Data-Ed Webinar: Data Architecture Requirements

Data architecture is foundational to an information-based
operational environment. It is your data architecture that
organizes your data assets so they can be leveraged in
your business strategy to create real business value.
Even though this is important, not all data architectures
are used effectively. This webinar describes the use of
data architecture as a basic analysis method. Various
uses of data architecture to inform, clarify, understand,
and resolve aspects of a variety of business problems
will be demonstrated. As opposed to showing how to
architect data, your presenter Dr. Peter Aiken, will show
how to use data architecting to solve business problems.
The goal is for you to be able to envision a number of
uses for data architectures that will raise the perceived
utility of this analysis method in the eyes of the business.
Welcome: Data Architecture Requirements
1
Copyright 2015 by Data Blueprint
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
Date: March 9, 2015
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD

Shannon Kempe
Executive Editor at DATAVERSITY.net
2

Two Most Commonly Asked Questions
3
1. Will I get copies of the
slides after the event?
2. Is this being recorded so I
can view it afterwards?

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Peter Aiken, Ph.D.
5
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions: 
- US DoD 
- Nokia 
- Deutsche Bank 
- Wells Fargo 
- Walmart
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
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

We believe ...
Data  
Assets
Financial  
Assets
Real 
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be  
used up
Can be  
used up
Non-
degrading √ √ Can degrade 
over time
Can degrade 
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
6
• Today, data is the most powerful, yet underutilized and poorly managed
organizational asset
• Data is your
– Sole
– Non-depleteable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• Our mission is to unlock business value by
– Strengthening your data management capabilities
– Providing tailored solutions, and
– Building lasting partnerships
Asset: A resource controlled by the organization as a result of past events or transactions and from which
future economic benefits are expected to flow [Wikipedia]

Presented by Peter Aiken, Ph.D.
Data Architecture Requirements

8
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A

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Maslow's Hierarchiy of Needs
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You can accomplish Advanced
Data Practices without
becoming proficient in the
Foundational Data
Management 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 Management Practices
11
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities

Maintain fit-for-purpose data,
efficiently and effectively
12
Manage data coherently
Manage data assets professionally
Data architecture
implementation
Data lifecycle
implementation
Organizational support
DMM℠ Structure of  
5 Integrated  
DM Practice Areas

The DAMA Guide to the Data Management Body of Knowledge
Data Management Functions
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 Architecture Management
14
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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

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Architecture is both the process and
product of planning, designing and
constructing space that reflects functional,
social, and aesthetic considerations.
A wider definition may comprise all design
activity from the macro-level (urban design,
landscape architecture) to the micro-level
(construction details and furniture).
In fact, architecture today may refer to the
activity of designing any kind of system and
is often used in the IT world.
Architecture

Architectures: here, whether you like it or not
deviantart.com
• All organizations
have architectures
– Some are better
understood and
documented (and
therefore more
useful to the
organization) than
others

Architecture Representation
• Architectures are the symbolic  
representation of the structure,  
use and reuse of resources
• Common components are  
represented using standardized notation
• Are sufficiently detailed to permit both business
analysts and technical personnel to separately read
the same model, and come away with a common
understanding and yet they are developed effectively

Understanding
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• A specific definition
– 'Understanding an architecture'
– Documented and articulated as a (digital) blueprint
illustrating the  
commonalities and  
interconnections  
among the  
architectural  
components
– Ideally the understanding  
is shared by systems and humans

• Process Architecture
– Arrangement of inputs -> transformations = value -> outputs
– Typical elements: Functions, activities, workflow, events, cycles, products, procedures
• Systems Architecture
– Applications, software components, interfaces, projects
• Business Architecture
– Goals, strategies, roles, organizational structure, location(s)
• 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
Typically Managed Organizational Architectures

