Slides: Enterprise Architecture vs. Data Architecture

Copyright Global Data Strategy, Ltd. 2020
Enterprise Architecture vs. Data Architecture
Creating a Blueprint for Success
Donna Burbank
Global Data Strategy, Ltd.
June 25th, 2020
Follow on Twitter @donnaburbank
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies

Confidential and Proprietary. Do not distribute without Couchbase consent. © Couchbase 2020. All rights reserved.
DATA
ARCHITECTURE IN
THE CLOUD ERA
Jeff Morris, VP Product & Solutions Marketing
1

2
Confidential and Proprietary. Do not distribute without
Couchbase consent. © Couchbase 2020. All rights reserved.
Where did Our
Customers Go?
…Online
Can we Reduce
Costs?
…Yes
How do we Stay in
Control?
…Choose wisely
What Our
Customers
Want to Know
Today
User Profiles, Interactions,
Catalogs, Sessions & DX
Accelerate Cloud Adoption
In March 2020 Online retail
spending was up 25%
Forrester observes that Only
20% of Enterprise
Applications have moved to
the cloud
Customer behavior has
changed dramatically
Secure, Operate & Service
these customers without
losing control of your assets

Confidential and Proprietary. Do not distribute without Couchbase consent. © Couchbase 2019. All rights reserved. 3
MASSIVELY INTERACTIVE APPLICATIONS
HAVE MASSIVE CHALLENGES
Database sprawl Scale fail Multi-cloud
manageability

Confidential and Proprietary. Do not distribute without Couchbase consent. © Couchbase 2019. All rights reserved. 4
THE COUCHBASE
ARCHITECTURAL ADVANTAGE
NoSQL database architected to remove enterprise pain
Develop with
agility
Manage
with ease
Perform at
any scale
“Couchbase is really the backbone
for everything. Wherever there’s a
piece of data, we try and fit it into
one of the resources they provide.”
Dylan Wilson | Manager of Data Sciences
Becton, Dickinson & Co. (BD)

Couchbase’s NoEQUAL Architectural Differentiation
Developer agility & versatility
• Multi-model: Key Value & JSON documents
• Multi-mode: Memory-first, ACID, operational & analytic workloads
• Programmable: schema flexibility + SQL in N1QL + stack-based SDKs
Performance at any scale
• No hassle scale out – shared-nothing, asynchronous, elastic architecture
• Built-in replication (XDCR)
• Always-on, globally distributed,
edge-to-cloud
Easy to manage
• Workload isolation with
multi-dimensional scaling
• Automatic cluster rebalancing
• Location and deployment agnostic
• Kubernetes & microservices-friendly

The Power of a Flexible JSON Schema
Store data in multiple ways:
• Denormalized single document
• Normalized with references
• Add new values when needed
• Support for binary values
Access Data in multiple ways:
• Direct Key-Value
• SQL querying
• Full-Text Search
• MPP for large, ad-hoc access

Data Access: N1QL = SQL for JSON
SELECT *
FROM users
WHERE users.lastName = “Johnson”
UPDATE users
SET status = “Platinum”
WHERE users.lastName = “Johnson”
DELETE
FROM users
WHERE users.firstName = “Shane”
Create User
Get User
Update User
Delete User
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
INSERT INTO
VALUES
Users (KEY, VALUE)
(
“user::100”,
{
“firstName”: ”Shane”,
“lastName”: ”Johnson”
}
);

Couchbase Multi-mode, Independent Services
• Inverted Indexes
• Language Awareness
• Scoring
Full Text Search
• Fast ingestion, MPP query execution
• Complex queries on large datasets
• Real-time insights for business teams
Analytics Service
• Javascript-based capture & notification
• Stream in and out of Kafka
• Set up custom Change Data Capture rules
Eventing Service
• JSON format allows for easy modification to
Keys, Values and Arrays
• Normalized, denormalized & binary data
• Query from all Couchbase services
Schema Flexibility with JSON Memory-first Data Handling Services
• Query Service: coordinates requests
• Index Service: cache-first then disk
• Data Control: write/retrieve data
• Does the work of multiple database
• SQL for JSON
• Full CRUD support
• Familiar syntax
SELECT, WHERE, JOIN, INSERT, DELETE
N1QL as Familiar as SQL

