Data Systems Integration & Business Value Pt. 2: Cloud

Copyright 2013 by Data Blueprint
Data Systems Integration & Business Value Part 2: Cloud-based Integration
All organizations are prepared to benefit from aspects of the cloud.
These benefits accrue when cloud-hosted datasets share three
attributes. They must be of:
1. Higher quality data than those data residing outside of the cloud;
2. Lower volume (1/5 the size of data collections) than similar
collections residing outside of the cloud; and
3. Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and
responsiveness, as well as the forecast decreases in cost accruing
to cloud-based computing are all possible after these first three
conditions have been met. Necessary investments in data
engineering can help organizations to save even more money by
reducing the amount of resources required to perform their duties
and increasing the effectiveness of their duties & decision-making.
This webinar will show you how to recognize the opportunities,
‘size up’ the required investment, and properly supervise your
efforts to take advantage of the opportunities presented by the
cloud.
Date: August 13, 2013
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D.
1

Executive Editor at DATAVERSITY.net
2
Shannon Kempe

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?
3

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5
Peter Aiken, PhD
• 25+ years of experience in data
management
• Multiple international awards &
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• President, DAMA International (dama.org)
• 8 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, Nokia, Deutsche Bank,
Wells Fargo, and the Commonwealth
of Virginia
2
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman

Data Systems Integration & Business
Value Part 2: Cloud-based Integration
Presented by Peter Aiken, Ph.D.
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

Anticipated Business Value of Cloud-based Integration
7
• Increased Automation and Storage Capacity
– Virtually unlimited capacity & flexible storage
– Easy to upgrade & Up-to-date software
– Automated file synching & backups
• Affordability
– Pay as you go
– Usage is scaled to fit needs
• Agility, Scalability and Flexibility
– Access from anywhere & collaborate
– Data is always current
• Free up IT Hours & Staff
– Cloud provider takes care of maintenance
• Ease of Use
– Easy to use & automated

Prerequisites to Cloud-based Integration
• Organizational investments in the cloud will be useless from
a data perspective unless:
– Data governance, architecture, quality, development practices are
sufficiently mature
– You must understand your data architecture and strategy in order to
evaluate various cloud options
– Data must be reengineered to be
• Less
• Better quality
• More shareable
– for the cloud
– Failure to do these will
result in more business
value for the cloud
vendors/service providers
and less for your
organization
8

1. Data Management: Contextual Overview
2. Necessary Data Management Functions
(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
3. Understanding Cloud-based
Technologies
4. Cloud-based Benefits
5. Cloud-based Integration
- Cleaner
- Smaller
- Shareable
6. Take Aways, References and Q&A
Tweeting now:
#dataed
Outline: Cloud-based Integration
9
(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
10

Data Program
Coordination
Feedback
Data
Development
Standard
Data
Five Integrated DM Practice Areas
Organizational Strategies
Goals
Business
Data
Business Value
Application
Models &
Designs
Implementation
Direction
Guidance
11
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

Five Integrated DM Practice Areas
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
12

Hierarchy of Data Management Practices (after Maslow)
• 5 Data management
practices areas /
data management
basics ...
• ... are necessary but
insufficient
prerequisites to
organizational data
leveraging
applications that is
self actualizing data
or advanced data
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
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Cloud

• Data Management Body of Knowledge
(DMBOK)
– Published by DAMA International, the
professional association for
Data Managers (40 chapters worldwide)
– Organized around primary data management
functions focused around data delivery to the
organization and several environmental elements
• Certified Data Management Professional
(CDMP)
– Series of 3 exams by DAMA International and
ICCP
– Membership in a distinct group of
fellow professionals
– Recognition for specialized knowledge in a
choice of 17 specialty areas
– For more information, please visit:
• www.dama.org, www.iccp.org
DAMA DM BoK & CDMP
14

