Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.

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DATA ARCHITECTURE:
THE FOUNDATION FOR ENTERPRISE ARCHITECTURE AND GOVERNANCE
FEBRUARY 5, 2019
Ron Huizenga
Senior Product Manager, Enterprise Architecture & Modeling
@DataAviator

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PRE-FLIGHT BRIEFING
▪ Business challenges
• “Lemonade Logistics”
▪ Why do we need architecture?
▪ What is it?
• Enterprise Architecture (EA) disciplines
▪ Data value and lifecycle
▪ How do we put it all together?
• Integrated modeling
• Data architecture
• Business architecture
• Technical architecture
• Collaborative data governance
▪ Q&A

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LEMONADE LOGISTICS

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AND ALL I WANTED WAS ….

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WHY DO WE NEED ARCHITECTURE?
Evolution:
▪ 38 years of construction
▪ 147 builders
▪ No Blueprints
▪ No Planning
Result:
• 7 stories
• 65 doors to blank walls
• 13 staircases abandoned
• 24 skylights in floors
• 160 rooms, 950 doors
• 47 fireplaces, 17 chimneys
• Miles of hallways
• Secret passages in walls
• 10,000 window panes (all bathrooms are fitted with windows)

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IN A TYPICAL ORGANIZATION WE WILL FIND:
▪ Proliferation of disparate systems
▪ ERP, mismatched departmental solutions
▪ SAAS (externally controlled and managed), cloud
▪ Obsolete legacy systems
▪ Poor decommissioning strategy
▪ Point-to-point interfaces
▪ Data warehouse, data marts, ETL …
▪ Multiplied n times due to mergers, acquisitions

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ENABLE DATA GOVERNANCE WITH ENTERPRISE ARCHITECTURE

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ARCHITECTURE DEFINITIONS
▪ Enterprise Architecture
• A defined practice for conducting enterprise analysis, design, planning, and
implementation, using a comprehensive approach at all times, for the
successful development and execution of strategy.
• Enterprise architecture applies architecture principles and practices to guide
organizations through the business, information, process, and technology
changes necessary to execute their strategies.
▪ Data Architecture
• Data architecture is composed of models, policies, rules or standards that
govern which data is collected, and how it is stored, arranged, integrated, and
put to use in data systems and in organizations.

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ARCHITECTURE DEFINITIONS
▪ Business Architecture
• “A blueprint of the enterprise that provides a common understanding of the
organization and is used to align strategic objectives and tactical demands.”
• From the Business Architecture Body of Knowledge
▪ Application Architecture
• Describes the behavior of applications used in a business, focused on how
they interact with each other and with users. It is focused on the data
consumed and produced by applications rather than their internal structure.
Applications are mapped to business functions.
▪ Technical Architecture
• Computer system architecture 'layer' which defines and specifies the
interfaces, parameters, and protocols used by product architecture and
system architecture layers.

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ER/STUDIO ENTERPRISE TEAM EDITION

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MODELS ARE CRUCIAL! (ZACHMAN FRAMEWORK)
What How Where Who When Why
Contextual Material List Process List
Geographical
Locations List
Organizational
Unit & Role List
Event List Goal List
Conceptual
Entity
Relationship
Model
Process Model Locations Model
Organizational
Unit & Role
Relationship
Model
Event Model Goal Relationship
Logical
Logical Data
Model
Process
Diagarm
Locations
Diagram
Role
Relationship
Diagram
Event Diagram Rules Diagram
Physical
Physical Data
Model
Process
Function
Specification
Location
Specification
Role
Specification
Event
Specification
Rules
Specification
Detailed Data Details Process Details Location Details Role Details Event Details Rules Details

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DATA MODELS
▪ Conceptual
• Technology-neutral, high-level layout of entities and their relationships
• Used to establish contextual consensus among modeling domain
stakeholders
▪ Logical
• Adds detail to conceptual models in a technology-neutral rendering
• More context on the entity relationships, including terms and
definitions
▪ Physical
• Tied to a particular database implementation
• Includes implementation-level details such as indexing and federation
ABSTRACTION
ELABORATION

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DATA VALUE CHAIN
Data
Data is the representation of
facts as text, numbers,
graphics, images sound or
video
Information
Definition
Format
Timeframe
Relevance
=+
Information is Data in
context. Without context,
data is meaningless.
Knowledge
Patterns & Trends
Relationships
Assumptions
=+
Knowledge is information in
perspective, integrated into
a viewpoint based upon the
recognition and
interpretation of patterns
(i.e. trends) formed with
other information and
experience.

