Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden

Peter Aiken, Ph.D.
Data Architecture Strategies
Constructing your data garden
Peter Aiken, Ph.D.
• 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 (DISA/Army/Marines/DLA)
– 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
2Copyright 2017 by Data Blueprint Slide #

Bryan Hogan, CDMP
• Data Consultant
• Certified Data
Management
Professional
• Experience in ….
– Organizational Data
Management Assessments
– Data Strategy Development
– ETL Process Development
– Reporting Solutions
– Software Development
• Worked in ….
– Healthcare
– Non-Profit
– Finance
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 √ √ √ √
We believe ...
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depletable
– 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]
Data Assets Win!

Data Architecture Requirements
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• NGO Data Architecture Case Study
• Take Aways, References & Q&A
ArchitectureJargon

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
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
Copyright 2017 by Data Blueprint Slide # 7
Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
Data architecture
implementation
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality

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
Architecture
• Things
– (components)
• The functions of the things
– (individually)
• How the things interact
– (as a system, towards a goal)

Data Architecture Management
• Definition
– Defining the data needs of the enterprise and designing
the master blueprints to meet those needs
• Goals
1. To plan with vision and foresight to provide high-quality
data
2. To identify and define common data requirements
3. To design conceptual structures and plans to meet the
current and long-term data requirements of the enterprise
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Data Architecture Management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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
• Architectures are sufficiently detailed to permit
both business analysts and technical personnel to
separately read the same model, and come away
with a common understanding

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
Organizational 
Architectures
• Amazon
– Traditional
structure
• Google
– Team of 3
• Facebook
– Do you really have
a structure?
• Microsoft
– Eliminate their own
products
• Apple
– Everything
revolves around
one individual
• Oracle
– Buys one company
after another

• 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
Information Architecture

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]

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?
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
• 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?
• 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
Higher res image available?

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.
Job Sharing
Running Query

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

Death by 1000 Cuts
Lack of coherent data architecture is a hidden expense
• 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
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.

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

Poor Quality Foundation
What they think they are purchasing!

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

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

Web Developers Understand IA
http://www.jeffkerndesign.com
How are data structures expressed as architectures?
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?
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
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?
• 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
Organizational Needs
become instantiated  
and integrated into an
Data/Information 
Architecture
Informa(on)System)
Requirements
authorizes and  
articulates
satisfyspecificorganizationalneeds

Data Leverage
• Permits organizations to better manage their sole non-depletable,
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
Less ROT
Technologies
Process
People
Architecture Evolution
Conceptual Logical Physical
Validated
Not
UnValidated
Every change can
be mapped to a
transformation in
this framework!

IT Project or Application-Centric Development
Original articulation from Doug Bagley @ Walmart
Data/
Information
IT 
Projects
 
Strategy
• In support of strategy, organizations
implement IT projects
• Data/information are typically
considered within the scope of IT
projects
• 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
IT 
Projects
Data/ 
Information
 
Strategy
• In support of strategy, the organization
develops specific, shared data-based
goals/objectives
• These organizational data goals/
objectives drive the development of
specific IT projects with an eye to
organization-wide usage
• 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
• 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
Copyright 2017 by Data Blueprint Slide # 53
USS Midway
& Pancakes
What is this?
• It is tall
• It has a clutch
• It was built in 1942
• It is still in regular use!

Engineering Standards
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.

System
Process
Process
2
Process
1
Process
3
Subprocess
1.1
Subprocess
1.2
Subprocess
1.3
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 a
governmental
pay and
personnel
scenario

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
DSS
"Governors"
Taxpayers
Clients
Vendors
Program Deliver
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
Client view
Payments
Clients Client
Benefits
Local
Wellfare
Agencies

Governance view
Payments
Social
Service
Programs
Governmental
Resources
Governance Governments
State Board
of Social
Services
Policy
Approval
Social
Service
Programs
Clients
Service
Delivery
Partners
Local
Wellfare
Agencies
Program Delivery view

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

• Non-Governmental Organization (NGO)
• Non-Profit
• Industry
– Address Priority Health Concerns for Developing Countries
• HIV & AIDS
• Malaria
• Etc…
– Provide Leadership Training
– Health Information System Management
• Function
– Project Management and Design for  
Health Care Implementations
• Operates
– Globally (30 + Countries)
Background

Problem
• Data needed to make key business decisions was not
accessible across the Enterprise
– Timeliness
– Accuracy
– Data Isolation
Root Cause
• No Enterprise-Wide understanding of its data assets
– Conceptual Data Model
• NGO does not have a common vocabulary
– Enterprise-Wide Taxonomy
• NGO lacks existing System and Data Architecture
– Vision
– Not Aligned with Business Model
– “Shiny Object Syndrome”
– Minimal Integration