Sample QA SetupSample Dev Setup
Elastic Scaling Architecture
Sample Production DeploymentNODE 1
Query
Global Index
Data
Analytics
Full Text
Cluster Manager
NODE 2
Eventing
NODE 1 NODE 13
Cluster Manager
Data Full Text AnalyticsGlobal Index Query Eventing
NODE 1
Global IndexQuery
Full Text
Analytics
Data
Cluster Manager
NODE 4
Eventing
Flexible cluster topology adjusts with growing demand

Reliable, Secure & Scalable Deployment
• Transaction management built into each SDK
• Commits across distributed clusters
• Supports system of record uses
Distributed ACID Transactions
• Creates low-latency access for users
• Supports geo-filtering for local regulations
• Migrate & backup hybrid, on-prem & cloud
deployments
Cross-Datacenter Replication (XDCR)
• Kubernetes-based command & control
• Automatically deploy, scale & upgrade nodes
• Fine-grained cluster control & reporting
• AKS, GKS, Azure, Red Hat Openshift
Autonomous Operator Orchestration
• Individually allocate service resources
across clusters based on workloads.
• Maintain predictable resource costs by
closely matching infrastructure to services
Multi-Dimensional Scaling (MDS) Active/Active Clustering
• Auto partitioning & data rebalancing
• Shared-nothing elastic architecture
• Highly available, fault tolerant, distributed
clusters.
• Full-stack security
• From mobile storage, across networks,
through browsers, within clusters and
permanent disks
Enterprise Security

Couchbase Cloud – Horizontally Appealing Use Cases
Catalog and
Inventory Management
Catalogs
• Deliver relevant product
content and a real-time
view of inventory
• Scale to millions of
products and requests
for the latest information
• Serves highly engaged
online audiences
Profile and
Session Management
Personalization
• Create custom experiences in
real time based on aggregate
data from multiple
• Aggregate customer data,
recommendations, user
profiles, session, history data
Customer 360
Single View
• Deliver a consistent, single
view of your data with one
platform
• Improve customer experience
and operational visibility
Digital
Transformations
Offload
• Create transformative digital
experiences by offloading
mainframe, RDBMS systems
• Reduce costs and improve
productivity and agility

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Couchbase consent. © Couchbase 2020. All rights reserved. 13
HYBRID CLOUD /
MULTI-CLOUD
DBaaS
Cloud-agnostic
application deployment
and management
platform that treats cloud
providers like
commodities and enables
you to migrate between
clouds freely.
Couchbase Cloud DBaaS

CUSTOMER’S VIRTUAL
PRIVATE CLOUD
In-VPC Deployment
• Customer-chosen cluster instances
based on your workload and
deployment timing. Change them
easily.
• Pick the perfect instances to run your
workload
• Multidimensional Scaling (MDS) of
individual Couchbase services
matches workload to infrastructure
• Elastic node scaling and rebalancing
for load spikes
• Hybrid-cloud XDCR allows for
workload shifting, backups and easy
data migration

