Unlocking Business Value with Data Modeling

TITLE
Data Modeling & Data Architecting for Business Value pt. 1
When asked why they are modeling data, many in the practice answer: "Because
that is what must be done." However, a better approach to this question is to speak
in terms that are understood in the executive suite – business results! All of our
organizations are faced with various organizational challenges that require analysis.
Building new systems is just one example. This webinar describes the use of data
modeling as a basic analysis method (one of many that good analysts should keep in
their “toolbox"). In addition, I will demonstrate various uses of data modeling to
inform, clarify, understand, and resolve aspects of a variety of business problems. As
opposed to showing how to data model, I will show you how to use data modeling to
solve business problems. The goal is for you to be able to envision a number of uses
for data modeling that will raise the perceived utility of this analysis method in the
eyes of business executives. Learning objectives include:

• Understanding how to contribute to organizational challenges beyond traditional
data modeling
• Realizing the fundamental difference between "definition" and "purpose"
• Guiding analyses through data analysis
• Using data modeling in conjunction with architecture/engineering techniques
• Understanding foundational data modeling concepts based on the Data
Management Body of Knowledge (DMBOK)
• How to utilize data modeling in support of business strategy
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1
© Copyright this and previous years by Data Blueprint - all rights reserved!

TITLE

Welcome!
Unlocking Business Value through
Data Modeling and Data Architecture Pt. 1

Date: January 8, 2013
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD


TITLE
Welcome!

Unlocking Business Value through
Data Modeling and Data Architecture Pt. 1

Date: January 8, 2013
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD


TITLE

Commonly Asked Questions

1) Will I get copies of the slides
after the event?

2) Is this being recorded so I
can view it afterwards?


TITLE

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TITLE

Your Presenter: Peter Aiken, PhD
• Internationally recognized thought-
leader in the data management field
- 30 years of experience
– Recipient of multiple international
awards
– Founder, Data Blueprint
(http://datablueprint.com)
• 7 books and dozens of articles
• Experienced w/ 500+ data
management practices in 20
countries
• Multi-year immersions with
organizations as diverse as the
US DoD, Deutsche Bank, Nokia,
Wells Fargo, the Commonwealth
of Virginia and Walmart

TITLE

Unlocking)Business)Value)
through)Data)Modeling)
and)Data)Architecture)
)
pt.)1

PRODUCED BY Unlocking Business Value through Data Modeling and Data Architecture
CLASSIFICATION DATE SLIDE
DATA BLUEPRINT10124-C W. BROAD ST, GLEN ALLEN, VA 23060
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION EDUCATION 7

TITLE


TITLE

Data Modeling for Business Value
• 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
– The more easily it adapts to change, the resource utilization
• 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
Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2

TITLE

Agenda
1. What is Data Management/DAMA/DM
BoK/CDMP?
2. Why data modeling & what is it?
3. The power of the purpose statement
4. Understanding how to contribute to
organizational challenges beyond
traditional data modeling
5. Guiding problem analyses
using data analysis
6. Using data modeling in conjunction
with architecture/engineering
techniques
7. How to utilize data modeling in Tweeting now:
support of business strategy #dataed
8. Take Aways, References & Q&A

TITLE

Data Management

#dataed

TITLE

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

Hierarchy of Data Management Practices (after Maslow)
• 5 Data
Management
Practices Areas /
Data Management
Basics
• Are necessary but
insufficient
Advanced
prerequisites to Data
organizational data Practices
• Cloud
leveraging • MDM
applications that is • Mining
• Analytics
Self Actualizing Data • Warehousing
or Advanced Data • SOA
Practices

Basic Data Management Practices
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations

http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png
- datablueprint.com 1/10/2013 © Copyright this and previous years by Data Blueprint - all rights reserved!

TITLE
Organizational DM Functions and their Inter-relationships

#dataed

TITLE
Data Management Functions
DAMA DM BoK & CDMP
• 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 (more at dama.org)
– Organized around several environmental
elements
• 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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DAMA DM BoK: Data Development

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

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Agenda
BoK/CDMP?
using data analysis
techniques

Why Modeling
• Would you build a house without an • Model is the sketch of the system to
architecture sketch? be built in a project.

• Would you like to have an estimate • Your model gives you a very good
how much your new house is going to idea of how demanding the
cost? implementation work is going to be!

• If you hired a set of constructors from • Model is the common language for the
all over the world to build your house, project team.
would you like them to have a
common language?
• Would you like to verify the proposals • Models can be reviewed before
of the construction team before the thousands of hours of implementation
work gets started? work will be done.

• If it was a great house, would you like • It is possible to implement the system
to build something rather similar again, to various platforms using the same
in another place? model.

• Would you drill into a wall of your • Models document the system built in a
house without a map of the plumbing project. This makes life easier for the
and electric lines? support and maintenance!
18 - datablueprint.com 1/10/2013 © Copyright this and previous years by Data Blueprint - all rights reserved!