• The underlying (information) design principals upon
which construction is based
– Source: http://architecturepractitioner.blogspot.com/
• … are plans, guiding the transformation of strategic
organizational information needs into specific
information systems development projects
– Source: Internet
• A framework providing a structured description of an
enterprise’s information assets — including
structured data and unstructured or semistructured
content — and the relationship of those assets to
business processes, business management, and IT
systems.
– Source: Gene Leganza, Forrester 2009
• "Information architecture is a foundation discipline
describing the theory, principles, guidelines,
standards, conventions, and factors for managing
information as a resource. It produces drawings,
charts, plans, documents, designs, blueprints, and
templates, helping everyone make efficient,
effective, productive and innovative use of all types
of information."
– Source: Information First by Roger & Elaine Evernden, 2003 ISBN 0
7506 5858 4 p.1.
• Defining the data needs of the enterprise and
designing the master blueprints to meet those needs
– Source: DM BoK
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Information Architecture

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Data Architecture – A Useful Definition
• Common vocabulary expressing
integrated requirements ensuring that data
assets are stored, arranged, managed,
and used in systems in support of
organizational strategy [Aiken 2010]

Vocabulary is Important-Tank, Tanks, Tankers, Tanked
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How one inventory item proliferates data throughout
an organization's data architecture
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555 Subassemblies & subcomponents
17,659 Repair parts or Consumables
System 1: 
18,214 Total items 
75 Attributes/ item 
1,366,050 Total attributes
System 2 
47 Total items 
15+ Attributes/item 
720 Total attributes
System 3
16,594 Total items
73 Attributes/item
1,211,362 Total attributes
System 4 
16 Attributes/item 
136,560 Total attributes
System 5 
22 Attributes/item 
351,098 Total attributes
Total for the five systems show above: 
59,350 Items 
179 Unique attributes 
3,065,790 values

Business Value: Agency units are carrying $1.5 billion worth of expired inventory
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• Generates unnecessary costs & negative impacts on operations, including:
– Resources are focused on non-value added tasks of maintaining obsolete inventory, which
creates distractions to the agency’s main mission
• Storage
– Physical/real estate needed to house items
• Handling
– Includes transportation and human resources  
dedicated to moving, maintaining, counting  
and securing outdated inventory
• Opportunity
– Inventory could be returned to manufacturer or  
sold to free up financial assets for more needed  
and critical supplies
• Systemic
– Cost of inventorying information and maintaing  
paper or electronic records which should be used to  
support mission-critical acquisitions and distribution
• Maintenance
– Repairing of expired items

Data Architecture – A More Useful Definition
• A structure of data-based information
assets supporting implementation of
organizational strategy (or strategies) [Aiken 2010]
• Most organizations have data assets that
are not supportive of strategies - i.e.,
information architectures that are not
helpful
• The really important question is: how can
organizations more effectively use their
information architectures to support
strategy implementation?

What do you use an information architecture for?
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Illustration by murdock23 @ http://designfestival.com/information-architecture-as-part-of-the-web-design-process/

Database Architecture Focus
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3

database
architecture
engineering
effort
DataData
DataData
Data
Data
Data
Focus of a
software
architecture
engineering
effort Program A
Program B
Program C
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 1
Application
domain 2Application
domain 3
Data
Focus of a
Data
Data
Data Architecture Focus has Greater Potential Business Value
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• 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

Why is Data Architecture Important?
34
• Poorly understood
– Data architecture asset value is not well  
understood
• Inarticulately explained
– Little opportunity to obtain learning and experience
• Indirectly experienced
– Cost organizations millions each year in productivity,
redundant and siloed efforts
– Example: Poorly thought out software purchases

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healthcare.gov
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• 55 Contractors!
• "Anyone who has written a
line of code or built a system
from the ground-up cannot be
surprised or even
mildly concerned that
Healthcare.gov did not work
out of the gate,"  
 