Series Context
• Certain systems are more data
focused than others. Usually
their primary focus is on
accomplishing integration of
disparate data. In these cases,
failure is most often attributable
to the adoption of a single
technological pillar (silver bullet).
The three webinars in the Data
Systems Integration and Business Value
series are designed to illustrate that
good systems development more often depends on at least three
DM disciplines (pie wedges) in order to provide a solid foundation.
• Data Systems Integration & Business Value
– Pt. 1: Metadata Practices
– Pt. 2: Cloud-based Integration
– Pt. 3: Warehousing, et al.
15

Uses
Part 1: Metadata Take Aways
• Metadata unlocks the value of data, and therefore requires
management attention [Gartner 2011]
• Metadata is the language of data governance
• Metadata defines the essence of integration challenges
Sources
Metadata Governance
Metadata
Engineering
Metadata
Delivery
Metadata Practices
Metadata
Storage
16
Specialized Team Skills

Data
Management
functions
necessary but
insufficient for
metadata-
based
integration
Data Management
Body of
Knowledge
17
From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
18

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
19

Data Management
Body of
Knowledge
20
Data
Management
functions
necessary but
insufficient
for cloud-
based
integration

21
Data
Governance

Data Governance
22

Organizational Data Governance Purpose Statement
• What does data governance
mean to my organization?
– Getting some individuals (whose
opinions matter)
– To form a body (needs a formal
purpose/authority)
– Who will advocate/evangelize for
(not dictate, enforce, rule)
– Increasing scope and rigor of
– Data-centric development
practices
23

Top
Operations
Job
Data Governance is a Gateway for IT Projects
24
Top Job
Top
Finance
Job
Top
Information
Technology
Job
Top
Marketing
Job
• Data assets are better foundational building block for IT projects
• CDO coordinates IT investment priorities with Top IT Job
• CDO determines when proposed IT projects are "ready"
Data Governance Organization
Chief
Data
Officer

25
Data
Architecture
Management

Data Architecture Management
26

Architectural Answers
(Adapted from [Allen & Boynton 1991])
Computers
Human resources
Communication facilities
Software
Management
responsibilities
Policies,
directives,
and rules
Data
27
• Where do they go?
• When are they needed?
• What standards
should be adopted?
• What vendors
should be chosen?
• What rules should govern
the decisions?
• What policies should guide
the process?
• How and why do the components interact?
• Why and how will the changes be implemented?
• What should be managed organization-wide and what should
be managed locally?

Zachman Framework 3.0 - the Enterprise Ontology
Classification
Names
Model
Names
*Horizontal integration lines
areshownforexamplepurposes
only and are not a complete set.
Composite, integrative rela-
tionships connecting every cell
horizontally potentially exist.
Audience
Perspectives
Enterprise
Names
Classification
Names
Audience
Perspectives
C o m p o s i t e I n t e g r a t i o n s
A
l
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s
C o m p o s i t e I n t e g r a t i o n s C o m p o s i t e I n t e g r a t i o n s
A
l
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g
n
m
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a
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f
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n
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T
r
a
n
s
f
o
r
m
a
t
i
o
n
s
A l i g n m e n t
A l i g n m e n t
How Where Who WhenWhat Why
Process
Flows
Distribution
Networks
Responsibility
Assignments
Timing
Cycles
Inventory
Sets
Motivation
Intentions
Operations
Instances
(Implementations)
The
Enterprise
The
Enterprise
Enterprise
Perspective
(Users)
Executive
Perspective
(Business
Context
Planners)
Business Mgmt
Perspective
(Business
Concept

Owners)
Architect
Perspective
(Business
Logic
Designers)
Engineer
Perspective
(Business
Physics

Builders)
Technician
Perspective
(Business
Component
Implementers)
Scope
Contexts
(Scope
Identification

Lists)
Business
Concepts
(Business
Definition

Models)
System
Logic
(System
Representation
Models)
Technology
Physics
(Technology
Specification
Models)
Tool
Components
(Tool
Configuration

