Data-Ed: 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.
Copyright 2014 by Data Blueprint
1
Welcome: Data Architecture Requirements
Date: May 13, 2014
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD

Two Most 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?
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Meet Your Presenter: Dr. Peter Aiken
• Internationally recognized data
management thought-leader
– 30 years of experience
– Recipient of multiple international awards
– Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• (Past) Pres. DAMA International (dama.org)
• 9 books and dozens of articles
• Multi-year immersions with
organizations as diverse as the
US DoD, Deutsche Bank, Nokia, Wells
Fargo, the Commonwealth of Virginia
and Walmart
4

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

• 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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7

You can accomplish
Advanced Data Practices
without becoming proficient
in the Basic Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
Data Management Practices Hierarchy
Basic Data Management Practices
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
8
Data Program Management
Data Stewardship Data Development
Data Support Operations
Organizational Data Integration

Data Program
Coordination
Feedback
Data
Development
Standard
Data
Organizational Strategies
Goals
Business
Data
Business Value
Application
Models &
Designs
Implementation
Direction
Guidance
9
Organizational
Data Integration
Data
Stewardship
Data Support
Operations
Data
Asset Use
Integrated
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity
subject area data
integration
Provide reliable
data access
Achieve sharing of data
within a business area
Organizational DM Practices

10
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program
Coordination
Organizational
Data Integration
Data Stewardship Data Development
Data Support
Operations
Five Integrated DM Practices

11
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

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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
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

17
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

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

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Architecture
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.

20
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

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Understanding
• 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

22

healthcare.gov
23
• 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_manage
ment_and_too_many_cooks.html

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• 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 Architectures

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

Data Architecture – Better Definition
27
• All organizations have information
architectures
– Some are better understood and
documented (and therefore more
useful to the organization) than
others.
• Common vocabulary expressing
integrated requirements ensuring
that data assets are stored,
arranged, managed, and used in
systems in support of
organizational strategy [Aiken 2010]

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29

Vocabulary is Important-Tank, Tanks, Tankers, Tanked
30

How one inventory item proliferates data throughout the chain
31
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
8,535 Total items
16 Attributes/item
136,560 Total attributes
System 5
15,959 Total items
22 Attributes/item
351,098 Total attributes
Total for the five systems show above:
59,350 Items
179 Unique attributes
3,065,790 values

32
• 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
Business Value: Agency units are carrying $1.5 billion worth of expired inventory

33
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 Architectural Models?

34
Architecture Example

35
Poor Quality Foundation

36
What they think they are purchasing!

37
Context Diagrams Show System Boundaries

38
Too Much Detail

39
Web Developers Understand IA
http://www.jeffkerndesign.com

40
Web Developers Understand IA
http://www.jeffkerndesign.com

41
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
Database Architecture Focus

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
42
Data Architecture Focus has Greater Potential Business Value
• Broader focus
than either
software
architecture or
database
architecture
• Analysis scope is
on the system
wide use of data
• Problems caused
by data
exchange or
interface
problems
• Architectural
goals more
strategic than
operational

Data
Data
Data
Information
Fact Meaning
Request
Strategic Information Use: Prerequisites
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
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
43

44
A B
C D
A B
C D
A
D
C
B
How are data structures expressed as architectures?
• Details are
organized into
larger
components
• Larger
components
are organized
into models
• Models are
organized into
architectures

45
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?
More Granular
More Abstract

46
Architectures Comprise a Network of Networks

47
How do data structures 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

Computers
Human resources
Communication facilities
Software
Management
responsibilities
Policies,
directives,
and rules
Data
48
What Questions Can Architectures Address?
• How and why do the
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?

!

!

!

!

49
Organizational Needs
become instantiated
and integrated into an Data/Information
Architecture
Informa(on)System)
Requirements
authorizes and
articulates
satisfyspecificorganizationalneeds
Data Architectures produce and are made up of information models that
are developed in response to organizational needs

50

51

52
Less ROT
Technologies
Process
People
Data Leverage
• 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

53
Conceptual Logical Physical
Validated
Not Validated
Architecture Evolution Framework
Every change can
be mapped to a
transformation in
this framework!