Standish Group International
Chairman Jim Johnson said in a
recent podcast.  
• "The real news would have
been if it actually did work.
The very fact that most of it
did work at all is a success in
itself."
• Software programmed to
access data using traditional
data management
technologies
• Data components
incorporated "big data
technologies" 
http://www.slate.com/articles/technology/bitwise/2013/10/
problems_with_healthcare_gov_cronyism_bad_management
_and_too_many_cooks.html

Moon Lighting
Practical Application of Data Architecting
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 JOB
CLASS;
BR3) Zero, one, or more EMPLOYEES can be associated with one POSITION
BR4) One or
more
POSITIONS can
be associated
with one JOB
CLASS.
37
Job Sharing

Running Query
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Optimized Query
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Repeat 100s, thousands, millions of times ...
40

Death by 1000 Cuts
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• How does poor data architecture cost money?
• 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?
– They cannot be helpful as long as their structure is unknown
• 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
Lack of coherent data architecture is a hidden expense
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MONETIZING
DATA MANAGEMENT

Data Architecting for Business Value
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Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2
• 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

Architecture Example

Poor Quality Foundation
45

What they think they are purchasing!
46

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

Too Much Detail

Web Developers Understand IA
http://www.jeffkerndesign.com

How are data structures expressed as architectures?
51
A B
C D
A B
C D
A
D
C
B
• Details are
organized into  
larger
components
• Larger
components
are organized
into models
• Models are
organized into
architectures

How are Data Models Expressed as Architectures?
52
More Granular 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
More Abstract 
• 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?

Data
Data
Data
Information
Fact Meaning
Request
Data must be Architected to Deliver Value
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
53
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
Data
Data
Data Data

How do data structures support
organizational strategy?
54
• Two answers
– Achieving efficiency and effectiveness goals
– Providing organizational dexterity for rapid implementation

Computers
Human resources
Communication facilities
Software
Management
responsibilities
Policies,
directives,
and rules
Data
What Questions Can Data Architectures Address?
• How and why do the
data components
interact?
• Where do they go?
• When are they needed?
• Why and how will the  
changes be
implemented?
• What should be
managed organization-
wide and what should be
managed locally?
• What standards should
be adopted?
• What vendors should be
chosen?
• What rules should
govern the decisions?
• What policies should
guide the process?

!

!

!

!

Data Architectures produce and are made up of information models that are
developed in response to organizational needs
56
Organizational Needs
become instantiated  
and integrated into an
Data/Information 
Architecture
Informa(on)System)
Requirements
authorizes and  
articulates
satisfyspecificorganizationalneeds

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Data Leverage
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Less ROT
Technologies
Process
People
• Permits organizations to better manage their sole non-depleteable, non-
degrading, durable, strategic asset - data
– within the organization, and
– with organizational data exchange partners
• Leverage
– Obtained by implementation of data-centric technologies, processes, and human skill
sets
– Increased by elimination of data ROT (redundant, obsolete, or trivial)
• The bigger the organization, the greater potential leverage exists
• Treating data more asset-like simultaneously
1. lowers organizational IT costs and
2. increases organizational knowledge worker productivity

Architecture Evolution
60
Conceptual Logical Physical
Validated
Not
UnValidated
Every change can
be mapped to a
transformation in
this framework!

Application-Centric Development
Original articulation from Doug Bagley @ Walmart
Data/
Information
Network/
Infrastructure
Systems/
Applications
Goals/
Objectives
Strategy
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• In support of strategy, organizations develop
specific goals/objectives
• The goals/objectives drive the development of
specific systems/applications
• Development of systems/applications leads to
network/infrastructure requirements
• Data/information are typically considered after
the systems/applications and network/
infrastructure have been articulated
• Problems with this approach:
– Ensures data is formed to the applications and not
around the organizational-wide information  
requirements
– Process are narrowly formed around applications
– Very little data reuse is possible