Models)
e.g. e.g. e.g. e.g. e.g. e.g.
e.g.: primitive e.g.: composite model:
model:
Forecast Sales
Plan Production
Sell Products
Take Orders
Train Employees
Assign Territories
Develop Markets
Maintain Facilities
Repair Products
Record Transctns
Material Supply Ntwk
Product Dist. Ntwk
Voice Comm. Ntwk
Data Comm. Ntwk
Manu. Process Ntwk
Office

Ntwk
Parts Dist. Ntwk
Personnel Dist. Ntwk
etc., etc.
General Mgmt
Product Mgmt
Engineering Design
Manu. Engineering
Accounting
Finance
Transportation
Distribution
Marketing
Sales
Product Cycle
Market Cycle
Planning Cycle
Order Cycle
Employee Cycle
Maint. Cycle
Production Cycle
Sales Cycle
Economic Cycle
Accounting Cycle
Products
Product Types
Warehouses
Parts Bins
Customers
Territories
Orders
Employees
Vehicles
Accounts
New Markets
Revenue Growth
Expns Reduction
Cust Convenience
Customer Satis.
Regulatory Comp.
New Capital
Social Contribution
Increased Yield
Increased Qualitye.g. e.g. e.g. e.g. e.g. e.g.
Operations
Transforms
Operations
In/Outputs
Operations
Locations
Operations
Connections
Operations
Roles
Operations
Work
Products
Operations
Intervals
Operations
Moments
Operations
Entities
Operations
Relationships
Operations
Ends
Operations
Means
Process
Instantiations
Distribution
Instantiations
Responsibility
Instantiations
Timing
Instantiations
Inventory
Instantiations
Motivation
Instantiations
List: Timing Types
Business Interval
Business Moment
List: Responsibility Types
Business Role
Business Work Product
List: Distribution Types
Business Location
Business Connection
List: Process Types
Business Transform
Business Input/Output
System Transform
System Input /Output
System Location
System Connection
System Role
System Work Product
System Interval
System Moment
Technology Transform
Technology Input /Output
Technology Location
Technology Connection
Technology Role
Technology Work Product
Technology Interval
Technology Moment
Tool Transform
Tool Input /Output
Tool Location
Tool Connection
Tool Role
Tool Work Product
Tool Interval
Tool Moment
List: Inventory Types
Business Entity
Business Relationship
System Entity
System Relationship
Technology Entity
Technology Relationship
Tool Entity
Tool Relationship
List: Motivation Types
Business End
Business Means
System End
System Means
Technology End
Technology Means
Tool End
Tool Means
Timing
IdentificationResponsibility
IdentificationDistribution
IdentificationProcess
Identification
Timing
DefinitionResponsibility
DefinitionDistribution
DefinitionProcess
Definition
Process
Representation Distribution
Representation Responsibility
Representation Timing
Representation
Process
Specification Distribution
Specification Responsibility
Specification Timing
Specification
Inventory
Identification
Inventory
Definition
Inventory
Representation
Inventory
Specification
Inventory
Configuration Process
Configuration Distribution
Configuration Responsibility
Configuration Timing
Configuration
Motivation
Identification
Motivation
Definition
Motivation
Representation
Motivation
Specification
Motivation
Configuration
28
Copyright 2008-2011 John A. Zachman

What is an information architecture?
• A structure of data-based information
assets supporting implementation of
organizational strategy (or strategies)
• 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?
29
Classification
Names
Model
Names
*Horizontal integration lines
areshownforexamplepurposes
only and are not a complete set.
Composite, integrative rela-
tionships connecting every cell
horizontally potentially exist.
Audience
Perspectives
Enterprise
Names
Classification
Names
Audience
Perspectives
A
l
i
g
n
m
e
n
t
T
r
a
n
s
f
o
r
m
a
t
i
o
n
s
A
l
i
g
n
m
e
n
t
T
r
a
n
s
f
o
r
m
a
t
i
o
n
s
C o m p o s i t e I n t e g r a t i o n s C o m p o s i t e I n t e g r a t i o n s
A
l
i
g
n
m
e
n
t
T
r
a
n
s
f
o
r
m
a
t
i
o
n
s
A
l
i
g
n
m
e
n
t
T
r
a
n
s
f
o
r
m
a
t
i
o
n
s
A l i g n m e n t
A l i g n m e n t
How Where Who WhenWhat Why
Process
Flows
Distribution
Networks
Responsibility
Assignments
Timing
Cycles
Inventory
Sets
Motivation
Intentions
Operations
Instances
(Implementations)
The
Enterprise
The
Enterprise
Enterprise
Perspective
(Users)
Executive
Perspective
(Business
Context
Planners)
Business Mgmt
Perspective
(Business
Concept