Solution
• Vision and Purpose
– Data Architecture
• Business Glossary
• Enterprise Conceptual Data Model
Vision and Purpose
TARGET STATE VISION
COLLABORATION & WIP DOCUMENTS
Talent
Management
Business
Development
Project
Management
CAPTURE DATA
INTEGRATE DATA
Talent
Management
Financial
Management
Business
Development
Project
Management
CREATEREPORTSANDPERFORMBI
STORECORPORATEDATA
MANAGE CONTENT
Financial
Management
DATAGOVERNANCE
• 100,000 ft. View
• Represents the processes,
procedures, and
technologies that make up
the Components
• Federated Data
Architecture (FDA)
• FDA supports the business
strategy
• Set of entities (Projects)
that have a level of
autonomy to support its
goal while a unifying entity
(Shared Services from
Corporate) provides a
framework and definition
on how data is to managed
and captured

Business Glossary
Entity Description Domain Area
Donor Funder Business Development
Solicitations Need for Work Business Development
Solicitations Proposal Response to Need for Work Business Development
Pre-Positioning Intelligence Gathering Business Development
Award/Sub-Award Funding Vehicle Business Development
Terms Conditions Details about a Funding Vehicle Business Development
Budget Amount of Money Available Business Development
Work Plan Set of Activities to Complete Business Development
PMP Monitoring Plan for Activities Business Development
Project
An NGO Project is defined as a
self-contained set of
interventions or activities with the
following characteristics:
a) an external client;
b) purchase order, contract or
agreement;
c) expected deliverables,
outcomes and results;
d) a beginning and end date of
implementation;
e) an approved budget; and
full and/or part time NGO staff Project Management
Geographic Area Project Management
Office Locations
Location in which a Central Office
resides Project Management
Project Roles Project Management
Project Artifacts Project Management
Project Budget Project Management
Project Work Plan Project Management
Milestones Schedule of completed activities Project Management
Monitoring Plan to measure Activities Project Management
Evaluation Assessment of Activities Project Management
Indicators Target of Outcome Project Management
Outcomes
Statement of what needs to be
accomplished Project Management
Acct Receivable Payments to NGO Financial Management
Chart of Accounts Defined Accounts Financial Management
Payroll Process to Pay Worker Financial Management
Supplier Provider of Goods or Service Financial Management
Contract Binding Agreement Financial Management
Purchase Order Statement of Good or Service Financial Management
Performance Level of Success Talent Management
Benefits Talent Management
Skills Talent Management
Worker
Person who has been hired by
NGO Talent Management
Candidate Potential hire of NGO Talent Management
• Start of Enterprise
Taxonomy
• Defines Initial Entities for
Conceptual Data Model
• Engages the Business
Community to Validate
Entities and provide
meaningful business
definitions
EnterpriseConceptualDataModel
• Linkages
across
Business
Functions
• How Data
flows
throughout
Enterprise
• Impact from
Data Changes
• Defines
Common
Vocabulary
• Aligning the
Data to
support the
Organizational
Strategy

Business Value
• Supports Organizational Strategy
• Reduced IT Costs
• Data Asset Knowledge and Reuse
• Accurate and Timely Reporting
Supports Organizational Strategy
• Defining a common vocabulary across the enterprise
increases cohesion between the Business and IT.
• Cohesion allows IT to effectively support the
Organizational Strategy
• Understanding the  
business’s needs
Understanding
Resized & moved
"Understanding"

Reduced IT Costs
• Data Architecture guides IT on software implementations
– Mitigates “poor” software purchases
– Reduces cost of implementations
• Maintaining and Managing the Data Landscape
– A defined Data Architecture allows IT to manage and maintain the
critical pieces of the Data Landscape
– Reduces cost of trying to manage and maintain everything
Data Asset Knowledge and Reuse
• Knowledge of how the Organization’s Data can be
leveraged
– Increased Organizational Learning
• Identified Key Integration Points
– Allows IT to focus on the critical Data Assets
– Increases Re-Use of Data Assets for future Integrations
• Identified Impact to Data Flows
– Allows IT to plan for future implementations
– Reduces impact to the Organizational existing Data Assets

• Reduce Time Building Reports
– Faster Decision Making
– Single Source of Truth
• Less “Massaging” of Data
– Increased Productivity from  
Knowledge Workers
– Decreased Errors from compiling redundant data
Accurate and Timely Reporting
DATA

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?
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

Organizational 
Strategy
Data Strategy
IT Projects
Organizational Operations
Data
Governance
Data Strategy and Data Governance in Context
Data
asset support for  
organizational
strategy
What the
data assets do to
support strategy
How well the data
strategy is working
Operational
feedback
How data is
delivered by IT
How IT
supports strategy
Other
aspects of
organizational
strategy
Questions?
+ =

March Webinar:
The Importance of MDM 
Eternal Management of the Data Mind
March 14, 2017 @ 2:00 PM ET/11:00 AM PT
April Webinar:
Data Modeling Strategies 
Getting Your Data Ready for the Catwalk
April 11, 2017 @ 2:00 PM ET/11:00 AM PT
Sign up at: www.datablueprint.com/webinar-schedule
Enterprise Data World 2017 (Atlanta):
Implementing a Data-Centric Strategy & Roadmap:  
Planning Not to Fail Importance of MDM 
April 3, 2017 @ 1:30 PM ET 
Using Data to Help Improve People's Everyday Lives 
April 5, 2017 @ 1:30 PM ET
Upcoming Events
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Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden

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