Enterprise Architecture vs. Data Architecture

Copyright Global Data Strategy, Ltd. 2022
Enterprise Architecture vs. Data Architecture
Donna Burbank
Global Data Strategy, Ltd.
November 29, 2022

Global Data Strategy, Ltd. 2022
Donna Burbank
2
Donna is a recognised industry expert in
information management with over 20 years
of experience in data strategy, information
management, data modeling, metadata
management, and enterprise architecture.
Her background is multi-faceted across
consulting, product development, product
management, brand strategy, marketing, and
business leadership.
She is currently the Managing Director at
Global Data Strategy, Ltd., an international
information management consulting company
that specializes in the alignment of business
drivers with data-centric technology.
In past roles, she has served in key brand
strategy and product management roles at CA
Technologies and Embarcadero Technologies
for several of the leading data management
products in the market.
As an active contributor to the data
management community, she is a long time
DAMA International member, Past President
and Advisor to the DAMA Rocky Mountain
chapter, and was awarded the Excellence in
Data Management Award from DAMA
International.
She has worked with dozens of Fortune 500
companies worldwide in the Americas,
Europe, Asia, and Africa and speaks regularly
at industry conferences. She has co-authored
several books and is a regular contributor to
industry publications. She can be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, US.

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Master Data Management – Aligning Data, Process, and Governance
• April Data Governance & Data Architecture: Alignment & Synergies
• May Improving Data Literacy Around Data Architecture
• June Business Intelligence & Data Analytics: An Architected Approach
• July Best Practices in Metadata Management
• August Data Quality Best Practices
• September Business-centric Data Modeling
• October Graph Databases: Benefits & Risks
• December Enterprise Architecture vs. Data Architecture
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This Year’s Lineup

What We’ll Cover Today
• Enterprise Architecture (EA) provides a visual
blueprint of the organization, and shows key
interrelationships between data, process,
applications, and more.
• By abstracting these assets in a graphical
view, it’s possible to see key
interrelationships, particularly as they relate
to data and its business impact across the
organization.
• This webinar will discuss how data
architecture is a key component of an overall
enterprise architecture for enhanced
business value and success.
4

Enterprise Architecture - Definition
• Enterprise architecture (EA) is a discipline for proactively
and holistically leading enterprise responses to
disruptive forces by identifying and analyzing the
execution of change toward desired business vision and
outcomes.
• …by presenting business and IT leaders with signature-
ready recommendations for adjusting policies and
projects to achieve target business outcomes that
capitalize on relevant business disruptions.
• EA is used to steer decision making toward the evolution
of the future state architecture.1
5
Supporting Business Innovation with a Strong Architectural Foundation
1 Gartner IT Glossary
Innovation
Foundation

Enterprise Architecture – Definition for Data Architects
• Just as you need to model the data in an
organization, you need to model the organization
itself:
• Motivations & Goals
• Business Capabilities
• Business Processes
• As well as the related technologies that support
the organization
• Applications
• Data
• Networks
• Etc.
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Modeling is important on many levels

Frameworks for Enterprise Architecture
• The Zachman Framework organizes
data into the simple categories of:
• What
• How
• Where
• Who
• When
• Why?
• Data fits nicely within the “What”
column.
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Zachman Framework
https://www.zachman.com

Frameworks for Enterprise Architecture
• The TOGAF Architecture Development Method (ADM) developed by the OpenGroup is a step-by-
step approach to developing an enterprise architecture.
• It provides a detailed framework for building an architecture around Business, Data, Application &
Technology.
8
TOGAF EA Framework
www.opengroup.org

Data as Part of Enterprise Architecture
• Enterprise Architecture provides a high-level view of the people, processes,
applications, and data of an organization
• Putting data in business context
• How does data link to the rest of my organization?
• If I change data, what business processes are affected?

What EA Model Types are in Use?
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What Types of Models/Diagrams do you use in your Data/Enterprise Architecture?
From Trends in Data Management, a 2022 DATAVERSITY® Report, by Donna Burbank and Keith Foote

3
Architecture vs. Construction
• It’s a common analogy to use building architecture as an analogy to data architecture.
• When constructing a building, there is a clear distinction between designing a house and building a house.
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Design Build

The Role of the Architect
12
Technology Business
Janus

Global Data Strategy, Ltd. 2022 13
Architecture Diagrams and Artifacts – Some Tools of the Trade

Business Motivation Model
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Corporate Mission Corporate Vision
Data-centric Goals & Objectives
To provide a full service online retail experience
for art supplies and craft products.
To be the respected source of art products worldwide,
creating an online community of art enthusiasts.
Artful Art Supplies ArtfulArt
C
External Drivers
Digital Self-Service
Increasing
Regulation Pressures
Online Community &
Social Media
Customer Demand
for Instant Provision
Internal Drivers
Cost Reduction
Targeted Marketing
360 View of
Customer
Brand Reputation Community Building
Revenue Growth
C
Accountability
• Create a Data Governance
Framework
• Define clear roles &
responsibilities for both
business & IT staff
• Publish a corporate
information policy
• Document data standards
• Train all staff in data
accountability
C
Quality
• Define measures & KPIs for
key data items
• Report & monitor on data
quality improvements
• Develop repeatable
processes for data quality
improvement
• Implement data quality
checks as BAU business
activities
C
Culture
• Ensure that all roles
understand their
contribution to data quality
• Promote business benefits
of better data quality
• Engage in innovative ways
to leverage data for
strategic advantage
• Create data-centric
communities of interest
• Corporate-level Mission & Vision
• May already be created or may
need to create as part of project.
• External Drivers are what you’re
facing in the industry
• Internal Drivers reflect internal
corporate initiatives.
• Data-Centric Goals & Objectives
• Clear direction for the project
• Use marketing-style headings
where possible