TITLE

Database Architecture Focus

#dataed

TITLE
Data Architecture Focus has potentially greater Business Value

#dataed

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Data Architecture Focus
• Data can be shared
• Redundancy can be reduced
• Inconsistency can be avoided
• Transaction support can be provided
• Integrity can be maintained
• Security can be enforced
• Conflicting requirements can be balanced
• Eliminates Data Dependency
– Technique used to physically stored and accessed are dictated by the
application, and the knowledge of physical representation and access
technique is built into the application code.
– Not desirable in database systems
– Different users require different views of the same data
– Freedom to change the physical representation or access technique
in view of the changing requirements
• Changing record types
• Physical storage location
#dataed

TITLE
Primary Deliverables become Reference Material


TITLE

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

TITLE

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

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Models as an Aid to Understanding
Models
• Are usually for the
purpose of
understanding
• Can be
– Equations
– Simulations including video games
– Physical models
– Mental models

#dataed

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Polling Question 1
What is a data model?

a. Framework for understanding and design

b. Easy to validate and review

c. Structure for organizing things

d. All of the above

#dataed

TITLE

What a model is

Source: Ellen Gottesdiener www.ebgconsulting.com #dataed

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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

The Role of Data Models in Rapid Development

360 hours or 15 days of continuous building
http://www.youtube.com/watch?v=Hdpf-MQM9vY&feature=player_embedded#!

TITLE

Modeling in Context

Preliminary Wrapup
Activity Modeling
activities activities
cycles
Analysis
Evidence
collection &
analysis Collection

Project
coordination
requirements Declining coordination requirements

Target
system
analysis Increasing amounts of tar et system analysis
g
Validation
M odeling
cycle
focus Refinement


TITLE

Agenda
BoK/CDMP?
using data analysis
techniques

Standard definition reporting does not provide conceptual context

Bed

Something you sleep in


TITLE
The power of the Purpose Statement
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
• 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; this one is read as "One room contains zero
or many beds."

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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

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

2. 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

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

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Don’t Tell Them your Modeling!

• Just write some stuff
down
• Then arrange it
• Then make some
appropriate
connections between
your objects

#dataed

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Entity Relationship View

CUSTOMER soda

coins machine

#dataed (adapted from [Davis 1990])

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Entity Relationship View
selects
CUSTOMER soda
given to

deposits coin dispenses
return

coins machine

entity thing about which we maintain
information
object entity encapsulated with attributes
#dataed and functions (adapted from [Davis 1990])

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Describing Data Flows and Processes
CUSTOMER
do
not
have money change cancel
Flavor change request
selection can
of
soda count money

insufficient
Detect
cancel
request
accept flavor
sufficient
flavors valid Flavor funds
selection

flavor choices dispense soda 39
(adapted from [Davis 1990])
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION

TITLE

Keep them focused on the purpose
• The reason we are locked
in this room is to:
– Mission: Review proposal
from voice over IP
providers
• Outcome: Walk out the
door with the top two
proposals selected and
scheduled personal
presentations from each.
– Mission: Discuss logo
ideas for the Bore No More
movement
• Outcome: We will walk
out the door when we
identify the top three
traits that represent the
Bore No More brand.
– Mission: Update all
employees on the
retirement plan options
• Outcomes: Confirm that
all team members took
part in the meeting and
have access to review
their plans privately with
a financial consultant.

TITLE

Agenda
BoK/CDMP?
using data analysis
techniques

TITLE

Metadata Example
CRUD matrix that shows business processes and their activity type
Creating, Reading, Updating, and Deleting various data items

Business Business Business Business Business
Process 1 Process 2 Process 3 Process 4 Process 5

Data Item A Create Read Delete

Data Item B Read Create Update

Data Item C Read Update

Data Item D Create Update Delete Update

Data Item E Create


Reengineering
Reverse Engineering

As Is As Is Design Assets As Is Implementation
Requirements Assets
Assets
Existing

To Be To Be To Be
Requirements Design Implementation
Assets Assets Assets
New

Forward engineering

• First, reverse engineering the existing system to understand its strengths/weaknesses
• Next, use this information to inform the design of the new system

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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.

TITLE

Modeling Types
Logical or Physical or
Essential Implementatio
System n System

Current
or
Existing Logical “as-is” Physical “as-is”
System

Proposed
or Target Logical “to-be” Physical “to-be”
System


Architecture Evolution Framework

Validated

Not
Validated

Conceptual Logical Physical

Every change can be mapped to a transformation in this framework

TITLE

Information Modeling
Strategic
Level
Models

Tactical
Level
Models

Operational
Level
Models
Models from a single
data reverse
engineering project


5 Basic Data Structures
Program: Where is the record
Program: Must start at the for person "Townsend?"
beginning and read each
record when looking for
person "Townsend?" Index