AVAILABILITY ZONE – A AVAILABILITY ZONE – B AVAILABILITY ZONE – C
COUCHBASE NODES
COUCHBASE OPERATOR
COUCHBASE NODES
• Secure
• Encrypted
• Single-tenant
• Best Practices
• Built Using Oss
• Fully - Automated
DATA PLANE MONITORING DATA PLANE MONITORING DATA PLAN MONITORING
COUCHBASE NODES
ELASTIC LOAD BALANCER
PROVISION, MANAGE
AND MONITOR
THE DATA PLANE
SINGLE PANE OF MANAGEMENT
USER MANAGEMENT
ORDERING BILLING
CLUSTER MANAGEMENT
COMPLIANCE AND GOVERNANCE
MUTLI-CLOUD ORCHESTRATOR
DATABASE OPTIMIZER
SECURITY
OPERATIONAL INTELLIGENCE
MONITORING AND METERING
IAM
ADD-ON SERVICES
EVENT MANAGEMENT
• Secure
• Encrypted
• Multi-tenant
• Highly Available
• Fully- Automated
• Self-service
CLOUD
CONTROL
PLANE
DATA PLANE
Couchbase Cloud Architecture
AUTO
SCALING
GROUP
MANAGEMENT
PLANE
CUSTOMER VPC/VNET
COUCHBASE VPC
AUTO
SCALING
GROUP

Create a Couchbase Cloud
Account
1
Create your Cloud
Environment
2
Spin-up a Database Based
on Your Workload
3
Fastest Way to Develop, Launch and Scale Applications
Choose Your Instance Cloud Environment Database Cluster

Choose development and
test clusters with relaxed
support service levels to
save 35%
over production
deployments.
Choose reserved
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Global Data Strategy, Ltd. 2020
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.
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
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
3
This Year’s Lineup

What We’ll Cover Today
4
• 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.

Global Data Strategy, Ltd. 2020 5
A Successful Data Strategy links Business Goals with Technology Solutions
“Top-Down” alignment with
business priorities
“Bottom-Up” management &
inventory of data sources
Managing the people, process,
policies & culture around data
Coordinating & integrating
disparate data sources
Leveraging & managing data for
strategic advantage
Data Governance & Architecture are Part of a Wider Data Strategy
www.globaldatastrategy.com

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
6
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.
7
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.
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

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?

What EA Model Types are in Use?
11
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

Business Motivation Model
12
Corporate Mission Corporate Vision
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.
• Project-level, Data-Centric Drivers
• External Drivers are what you’re
facing in the industry
• Internal Drivers reflect internal
corporate initiatives.
• Project-level, 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
worldwide
• Provides a “heat map” of
usage patterns
• Particularly important for large,
geographically distributed
teams & departments.

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
14
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
15
• 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.

Data Models are a Key Part of Data Governance
16
Data Models are critical to data governance at the conceptual, logical, and physical levels.

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.
19
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.

Aligning Organizational Capability to Data Governance
Organizational Capability, Organizational Structure, and Roles are key to Data Governance and
Data-Centric Organizational Structures
20
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-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.
22
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.
23

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.
24
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
26
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

Data Platform Evolution
Data Technology & Platforms continue to evolve
• 81% are using relational databases on-premises
• 71% are using spreadsheets as a data platform (!)
• Future plans include a wide range of technologies:
• Cloud-based relational databases
• Graph databases
• NoSQL databases
• Big Data platforms
28
While relational databases remain the leading platform, new technologies are being added to the mix.

Current Platform Adoption
• Relational Database still dominate the data
management landscape
• Majority is on-premises
• Some Cloud Adoption
• Spreadsheets still ubiquitous, partly due to
ease of use and increased interest in data
from business users.
29
Relational database still dominate the market, both on premises and Cloud-based

Future Platform Adoption
• Future Plans still include a high percentage
of relational databases, with a higher
percentage of Cloud-based systems.
• A wider distribution of platform usage
indicates the variety of options and fit-for-
purpose solution – one size doesn’t fit all.
30
Future plans still feature relational databases, with a higher focus on Cloud Adoption, and a wider mix of technologies.

System Architecture Diagram
• High-level system architecture diagrams can create a “big picture” of how systems and their
components fit together.
31
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

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
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
33
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.
34
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

White Paper: Trends in Data Management
• Download from www.globaldatastrategy.com
• Under ‘Resources - Whitepapers’
• Also available on Dataversity.net
35
Free Download

Questions?
36
• Thoughts? Ideas?
www.globaldatastrategy.com

Slides: Enterprise Architecture vs. Data Architecture

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