JOB
CLASS.

Job
Sharing
Employee Posi:on

BR3) Zero, one, or more EMPLOYEES can be associated with one POSITION

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Outline
Development?
compare?
4. Where do they fit in as data
management components?
Blocks


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Data Management Functions

from The DAMA Guide to the Data Management Body
#dataed of Knowledge © 2009 by DAMA International

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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Data Modeling and Design Quality Management

Analysis
Design
Build
Test
Maintain
• Implement development/test database changes
• Create and maintain test data
• Migrate and convert data
• Build and test information products
• Build and test data access services
• Validate information requirements
• Prepare for data deployment
#dataed

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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 – this is a challenge
– IE or information engineering
– IDEF1X used by DoD
– ORM or object role modeling
– UML or unified modeling language
• Data models are useful
– In stand-alone mode
– As components of a larger information architecture
#dataed

Data Architectures produce and are made up of models that are
developed in response to organizational needs

satisfy specific organizational needs
Organizational Needs

become instantiated
and integrated into an Data/Information
Architecture

authorizes and
! articulates
!
" !
" !
"
!"#$%&'($")*+,-.&)
"
/.012%.&."-,3
#dataed

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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
1. Achieving efficiency and effectiveness goals
2. Providing organizational dexterity for rapid implementation
#dataed

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How are Data Models Expressed as Architectures?
• Attributes are organized into entities/objects
– Attributes are characteristics of "things"
– Entitles/objects are "things" whose information is managed in support of
strategy
– Examples
• 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
– Examples
• 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?
#dataed

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Outline
Development?
compare?
components?
6. Data/Information Architecture Building Blocks
Mistakes You Cannot Afford to Make
9. Take Aways, References & Q&A Tweeting now:
#dataed


The Data Model Pyramid
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Source: Steve Hoberman & George McGeachie, Key Features Needed in a Data
#dataed Modeling Tool; http://www.tdan.com/view-articles/15768

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Disposition Data Map

#dataed

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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
DISCHARGE A 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

PRODUCED
BY CLASSIFICATION DATE SLIDE

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Attributes & Definitions BED
Bed.Id
#
Attributes arranged into an entity Bed.Descrip:on
named "bed" – the attribute Bed.Id
is the means used to identify a Bed.Status
unique occurrence of bed Bed.Sex.To.Be.Assigned
Bed.Reserve.Reason
Entity: BED
Data Asset Type: Principal Data Entity
Purpose: This is a substructure within the Attributes displayed in a
Room substructure of the Facility manner encouraging their
Location. It contains information reuse as perhaps in a CASE-
about beds within rooms. tool or metadata repository –
A purpose statement
Source: Maintenance Manual for File and describing why the
Table Data (Software Version organization is maintaining
3.0, Release 3.1) information about these
Attributes: Bed.Description "business things" – Sources
Bed.Status of information about it –
(A partial) List of the
Bed.Sex.To.Be.Assigned attributes or characteristics of
Bed.Reserve.Reason the entity – Associations
Associations: >0-+ Room with other data items; this is
Status: Validated read as ROOM contains zero
or more BEDS


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ANSI-SPARK 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: For example, a changeover to a new
– Changes to the structure of an DBMS technology. The database
organization's data will be required. The administrator should be able to
internal structure of the database should change the conceptual or global
be unaffected by changes to the physical structure of the database without
aspects of the storage. affecting the users.

Data Modeling is used throughout the Systems
Development Lifecycle

Analysis
Design
Build
Test
Maintain


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Outline
Development?
compare?
components?
Blocks


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Data Modeling & Development Building Blocks

ü ü ü ü ü ü ü
ü ü ü ü ü ü ü

#dataed Illustration from The DAMA Guide to the Data Management Body of Knowledge p. 37 © 2009 by DAMA International
45

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Summary: Data Development


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Goals and Principles
1. Identify and define data
requirements.
2. Design data structures and
other solutions to these
requirements.
3. Implement and maintain
solution components that meet
these requirements.
4. Ensure solution conformance to
data architecture and standards
as appropriate.
5. Ensure the integrity, security,
usability, and maintainability of
structured data assets.