Flat File Index: Start looking here
where the "Ts" are stored

Network Database
Indexed Sequential File
Relational Database

Hierarchical Database

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Polling Question 2
How much non-relational database processing is out
there?
a. A lot

b. Just a tiny bit

c. A significant

d. None

#dataed

Total % of non-relational processing
5.4% Non-Relational Database Processing
Percentage of mission-critical, non-relational processing
1.5% Percentage of non-relational processing (excluding mission-critical)

20.5% 0.5%
15.7% 1.0%

9.8% 0.3% 0.3%
7.6% 8.6%
0.4%
4.9% 4.9%
3.0% 0.4%
1.7% 1.3%
0% 0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage of organizations relying on x amount of non-relational database processing

• 68% using hierarchical (typically IMS or Adabase)
• 20% reporting operational network DBMS
• "the rumors of the demise of non-relational processing
are greatly exaggerated" (from Mark Twain)
• Virtually no textbook education

Poor Quality Foundation


TITLE

Agenda
BoK/CDMP?
using data analysis
techniques

TITLE

Eliminate Entire IT Systems

#dataed

Why have data structure problems been so difficult?


Student System
Data Model


Proposed Data Model

Running Query


Optimized Query


TITLE

Agenda
BoK/CDMP?
using data analysis
techniques

TITLE

Technique/Technical Interdependencies

Master Data Management

Data Governance
Data Quality


Models are required to synchronize between IT activities
Metadata Management Practices will be inextricably intertwined with Extraction
Sources
Data Quality and Master Data and Knowledge
Management, (among other EIM Functions)
Organized Knowledge 'Data' Knowledge
Management
Practices
Routine Data Scans Data Organization Practices
Metadata(Prac8ces((dashed lines not in existence)
( (

Metadata( Metadata( Metadata(
Engineering( Storage( Delivery(
Sources( ( Uses(
Metadata(Governance(

Data that might benefit from
Suspected/ Master Management
Identified
Master Data Catalogs
Data
Quality Master Data
Management
Problems Data Quality Practices
Engineering

Routine Data Scans
Improved Quality Data
Operational Data

TITLE
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
Moonlighting

with one JOB CLASS;
associated
with one JOB
CLASS.

Job Sharing
Employee Position

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

Person Job Class
Expressing Data Requirements
• Example 1:
– Our organization has lots of
Employee Position employees who work multiple jobs
• Example 2:
– Our organization wants to employ many part-time
employees
• Requirements
– We need to manage these requirements as efficiently as
possible
– Each person that we track must have the capability to be
tracked as multiple employees
– Each position must be capable of being staffed by
multiple persons -> employees


TITLE

Agenda
BoK/CDMP?
using data analysis
techniques

TITLE

Polling Question 3
How do Data models support strategy?

a. Flexible, adaptable data structures

b. Cleaner, less complex code

c. Built in future capabilities

d. All of the above

#dataed

TITLE
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

TITLE

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
Type

Sales Manager
Manager
Person Type

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

TITLE

S0: 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

#dataed

TITLE

S1: Mission Model Analysis

#dataed

TITLE

S2: 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

#dataed

TITLE

Map Goals to Mission

#dataed

TITLE

Next Step
Market
Market Need
Need

Market
Product
Market Product
Customer Need

Customer
Need

Customer
Customer Product
Product


TITLE

Subsequent Step for Business Value
Need
Market Need
Performance

Market Product
Performance
Performance Performance

Customer
Customer Product
Performance


TITLE

Agenda
BoK/CDMP?
using data analysis
techniques

TITLE

Virtually any Tool can help!

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

Data Model Users/Uses
Database administration : Strategic planners :
content management, cluster analyses, data storing the organizational data
base design and implementation, performance architecture, enterprise wide models, and the strategic
normalization metadata information plan, system utilization
information, and the strategic information plan metadata
Repository administration :
Establish the corporate repository model, .
repository customization, content management, Projects developers :
and "where used" and "how used" metadata requirement, storing requirements, analysis, prototypes,
designs, tests, project management, project deliverables,
code creating and impact analysis metadata
Data administration :
Standards, data assets, context and content
management, data tracaability metadata End users :
policies, practices, procedures, organizations, business
rules, responsibilities, authorities, roles metadata

Project management :
Estimating, tracking, and reporting metadata

Methods administration :
Quality assurance personnel : methodology evolution and customization,
Content verification, reconciliation, and facilitation, technique customization, compliance
standards compliance metadata and deliverable production metadata

TITLE

Data Modeling for Business Value
• 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
– The more easily it adapts to change, the resource utilization
• 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
Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2

TITLE

Questions?

+ =

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


TITLE

Upcoming Events
February Webinar:
Unlocking Business Value through Data Modeling and Data Architecture
(Part II of II)
February 12, 2012 @ 2:00 PM ET/11:00 AM PT

March Webinar:
The Top Data Job
March 12, 2012 @ 2:00 PM ET/11:00 AM PT

Sign up here:
• www.datablueprint.com/webinar-schedule
• www.Dataversity.net
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