Owners)
Architect
Perspective
(Business
Logic
Designers)
Engineer
Perspective
(Business
Physics

Builders)
Technician
Perspective
(Business
Component
Implementers)
Scope
Contexts
(Scope
Identification

Lists)
Business
Concepts
(Business
Definition

Models)
System
Logic
(System
Representation
Models)
Technology
Physics
(Technology
Specification
Models)
Tool
Components
(Tool
Configuration

Models)
e.g.: primitive e.g.: composite model:
model:
Forecast Sales
Plan Production
Sell Products
Take Orders
Train Employees
Assign Territories
Develop Markets
Maintain Facilities
Repair Products
Record Transctns
Material Supply Ntwk
Product Dist. Ntwk
Voice Comm. Ntwk
Data Comm. Ntwk
Manu. Process Ntwk
Office

Ntwk
Parts Dist. Ntwk
Personnel Dist. Ntwk
etc., etc.
General Mgmt
Product Mgmt
Engineering Design
Manu. Engineering
Accounting
Finance
Transportation
Distribution
Marketing
Sales
Product Cycle
Market Cycle
Planning Cycle
Order Cycle
Employee Cycle
Maint. Cycle
Production Cycle
Sales Cycle
Economic Cycle
Accounting Cycle
Products
Product Types
Warehouses
Parts Bins
Customers
Territories
Orders
Employees
Vehicles
Accounts
New Markets
Revenue Growth
Expns Reduction
Cust Convenience
Customer Satis.
Regulatory Comp.
New Capital
Social Contribution
Increased Yield
Increased Qualitye.g. e.g. e.g. e.g. e.g. e.g.
Operations
Transforms
Operations
In/Outputs
Operations
Locations
Operations
Connections
Operations
Roles
Operations
Work
Products
Operations
Intervals
Operations
Moments
Operations
Entities
Operations
Relationships
Operations
Ends
Operations
Means
Process
Instantiations
Distribution
Instantiations
Responsibility
Instantiations
Timing
Instantiations
Inventory
Instantiations
Motivation
Instantiations
List: Timing Types
Business Interval
Business Moment
List: Responsibility Types
Business Role
Business Work Product
List: Distribution Types
Business Location
Business Connection
List: Process Types
Business Transform
Business Input/Output
System Transform
System Input /Output
System Location
System Connection
System Role
System Work Product
System Interval
System Moment
Technology Transform
Technology Input /Output
Technology Location
Technology Connection
Technology Role
Technology Work Product
Technology Interval
Technology Moment
Tool Transform
Tool Input /Output
Tool Location
Tool Connection
Tool Role
Tool Work Product
Tool Interval
Tool Moment
List: Inventory Types
Business Entity
Business Relationship
System Entity
System Relationship
Technology Entity
Technology Relationship
Tool Entity
Tool Relationship
List: Motivation Types
Business End
Business Means
System End
System Means
Technology End
Technology Means
Tool End
Tool Means
Timing
IdentificationResponsibility
IdentificationDistribution
IdentificationProcess
Identification
Timing
DefinitionResponsibility
DefinitionDistribution
DefinitionProcess
Definition
Process
Representation Distribution
Representation Responsibility
Representation Timing
Representation
Process
Specification Distribution
Specification Responsibility
Specification Timing
Specification
Inventory
Identification
Inventory
Definition
Inventory
Representation
Inventory
Specification
Inventory
Configuration Process
Configuration Distribution
Configuration Responsibility
Configuration Timing
Configuration
Motivation
Identification
Motivation
Definition
Motivation
Representation
Motivation
Specification
Motivation
Configuration

!