Application-Centric Development
Original articulation from Doug Bagley @ Walmart
54
Data/
Information
Network/
Infrastructure
Systems/
Applications
Goals/
Objectives
Strategy
• 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
55
Systems/
Applications
Network/
Infrastructure
Data/
Information
Goals/
Objectives
Strategy
• 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

Why is Data Architecture Important?
• 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
56

57
Architectural Work Product
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.

58
Engineering Standards

System
Process
Process
2
Process
1
Process
3
Subprocess
1.1
Subprocess
1.2
Subprocess
1.3
59
Hierarchical System Functional Decomposition

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
60

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
61

DSS
"Governors"
Taxpayers Clients
Vendors Program Deliver
62
Data model is comprised of model views
DSS Strategic Data Model
Taxpayer view
Client view
Governance view
Program Delivery view
Vendor view

Taxpayer view
Payments Taxpayers
Social
Service
Programs
Taxpayer
Benefits
63

Client view
Payments
Clients Client
Benefits
Local
Wellfare
Agencies
64

Governance view
Payments
Social
Service
Programs
Governmental
Resources
Governance Governments
State Board
of Social
Services
Policy
Approval
65

Social
Service
Programs
Clients
Service
Delivery
Partners
Local
Wellfare
Agencies
66
Program Delivery view

Payments
Social
Service
Programs
Clients
Local
Wellfare
Agencies
Goods
and
Services
Vendors
67
Vendor view

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
68
DSS Strategic Level Data Model

69

70

71
Challenge
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."

Home Page
Business Process
Name
Business Process
Component
Business Process
Component Step
72
PeopleSoft Process Metadata
Home Page Name
(relates to one or more)
Business Process Name
Business Process Component Name
Business Process Component Step Name

73
Example Query Outputs

Home Page Name
Business Process Name
Business Process Component Name
Business Process Component Step Name
Peoplesoft Metadata Structure
processes
(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)
74
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
75
Business Value - Better Decisions

76

77

78
A National Cancer Institute
• This Virginia 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

Other Departments
SSI
S
Cancer
Registry
Hospital Claims
Staging
SSI
S
Physician
Invoices
Patient
Demographics
Billing Data
(Hospital)
Billing Data
(Physician)
Diagnoses
(Hospital)
Diagnoses
(Physician)
Diagnoses
(Registry)
Physicians
(Hospital)
Physicians
(Physician)
SSI
S
SSI
S
Consolidated/
Sandbox
SSIS SSA
S
Patient
(Consolidated)
RP
T
Physicians
(Consolidated)
Diagnoses
(Consolidated)
SSR
S
SharePoint
Excel
Email
One-off reports
Reusable reports
Conceptual Target Architecture

0
25
50
75
100
Current Improved
Reversing The Measures
• 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
81
Manipulation Analysis
A 20% improvement results in a doubling of productivity!

Results: It is not always about money
• 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
82

83

84

Engineering
Architecture
85
Engineering/Architecting Relationship
• 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
86
What is this?
• It is tall
• It has a clutch
• It was built in 1942
• It is still in regular use!

Improving Data Quality during System Migration
87
• 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 9.50% 22.62% 67.88%
18 7.46% 22.62% 69.92%
Architecture Derived: Diminishing Returns Determination
88

Time needed to review all NSNs once over the life of the project: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: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: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: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 93
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
89
Quantitative Benefits

90

91

92
Take Aways
• 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

June Webinar:
Monetizing Data Management
June 10, 2014 @ 2:00 PM ET/11:00 AM PT
Sign up here:
• www.datablueprint.com/webinar-schedule
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PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.

Questions?
94
+ =

Data-Ed: Data Architecture Requirements

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