Data-Centric Development
Original articulation from Doug Bagley @ Walmart
Systems/
Applications
Network/
Infrastructure
Data/
Information
Goals/
Objectives
Strategy
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• In support of strategy, the organization develops
specific goals/objectives
• The goals/objectives drive the development of
specific data/information assets with an eye to
organization-wide usage
• Network/infrastructure components are
developed supporting organizational data use
• Development of systems/applications is derived
from the data/network architecture
• Advantages of this approach:
– Data/information assets are developed from an
organization-wide perspective
– Systems support organizational data needs and
compliment organizational process flows
– Maximum data/information reuse

Engineering
Architecture
Engineering/Architecting Relationship
63
• Architecting is used to
create and build systems
too complex to be treated
by engineering analysis
alone
• Architects require
technical details as the
exception
• Engineers develop the
technical designs
• Craftsman deliver
components supervised
by:
– Building Contractor
– Manufacturer

USS Midway
& Pancakes
What is this?
64
• It is tall
• It has a clutch
• It was built in 1942
• It is still in regular use!

Engineering Standards
65

Architectural Work Product
66
Components may be defined as:
• The intersection of common business functionality and the  
subsets of the organizational technology and data  
architectures used to implement that functionality
• Component definition is an important activity because CM2 component
engineering is focused on an entire component as an analysis unit. A
concrete example of a component might be
– The business processes, the technology and the data supporting
organizational human resource benefits operations. This same
component could be described simply as the "PeopleSoft™
version 7.5 benefits module implemented on Windows 95."
illustrates the integration of the three primary PeopleSoft
metadata structures describing the: business processes used to
organization the work flow, menu navigation required to access
system functionality, and data which when combined with
meanings provided by the panels provided information to the
knowledge workers.

System
Process
Process
2
Process
1
Process
3
Subprocess
1.1
Subprocess
1.2
Subprocess
1.3
Hierarchical System Functional Decomposition
67

Level 1 Level 2 Level 3
Pay Employment Recruitment
and Selection
personnel Personnel Employee relations
administration Employee compensation changes
Salary planning
Classification and pay
Job evaluation
Benefits administration
Health insurance plans
F lexible spending accounts
Group life insurance
Retirement plans
Payroll Payroll administration
Payroll processing
Payroll interfaces
Development N/A
Training
administration
Career planning and skills
inventory
Work group activities
Health and
safety
Accidents and workers
compensation
Health and safety programs
A three-level
decomposition of
the model views
from the
governmental pay
and personnel
scenario
68

H ealth car e system
1 Patient administration
1.1 R egistration
1.2 Admission
1.3 Disposition
1.4 Transfer
1.5 M edical record
1.6 Administration
1.7 Patient billing
1.8 Patient affairs
1.9 Patient management
2 Patient appointments
and scheduling
2.1 Create or maintain
schedules
2.2 Appoint patients
2.3 R ecord patient encounter
2.4 I dentify patient
2.5 I dentify health care
provider
3 Nursing
3.1 Patient care
3.2 Unit management
4 Laboratory
4.1 R esults reporting
4.2 Specimen processing
4.3 R esult entry processing
4.4 Laboratory management
4.5 Workload support
5 Pharmacy
5.1 Unit dose dispensing
5.2 Controlled Drug
I nventory
5.3 Outpatient
6 R adiology
6.1 Scheduling
6.2 E xam processing
6.3 E xam reporting
6.4 Special interest and
teaching
6.5 R adiology workload
reporting
7 Clinical dietetics
7.1 E stablish parameters
7.2 R eceive diet orders
8 Order entry and results
8.1 R eporting
8.2 E nter and maintain
orders
8.3 Obtain results
8.4 R eview patient
information
8.5 Clinical desktop
9 System management
9.1 Logon and security
management
9.2 Archive run
M anagement
9.3 Communication software
9.4 M anagement
9.5 Site management
10 Facility quality assurance
10.1 Provider credentialing
10.2 M onitor and evaluation
A relatively
complex model
view
decomposition
69

DSS
"Governors"
Taxpayers Clients
Vendors Program Deliver
Data model is comprised of model views
70
DSS Strategic Data Model
Taxpayer view
Client view
Governance view
Program Delivery view
Vendor view