!

!

!

30
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

Data Architecture – Better Definition
31
• 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]

32
Data
Development

Data Modeling/Data Development
33

34
#dataed
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
Data Development Focus

35
#dataed
Data Development has greater Business Value

36
Conceptual Logical Physical
Validated
Not Validated
Every change can
be mapped to a
transformation in
this framework!
Data Development Evolution Framework

37
Data
Quality
Management

Data Quality Engineering
38

Definitions
• Quality Data
– Fit for use meets the requirements of its authors, users,
and administrators (adapted from Martin Eppler)
– Synonymous with information quality, since poor data quality
results in inaccurate information and poor business performance
• Data Quality Management
– Planning, implementation and control activities that apply quality
management techniques to measure, assess, improve, and
ensure data quality
– Entails the "establishment and deployment of roles, responsibilities
concerning the acquisition, maintenance, dissemination, and
disposition of data" http://www2.sas.com/proceedings/sugi29/098-29.pdf
✓ Critical supporting process from change management
✓ Continuous process for defining acceptable levels of data quality to meet business
needs and for ensuring that data quality meets these levels
• Data Quality Engineering
– Recognition that data quality solutions cannot not managed but must be engineered
– Engineering is the application of scientific, economic, social, and practical knowledge in
order to design, build, and maintain solutions to data quality challenges
– Engineering concepts are generally not known and understood within IT or business!
39
Spinach/Popeye story from http://it.toolbox.com/blogs/infosphere/spinach-how-a-data-quality-mistake-created-a-myth-and-a-cartoon-character-10166

Quality Dimensions
40

Starting
point
for new
system
development
data performance metadata
data architecture
data
architecture and
data models
shared data updated data
corrected
data
architecture
refinements
facts &
meanings
Metadata &
Data Storage
Starting point
for existing
systems
Metadata Refinement
• Correct Structural Defects
• Update Implementation
Metadata Creation
• Define Data Architecture
• Define Data Model Structures
Metadata Structuring
• Implement Data Model Views
• Populate Data Model Views
Data Refinement
• Correct Data Value Defects
• Re-store Data Values
Data Manipulation
• Manipulate Data
• Updata Data
Data Utilization
• Inspect Data
• Present Data
Data Creation
• Create Data
• Verify Data Values
Data Assessment
• Assess Data Values
• Assess Metadata
Extended data life cycle model with metadata sources and uses
41

DQE Context & Engineering Concepts
• Can rules be implemented stating that no data can be
corrected unless the source of the error has been
discovered and addressed?
• All data must
be 100%
perfect?
• Pareto
– 80/20 rule
– Not all data
is of equal
Importance
• Scientific,
economic,
social, and
practical
knowledge
42

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
43

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
44

http://visual.ly/amazing-journey-data-cloud
45

46

47

Gartner Five-phase Hype Cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
48
Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest
trigger significant publicity. Often no usable products exist and commercial viability is unproven.
Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the
technology shake out or fail. Investments continue only if the surviving providers improve their products to the
satisfaction of early adopters.
Peak of Inflated Expectations: Early publicity produces a number of
success stories—often accompanied by scores of failures. Some
companies take action; many do not.
Slope of Enlightenment: More instances of how the technology can benefit the
enterprise start to crystallize and become more widely understood. Second- and third-
generation products appear from technology providers. More enterprises fund pilots;
conservative companies remain cautious.
Plateau of Productivity: Mainstream adoption starts to
take off. Criteria for assessing provider viability are more
clearly defined. The technology’s broad market
applicability and relevance are clearly paying off.