Use Case Model
• The Use Case Model
• Categorizes existing demand
• Provides a “heat map” of
usage patterns
• Particularly important for large,
geographically distributed
teams & departments.

Business Capability Models
• A business capability model outlines the core functional areas of the organization.
• Note: this is not the same as an organizational chart
• Capabilities can be overlaid with key data domains to create a “heat map” of cross-functional data usage.
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Core Business
Shared Services
Artful Art’s Business Capabilities
Etc. – sample subset only
Product Development
R&D
Product
Management
Product
Manufacturing
Packaging
Marketing
Product
Messaging
Branding
Product
Launch
Campaign
Development
Lead
Generation
Pricing
Sales
Pipeline
Management
Customer
Relationship
Quotes &
Tenders
Research & Development Branding & Go-to-Market
Partner
Management
Sales & Distribution
Human Resources
Recruitment
Employee
Training
Performance
Management
Legal
Compliance
Contract
Management
Data Domains
Customer
Product
Account
Etc.

Data-Driven Merger for Financial Services
The combined information assets of both companies is one of our biggest strategic advantages.
- CEO
• A key driver for a recent merger of two large financial
institutions was the integration of data assets
• Streamlining the merger of the two organizations by integrating
the data assets
• Identify ways in which data can be used to strategic advantage
• Organizational Structure & Business Capability Alignment were
critical
• Understanding how data was used across the organization
• Identifying efficiencies & opportunities for collaboration
Business Capabilities
Company A
Business Capabilities
Company B
Common Data Foundation

Aligning Organizational Capability to Data Governance
Organizational Capability, Organizational Structure, and Roles are key to Data Governance and
Data-Centric Organizational Structures
18
Aligning to Organizational Capabilities
e.g. From Plan to Production to Sales & Distribution
Designing Org Structures for Data-Centric Efforts
e.g. Aligning Data Governance to Individual Culture

Data Models
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Conceptual Data Model
• A conceptual data model
provides core definitions of
key data objects.
• It also shows key
relationships between data
objects.
• Even a simple diagram as the
one on the right can tell a
powerful “story”
…. And uncover key business
issues and opportunities.

Logical Data Model
Place
Appear on
Contain
Belong to
Customer
customer identifier
first name
middle initial
last name
description
Product
product identifier
product name
description
Order
customer identifier (FK)
product identifier (FK)
order date
Product Part Combination
product identifier (FK)
part identifier (FK)
Raw Material
material_identifier
Finished Good
finished good identifier
Subassembly
subassembly identifier
Part
part identifier
part name
description
• A logical data model describes
key business rules and
definitions.
• Attributes are typically shown.
• Cardinality specifies additional
detail regarding relationships.
…. The Logical Model defines
additional detail regarding data
entities, attributes, and their
relationships.

Case Study: Data Models by Business Capability
Dataversity Webinar from March 2019
22
In March 2019, Kiewit gave a great case study of
how they used data models for business success.
Catch the replay at: https://www.dataversity.net/das-webinar-
data-modeling-case-study-business-data-modeling-at-kiewit/

Process / Workflow Models
• Process models are a helpful tool for
describing core business processes.
• “Swimlanes” outline organizational
considerations
• Data can be mapped to key business
processes to understand creation & usage
of information.
• They are particularly helpful for areas such
as Master Data Management (MDM)
where process is critical to data
stewardship & integration.
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Identifying key data dependencies in core business processes

Customer Journey Map
• A customer journey map
outlines key phases of the
customer in their “journey”.
• They are similar to a process
model, but with a different
focus & perspective.
• Creating a data overlay is a
helpful way to see the key
data touched at each point
in the journey.
• Journey maps can be
created for other industries
as well, e.g. Student,
Patient, etc.
24

CRUD Matrix
Product
Development
Supply Chain
Accounting
Marketing Finance
Product Assembly Instructions C R
Product Components C R
Product Price C U R
Product Name C U,D
Etc.
25
Create, Read, Update, Delete
• CRUD Matrices shows where data is Created, Read, Updated or Deleted across the various areas of the organization.
• They can be created by department, by system/application, etc.
• This can be a helpful tool in data governance & data quality.