Taxpayer view
Payments Taxpayers
Social
Service
Programs
Taxpayer
Benefits
71

Client view
Payments
Clients Client
Benefits
Local
Wellfare
Agencies
72

Governance view
Payments
Social
Service
Programs
Governmental
Resources
Governance Governments
State Board
of Social
Services
Policy
Approval
73

Social
Service
Programs
Clients
Service
Delivery
Partners
Local
Wellfare
Agencies
Program Delivery view
74

Payments
Social
Service
Programs
Clients
Local
Wellfare
Agencies
Goods
and
Services
Vendors
Vendor view
75

Governmental
Resources
Governance Governments Payments Taxpayers
State Board
of Social
Services
Social
Service
Programs
Clients Client
Benefits
Taxpayer
Benefits
Policy
Approval
Service
Delivery
Partners
Local
Wellfare
Agencies
Goods
and
Services
Vendors
DSS Strategic Level Data Model
76

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Challenge
79
Package Implementation Example
• "Green screen" legacy system to be replaced with Windows Icons
Mice Pointers (WIMP) interface; and
• Major changes to operational processes
– 1 screen to 23 screens
• Management didn't think workforce could adjust to simultaneous
changes
– Question: "How big a change will it be to replace all instances of person_identifier
with social_security_number?"
• Answer:
– (from "big" consultants) "Not a very big change." ($5 million budget)

Home Page
Business Process  
Name
Component
Component Step
PeopleSoft Process Metadata
80
Home Page Name
(relates to one or more)
Business Process Name
Business Process Component Name
Business Process Component Step Name

Example Query Outputs
81

Home Page Name
Business Process Name
Business Process Component Name
Business Process Component Step Name
Peoplesoft Metadata Structureprocesses
(39)
homepages
(7)
menugroups
(8)
components
(180)
stepnames
(822)
menunames
(86)
panels
(1421)
menuitems
(1149)
menubars
(31)
fields
(7073)
records
(2706)
parents
(264)
reports
(347)
children
(647)
(41) (8)
(182)
(847)
(949)
(86)
(281)
(1259)(1916)
(5873)
(264)
(647)(708)
(647)
(25906)
(347)
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PeoplesoftMetadataStructure

Quantity
System
Component
Time to make
change
 
Labor Hours
1,400 Panels 15 minutes 350
1,500 Tables 15 minutes 375
984
Business
process
component steps
15 minutes 246
Total 971
X $200/hour $194,200
X 5 upgrades $1,000,000
Business Value - Better Decisions
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Blueprint

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A National Cancer Institute
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• This cancer center is a leader in
shaping the fight against cancer
• Over 500 researchers and staff
tend to over 12,000 patients
annually
• This requires robust information
management and analytical
services
• The problem: It takes 1 month to
run a report on an incident, i.e. a
patient’s hospital visit that shows
all touch points

Other Departments
SQL
SQLSAS
Cancer
Registry
Claims
Database
File
Export
Physician
Invoices
Patient
(Hospital)
Patient
(Physician)
Patient
(Registry)
Billing Data
(Hospital)
Billing Data
(Physician)
Diagnoses
(Hospital)
Diagnoses
(Physician)
Diagnoses
(Registry)
Physicians
(Hospital)
Physicians
(Physician)
Access
SQL
SQL
SAS
SQL
Excel
Excel
Hospital
Claims
Text
Files FTP FTP
Text
Files
FTP or
Email
Word
Word
Word
Current State Assessment
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Other Departments
SSIS
Cancer Registry
Hospital Claims
Staging
SSIS
Physician Invoices
Patient
Demographics
Billing Data
(Hospital)
Billing Data
(Physician)
Diagnoses
(Hospital)
Diagnoses
(Physician)
Diagnoses
(Registry)
Physicians
(Hospital)
Physicians
(Physician)
SSIS SSIS Consolidated/
Sandbox
SSIS
SSAS
Patient
(Consolidated)
RPT
Physicians
(Consolidated)
Diagnoses
(Consolidated)
SSR
S
SharePoint
Excel
Email
One-off reports
Reusable reports
Conceptual Target Architecture
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0
25
50
75
100
Current Improved
Manipulation Analysis
Reversing The Measures
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• Currently:
– Analysts spend 80% of their time manipulating data and 20% of their time
analyzing data
– Hidden productivity bottlenecks
• After rearchitecting:
– Analysts spend less time manipulating data and more of their time analyzing data
– Significant improvements in knowledge worker productivity
A 20% improvement results in a doubling of productivity!