Gartner Cloud Hype Cycle “While clearly
maturing, cloud
computing
continues to be the
most hyped subject
in IT today.”
49

50
• Cloud computing is location-independent
computing, whereby shared servers provide
resources, software, and data to computers
and other devices on demand, as with the
electricity grid.
• Cloud computing is a natural evolution of the
widespread adoption of virtualization, service-
oriented architecture and utility computing.
• Details are abstracted from consumers, who no
longer have need for expertise in, or control over,
the technology infrastructure "in the cloud" that
supports them.
Cloud Computing

Five Essential Characteristics of Data Cloud Infrastructure
• Gartner defines "cloud computing" as the set of disciplines,
technologies, and business models used to deliver IT
capabilities (software, platforms, hardware) as an on-
demand, scalable, elastic service.
• Five essential characteristics of cloud computing:
– It uses shared infrastructure
– It provides on-demand
self-service
– It is elastic and scalable
– It is priced by consumption
– It is dynamic and virtualized
51

52
Cloud Scalability

Cloud Rendering
53

Cisco's Ladder to the Cloud
54

Cloud Options
55

Solving the Big Data Puzzle
56
http://damfoundation.org/2012/06/whats-the-big-deal-about-big-data/

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
Outline
57

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
Outline
58

Benefits
59

Benefits
60

Anticipated Benefits
61
0% 13% 25% 38% 50%
Improve data quality
Reduce installation and maintenance efforts
Reduce implementation efforts
Eliminate manual processes
Reduce time require to collect and prepare data
Apply data governance policies

Similar Opportunity
• IT Infrastructure. Your submission should include funding for the timely execution of agency plans
to consolidate data centers developed in FY 2010 (reference FY 2011 passback guidance). In
coordination with the data center consolidations, agencies should evaluate the potential to adopt
cloud computing solutions by analyzing computing alternatives for IT investments in FY 2012.
Agencies will be expected to adopt cloud computing solutions where they represent the best value at
an acceptable level of risk.
• Adopt Light Technologies and
Shared Solutions. We are reducing
our data center footprint by 40
percent by 2015 and shifting the
agency default approach to IT to a
cloud-first policy as part of the 2012
budget process. Consolidating more
than 2,000 government data centers
will save money, increase security
and improve performance.
62

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
63

(Prerequisites)
- Data Governance
- Data Architecture
- Data Development
- Data Quality
Technologies
- Cleaner
- Smaller
- Shareable
Tweeting now:
#dataed
64

Data in the cloud should have three attributes that
data outside the cloud should not have. It should be:
65
Sharable-er
Cleaner
Smaller

Aspirational Data in the Cloud
66

Effective Cloud Transformation
• Transformation into cloud computing cannot be
done in a manner that benefits organizations
unless data is re-architected – formally with two goals:
– Maximizing effective, organization-wide data sharing; and
– Minimizing organizational data ROT.
• Resulting data volume reduction should be 1/5 what is currently is
– A significant economic motivator.
• All existing organizations have data collections that possess
unique strengths and weaknesses
– Strengths that should be leveraged
– Weaknesses must be addressed
• Neither of these can be accomplished without formal data
rearchitecting prior to cloud loading.
• There are very few who work in the area for a living but my team
has achieved some remarkable successes.
67

Transform
68
Problems with forklifting
1. no basis for
decisions made
2. no inclusion of
architecture/
engineering concepts
3. no idea that these
concepts are missing
from the process
Less
Cleaner
More shareable
... data
Getting into the Cloud

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
69
Less ROT
Technologies
Process
People

The Cloud as a Data Quality Tool
Enterprise Portal
Data DeliveryData Analysis
Quality
Technology
Continuous Improvement
Data Baselining
Statistical Data Control
Cost of Quality Model
Empowerment
Data Reduction
Pattern Analysis
Mathematical Analysis
Schema Validation
Reusability
Logic & Logic Programming
Relational DB Technology
Data Migration Technologies
Statistical Programming Languages
70

Fixing Data in the Cloud Using A Glovebox
71

72
Conceptual Logical Physical
Validated
Not Validated
Every change can
be mapped to a
transformation in
this framework!
Data Development Evolution Framework

Data Systems Integration & Business Value Pt. 2: Cloud

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