Process Models & CRUD Fit Well Together
• Business Process Models describe key activities within the organization.
• Linking these processes to the data that is Created, Updated, or Deleted (CRUD) is important to
understanding data usage.
Customer Order Account Invoice Product
Receive Customer Order R C C, R
Process Customer Order C,R,U R,U R
Fill Order R,U R,U R,U
Send Invoice R,U R,U C
CRUD Matrix
Business Process Model

Linking Data with Process for Master Data Management
• An international restaurant chain realized through its digital strategy that:
• While menus are the core product that drives their business…
• They had little control or visibility over their menu data
• Menu data was scattered across multiple systems in the organization from supply chain to kitchen prep to marketing,
restaurant operations, etc.
• Menu data was consolidated & managed in a central hub:
• Master Data Management created a “single view of menu” for business efficiency & quality control
• Data Governance created the workflow & policies around managing menu data
• Process Models & Data Mappings were critical
• Business Process Models to identify the flow of information
• CRUD Matrixes to understand usage, stewardship & ownership
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Managing the Data that Runs the Business
Product Creation &
Testing
Menu Display &
Marketing
Supply Chain Point of Sale &
Restaurant Operations

International Pharmaceutical Company
• An international Pharmaceutical company was looking
to make better use of its data to streamline its:
• Clinical Development
• Commercial Processes
• R&D.
• Business alignment was a key first step
• Greater understanding how data was used by and
critical to key business activities
• Created “blueprints” of how the business runs—then
how data maps to that”
• Data models, Process models, & mappings
• Identified opportunities for business efficiencies
28
Business Alignment and IT Strategy
1 Clearly Define & Promote Services
Architecture a stage gate for
every project – Data, Process
models & mapping
2 Align with Business Needs & Capabilities
3 Integrate into Project Governance

Roles & Culture: Pharmaceutical Company
• Business Acceptance: Clinical Scientists
had data models on their office walls
• “Blueprints” describing their clinical
development
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• Architecture team had clear direction
• “Who we are and what we do” clearly articulated to the
business
• Best Practices made processes more efficient
• Governance driving architecture as a “must-have” for each
new initiative.

Technology Trends – the Basics Prevail, but Options Exist
• While a wide range of technologies are in use,
relational data stores make up the majority of
usage.
• Top data sources or platforms include:
• Relational on-premise database: 75%
• Spreadsheets: 70%
• Relational cloud-based database: 56%
• Most organizations are using a multi-platform
approach for a “fit for purpose” paradigm.
• It’s “both and”, not “either or”
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Organizations are using multiple tools for a “fit for purpose” approach with Relational Databases leading the use cases.
* Trends in Data Management, a 2022 DATAVERSITY® Report, by Donna Burbank and Keith Foote

A Cautious Approach to the Future
• Future Trends point to continued focus on
relational databases, with usage shifting
to the Cloud
• Top Initiatives planned in 1-2 years include:
• Relational cloud-based databases: 53%
• Cloud Object Storage / Data Lakes: 36%
• Relational on-premise database: 32%
• And Spreadsheets continue to be a “data
platform” at 30% …sigh…
31
Relational Databases are the main focus for the next 1-2 years, indicating a “back to basics” approach
From Trends in Data Management, a 2022 DATAVERSITY® Report, by Donna Burbank and Keith Foote

System Architecture Diagram
• High-level system architecture diagrams can create a “big picture” of how systems and
their components fit together.
32

Summary
• Enterprise Architecture provides a series of
models and diagrams to describe the
organization to maximize business value
• While the number and diversity of platforms is
increasing it is important to:
• Focus on the business need and application
• Design fit-for-purpose solutions using a number
of interrelated technologies

DATAVERSITY Data Architecture Strategies – 2023 Lineup
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Data Mesh or Data Mess? Separating the Reality from the Hype
• April Master Data Management - Aligning Data, Process, and Governance
• May How do Data Governance & Data Architecture Support Each Other?
• June Why You Need Data Management – Getting Executive Buy-In
• July Artificial Intelligence and Machine Learning – Building the Right Architectural Foundation
• August Data Quality Best Practices (with Nigel Turner)
• September Best Practices in Metadata Management
• October Designing Data for Business Intelligence & Analytics – Where the Star Schema Fits in a
Modern Data Architecture
• December Enterprise Architecture vs. Data Architecture
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Join Us Next Year

Who We Are: Business-Focused Data Strategy
Maximize the Organizational Value of Your Data Investment
In today’s business environment, showing rapid time to value for
any technical investment is critical.
But technology and data can be complex. At Global Data Strategy,
we help demystify technical complexity to help you:
• Demonstrate the ROI and business value of data to your
management
• Build a data strategy at your pace to match your unique culture
and organizational style.
• Create an actionable roadmap for “quick wins”, which building
towards a long-term scalable architecture.
Global Data Strategy’s shares experience from some of the largest
international organizations scaled to the pace of your unique team.
www.globaldatastrategy.com
Global Data Strategy has worked with organizations globally in the
following industries:
Finance · Retail · Social Services · Health Care · Education · Manufacturing
· Government · Public Utilities · Construction · Media & Entertainment ·
Insurance …. and more

Global Data Strategy, Ltd. 2021 www.globaldatastrategy.com
Questions?
Thoughts? Ideas?
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Enterprise Architecture vs. Data Architecture

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