Results: It is not always about money
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• Solution:
– Integrate multiple databases into
one to create holistic view of
data
– Automation of manual process
• Results:
– Data is passed safely and
effectively
– Eliminate inconsistencies,
redundancies, and corruption
– Ability to cross-analyze
– Significantly reduced turnaround
time for matching patients with
potential donor -> increased
potential to make life-saving
connection in a manner that is
faster, safer and more reliable
– Increased safe matches from 3
out of 10 to 6 out of 10

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Improving Data Quality during System Migration
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• Challenge
– Millions of NSN/SKUs  
maintained in a catalog
– Key and other data stored in  
clear text/comment fields
– Original suggestion was manual  
approach to text extraction
– Left the data structuring problem unsolved
• Solution
– Proprietary, improvable text extraction process
– Converted non-tabular data into tabular data
– Saved a minimum of $5 million
– Literally person centuries of work

Unmatched Items Ignorable Items Items Matched
Week # (% Total) (% Total) (% Total)
1 31.47% 1.34% N/A
2 21.22% 6.97% N/A
3 20.66% 7.49% N/A
4 32.48% 11.99% 55.53%
… … … …
14 9.02% 22.62% 68.36%
15 9.06% 22.62% 68.33%
16 9.53% 22.62% 67.85%
17 9.5% 22.62% 67.88%
18 7.46% 22.62% 69.92%
Architecture Derived: Diminishing Returns Determination
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Time needed to review all NSNs once over the life of the project:
NSNs 2,000,000
Average time to review & cleanse (in minutes) 5
Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:
Work weeks in a year 48
Work days in a week 5
Work hours in a day 7.5
Work minutes in a day 450
Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:
Minutes needed 10,000,000
Minutes available person/year 108,000
Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:
Avg Salary for SME year (not including overhead) $60,000.00
Projected Years Required to Cleanse/Total DLA Person Year
Saved
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Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
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Quantitative Benefits

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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!
Why Architect Data?
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Take Aways
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• What is an information architecture?
– A structure of data-based information assets  
supporting implementation of organizational strategy
– Most organizations have data assets that are not supportive of strategies -  
i.e., information architectures that are not helpful
– The really important question is: how can organizations more effectively use their
information architectures to support strategy implementation?
• What is meant by use of an information architecture?
– Application of data assets towards organizational strategic objectives
– Assessed by the maturity of organizational data management practices
– Results in increased capabilities, dexterity, and self awareness
– Accomplished through use of data-centric development practices (including taxonomies,
stewardship, and repository use)
• How does an organization achieve better use of its information
architecture?
– Continuous re-development; the starting point isn't the beginning
– Information architecture components must typically be reengineered
– Using an iterative, incremental approach, typically focusing on one component at a time and
applying formal transformations

Upcoming Events
EDW 2015 
 
Developing Data Strategy and Roadmap
March 29, 2015 @ 5:00 PM ET
Addressing Data Challenges  
with the (DMM) Data Management Maturity
March 30, 2015 @ 2:00 PM ET/11:00 AM PT 
April Webinar:
Data Governance Strategies
April 14, 2015 @ 2:00 PM ET/11:00 AM PT
Sign up here:
• www.datablueprint.com/webinar-schedule
• www.Dataversity.net
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