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Data Modeling/Development Activities
1. Data modeling, analysis and solution
design
1) Analyze information requirements
2) Develop and maintain conceptual models
3) Develop and maintain logical models
4) Develop and maintain physical models
2. Detailed data design
1) Design physical databases
2) Design information products
3) Design data access services
4) Design data integration services

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Data Modeling/Development Activities, cont’d
3. Data model and design quality management
1) Develop data modeling and design standards
2) Review data model and database design quality
3) Manage data model versioning and integration
4. Data implementation
1) Implement development/test
database changes
1) Create and maintain test data
2) Migrate and convert data
3) Build and test information products
4) Build and test data access services
5) Validate information requirements
6) Prepare for data deployment

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Primary Deliverables
• Data Requirements and
Business Rules
• Conceptual Data Models
• Logical Data Models and
Specifications
• Physical Data Models and
Specifications
• Meta-data (Business and
Technical) • Version Controlled Data
Models
• Data Modeling and DB design
Standards • Test Data
• Data Model and DB Design • Development and Test
Reviews Databases
• Data Integration Services • Information Products
• Data Access Services • Migrated and Converted Data

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Primary Deliverables become Reference Material


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Data Modeling/Dev. Roles & Responsibilities
Suppliers: Consumers:
• Data Stewards and SMEs • Data Producers
• IT Steering committee • Knowledge Workers
• Data Governance Council • Managers and Executives
• Data Architects and Analysts • Customers
• Software Developers • Data Professionals
• Data Producers • Other IT Professionals
• Information Consumers

Participants:
• Data Stewards and SMEs
• Data Architects and Analysts
• Database Administrators
• Data Model Administrators
• Software Developers
• Project Managers
• DM Executives and other IT Management

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Data Modeling/Development Technology

Testing Tools
Data Profiling Tools
Data Modeling Tools
Office Productivity Tools
Model Management Tools
Software Development Tools
Database Management Systems
Configuration Management Tools

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Outline
Development?
compare?
components?
Blocks


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Guiding Principles
1. Data development activities are
an integral part of the software
development lifecycle (SDLC).
2. Data modeling is an essential
technique for effective data
management and system design.
3. Conceptual and logical data modeling express business and
application requirements, while physical data modeling
represents solution design.
4. Data modeling and database design balances tradeoffs and
needs.
5. Data professionals should collaborate with other project team
members to design information products and data access and
integration interfaces.

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Guiding Principles, cont’d
6. Data modeling and database
design should follow documented
standards
7. Design reviews should review all
data models and designs, in order
to ensure they meet business
requirements and follow design
standards.
6. Data models represent valuable knowledge resources
(metadata). Carefully manage and control them through
library, configuration, and change management to ensure
data model quality and availability.
7. DBAs and other data professionals play important roles in the
construction, testing, and deployment of databases and
related application systems.

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Outline
Development?
compare?
components?
Blocks
Development within Your Organization:
7 Mistakes You Cannot Afford to Make Tweeting now:


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7 Mistakes You Can’t Afford to Make
Enterprise Data Modeling
Source: Karen Lopez, InfoAdvisors; @datachick
1. Forgetting that an enterprise architecture is a
living framework
• Traceability is key to realizing the benefits of an
enterprise data management program: Any team
member should be able to trace a business concept
from the logical model to the physical model to the
physical implementation of that concept
2. Keeping data models invisible
• In order to deliver business value, a data
management effort must be accessible,
understandable and shareable.
• Models need to be available in an easily searchable
manner.
Source: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2


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7 Mistakes You Can’t Afford to Make
3. Assuming that business users can’t understand or
review models
• Business users need to be able to access and digest data
models so they can make informed business decisions
• It is key to give them data model viewing and reporting
capabilities
• Remember: business users who see models regularly are
more likely to support the allocation of resources to future
efforts
4. Thinking that data models are only about databases
• Both logical and physical models support more than just
databases
• Allowing team members to import/export metadata
contributes to a model-driven design environment and
establishes integration of model metadata with other
#dataed platforms


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7 Mistakes You Can’t Afford to Make, cont’d
5. Throwing models “over the wall”
• A modeler is the mediator between business
requirements and physical implementations
• He/She should be involved in how requirements are
captured as well as implemented
6. Forgetting about the sizzle
• One of the main benefits of effective enterprise data
management is better communication
• Models should be interesting and the successful data
modeler must never underestimate the value of sizzle
• Presentations of models must be clear and
understandable
• Adding color and diagramming objects customizes
models and allows for a more engaging and enjoyable
user review process
#dataed


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7 Mistakes You Can’t Afford to Make, cont’d
7. Thinking of them as “your” models
• Most critical mistake is treating data models as if
the modeler personally owns them
• Models belong to the business and are tended to
by the modelers. This means:
• Share them openly
• Provide access to those who want it
• Keep extra printouts available
• Offer training on how to read them
• Make every effort to make them clear and understandable

Treating models as technical specifications that are understood
only by developers and DBAs will not provide the benefits of an
enterprise architecture
#dataed


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Outline
Development?
compare?
components?
Blocks


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References


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Questions?

+ =

It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.


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