DAS Slides: Enterprise Architecture vs. Data Architecture

Copyright Global Data Strategy, Ltd. 2019
Master Data Management - Aligning Data,
Process, and Governance
Donna Burbank
Global Data Strategy, Ltd.
June 27th, 2019
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Modern Applications Are Fundamentally Different
NoSQL
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TransactionsInteractions
Legacy monolithic
infrastructure no
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Microservices
architecture
supports web,
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match
Transactional Analytical
Confidential and proprietary. Do not distribute without Couchbase consent. © Couchbase 2019. All rights reserved.
Legacy Databases Insufficient
*SQL-compatible

Couchbase Behind Today’s Business-Critical Applications
Application
Customers
Infrastructure ManageabilityPerformance at Scale
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booking, pricing
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<2.5ms
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Global Data Strategy, Ltd. 2019
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 recently awarded the
Excellence in Data Management Award from
DAMA International in 2016.
Donna is also an analyst at the Boulder BI
Train Trust (BBBT) where she provides advice
and gains insight on the latest BI and
Analytics software in the market. She was on
several review committees for the Object
Management Group’s for key information
management and process modeling
notations.
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 two books: Data Modeling for the
Business and Data Modeling Made Simple
with ERwin Data Modeler and is a regular
contributor to industry publications. She can
be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, USA.
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 - on demand Data Modeling at the Environment Agency of England - Case Study
• April 25 - on demand Data Governance - Combining Data Management with Organizational Change
• May 23 - on demand Master Data Management - Aligning Data, Process, and Governance
• June 27 Enterprise Architecture vs. Data Architecture
• July 25 Metadata Management: from Technical Architecture & Business Techniques
• August 22 Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 Self Service BI & Analytics: Architecting for Collaboration
• October 24 Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
3
This Year’s Lineup

Today’s Topic
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-
relationships 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.
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 2013
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.
6
Modeling is important on many levels

Data Modeling for 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?

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.
8
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.
9
TOGAF EA Framework
www.opengroup.org

What EA Model Types are in Use?
10
What Types of Models/Diagrams do you use in your Data/Enterprise Architecture?
From Trends in Data Management, a 2019 DATAVERSITY® Report, by Donna Burbank and Michelle
Knight – due to be released Sept 2019

Conceptual Data Model
• Communication & definition of core data concepts & their definitions
• 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.

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.
13
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

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.
15
Identifying key data dependencies in core business processes

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.
16
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
• BPMN diagrams to identify the flow of information
• CRUD Matrixes to understand usage, stewardship & ownership
18
Managing the Data that Runs the Business
Product Creation &
Testing
Menu Display &
Marketing
Supply Chain Point of Sale &
Restaurant Operations

Developers
Managers
Enterprise Data Management
Part of a Data Strategy is Defining Fit for Purpose Solutions
Operational Data Reporting & Analytics Master & Reference Data Metadata
CRM
Customer X orders
Product Y at 2pm on
Oct 24, 2017 Sales
CRM
ERP
Customer
Care
IoT
Customer X calls
Support at 1pm on
Nov 1, 2017
Inventory consists of x
number of Product X
components on Oct
24, 2017
Supply
Chain
Customer turns on
foot warmer at 11pm
on Oct 30, 2017
Product
Team
Customer
CRM
& other
systems
DW
What were total
sales for Product X
in 2016 by region?
Lake
Operational Reporting
Enterprise Historical Reporting
Analytics & Discovery
What variables
most influence
customer repeat
purchases?
Limited Personal Use
Limited ad hoc analysis
for small data sets.
Not recommended
for enterprise data
management.
UCM
UPM
“Golden Record” for Customer,
Product, etc.
Mary Smith lives on 101 Main ST,
Detroit, MI and has been a
customer since 2011
Product 720 has a product code
of SS720 & a suggested retail
price of $11,000 USD.
Business & Technical Context &
Descriptions
ELT
How many support
calls are currently
open?
Analytics
Team
Managers
Reference Data
Hierarchies
The Sales management reporting
hierarchy is structured as follows.
Valid Return Codes are “X, Y, & Z”
State Codes include MA, MD MI …
Applications
DW Etc
DW Etc
DW Etc
Business
Glossary How is Total Sales
calculated?
What is a Qualified
Lead?
Business
Users
Data
Models
How do we uniquely
identify a customer?
Can a customer have
more than 1 email?
Data
Dictionary
What is this DW table
used for?
The standard length for
customer ID is CHAR(12)
Developers
Data
Lineage
How was this field
calculated?
What will break
downstream if I make a
change?
Developers
Developers
Business
Users
Access

System Architecture Diagram
• High-level system architecture diagrams can create a “big picture” of how systems and their
components fit together.
20
Data Analysis & Discovery – Data Lake Enterprise Systems of Record
Data Governance & Collaboration
Master &
Reference Data
Data Warehouse
Data MartsOperational Data
Security & Privacy
Sandbox
Lightly Modeled
Data
Data
Exploration
Reporting & Analytics
Advanced
Analytics
Self-Service BI
Standard BI
Reports

Current Platform Use
21
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%
Time Series database
Other (please specify)
Semantic technologies
Don’t know
Real-time/streaming database
IoT data
Graph database
Open data
Social Media
Non-relational on-premises database
Media files (videos, photos, etc.)
Non-relational cloud-based database
Big Data platforms (e.g. Hadoop Ecosystem)
XML
Legacy systems (e.g. Mainframe, COBOL, etc.)
JSON
Relational cloud-based database
Packaged applications (e.g. ERP, CRM, etc.)
Spreadsheets
Relational on-premises database
Which of the following data sources or platforms are you currently using? [Select all that apply]
From Trends in Data Management, a 2019 DATAVERSITY® Report, by Donna Burbank and Michelle Knight – due to be released Sept 2019

Future Platform Use
22
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% 50.00%
Other (please specify)
Time Series database
Semantic technologies
Open data
Non-relational on-premises database
Legacy systems (e.g. Mainframe, COBOL, etc.)
IoT data
Real-time/streaming database
Media files (videos, photos, etc.)
Graph database
Social Media
Don’t know
XML
Non-relational cloud-based database
Big Data platforms (e.g. Hadoop Ecosystem)
Spreadsheets
JSON
Packaged applications (e.g. ERP, CRM, etc.)
Relational on-premises database
Relational cloud-based database
From Trends in Data Management, a 2019 DATAVERSITY® Report, by Donna Burbank and Michelle Knight – due to be released Sept 2019

Summary
• Enterprise Architecture provides a series of models and diagrams to describe the organization
• Data models and data-centric models play a key role in any Enterprise Architecture
• 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
• Adding some enterprise architecture components to your projects can help focus on business value

DATAVERSITY Data Architecture Strategies
• January 24 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA)
• April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner)
• May 23 Master Data Management - Aligning Data, Process, and Governance
• June 27 Enterprise Architecture vs. Data Architecture
• July 25 Metadata Management: from Technical Architecture & Business Techniques
• August 22 Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 Self Service BI & Analytics: Architecting for Collaboration
• October 24 Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
24
Join Us Next Month

About Global Data Strategy, Ltd
• Global Data Strategy is an international information management consulting company that
specializes in the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
• Business-Driven: We put the needs of your business first, before we look at any technology solution.
• Clear & Relevant: We provide clear explanations using real-world examples.
• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s
size, corporate culture, and geography.
• High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of
technical expertise in the industry.
25
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

Questions?
26
• Thoughts? Ideas?

DAS Slides: Enterprise Architecture vs. Data Architecture

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