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DATA - LIFECYCLE
▪ Describes how a data element is created,
read, updated, deleted (CRUD)
▪ Many factors come into play
• Business rules
• Business processes
• Applications
▪ There may be more than 1 way a particular
data element is created
▪ Need to model:
• Business process
• Data lineage
• Data flow
• Integration
• Include Extract Transform and Load (ETL) for
data warehouse/data marts and staging areas
Create/Collect
Classify
Store
Use/ModifyShare
Retain/Archive
Destroy

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DATA MODELING CONTEXT
Implementation Models
Categories
CustomerCustomerDemo
CustomerDemographicsCustomers
Employees
EmployeeTerritories
Order Details
Orders
Products
Region
Shippers
Suppliers
Territories
Data Warehouse
Enterprise Model(s)
Enterprise Data Dictionaries
ConceptualModels

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ENTITY INSTANCES
Repository
Universal Mappings

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UNIVERSAL MAPPINGS
▪ Ability to link “like” or related objects
• Within same model file
• Across separate model files
▪ Entity/Table level
▪ Attribute/Column level

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DATA MODEL CONSTRUCTS
▪ Full Specification
• Logical
• Physical
▪ Persistence Boundaries
• Business Data Objects
▪ Descriptive metadata
• Names
• Definitions (data dictionary)
• Notes
▪ Implementation characteristics
• Data types
• Keys
• Indexes
• Views
▪ Business Rules
• Relationships (referential
constraints)
• Value Restrictions (constraints)
▪ Security Classifications + Rules
▪ Governance Metadata
• Master Data Management classes
• Data Quality classifications
• Retention policies

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ATTACHMENTS (METADATA EXTENSIONS)

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DATA LINEAGE (ANALYTICS/BI)

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DATA FLOW AND LINEAGE (ADVANCING THROUGH LIFECYCLE)

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BUSINESS ARCHITECTURE
Capabilities
Organization
Value
Streams
Information
Products,
Services
Metrics,
Measures
Projects,
Initiatives
Vision,
Strategy,
Tactics
Policies,
Rules,
Regulations
Customers,
Partners,
Competitors
Decisions,
Events
Who? Where?
Why?
What?
How?
When?
How well?
Who? Where?
Why?
What?
What?
How?

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BUSINESS DECOMPOSITION

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BUSINESS DECOMPOSITION: PARTIAL VIEW

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HIGH LEVEL PROCESS CONTEXT

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BASIC PROCESS

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HOW IS THE DATA USED IN BUSINESS PROCESSES?

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EXPANDED PROCESS DETAIL (NEXT LEVEL)

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APPLICATION & TECHNICAL ARCHITECTURE - MODELS
Use Case
sd Interaction
User ATM Clearinghouse Bank
2.1.1: Eject Card2.1.1: Eject Card
1: Insert card1: Insert card
2.1: Reject Card2.1: Reject Card
1.1: Verify Card1.1: Verify Card
1.1.1: Verify Account1.1.1: Verify Account
2: Account Invalid2: Account Invalid
Sequence State
Also:
Activity
Class
Communication
Component
Composite Structure
Deployment

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ENABLING GOVERNANCE
Data
Governance
Data
Architecture
Data Modeling
& Design
Data Storage
& Operations
Data Security
Data
Integration &
Interoperability
Documents &
Content
Reference &
Master Data
Data
Warehousing
& Business
Intelligence
MetaData
Data Quality

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APPROACH AND UNDERLYING ARCHITECTURE ARE EVERYTHING!
▪ Metadata Repository only
• Metadata import
• Metadata Catalog (without visual
models)
• Text search & lookup
• Like the “Flat Earth Society”
▪ Fully integrated metadata and
visual models (ER/Studio)
• Global perspective & focal point for:
• Data Models, Business Process
Models
• Visual Data Lineage
• Metadata, Policies, Reference Data

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BUSINESS GLOSSARIES
▪ Alignment to functional areas
▪ Child glossaries inherit a subset of
parent terms
▪ No limit to hierarchy level

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ENABLING KNOWLEDGE: BUSINESS GLOSSARY INTEGRATION

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GOVERNANCE POLICY CATALOG

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SPECIFIC REGULATION (HIPAA)

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HIPAA: SPECIFIC POLICY STATEMENTS

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MODEL DRIVEN SECURITY ALERTS

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HIPAA: RELATED POLICY STATEMENTS FOR THE OBJECT

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REFERENCE DATA SET LIBRARY

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SPECIFIC REFERENCE DATA SETS (LINK TO SOURCE)

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REFERENCE DATA: LINKED WORKBOOK EXAMPLE

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ER/STUDIO ENTERPRISE TEAM EDITION:
INTEGRATED MODELING, ENTERPRISE ARCHITECTURE, GOVERNANCE COLLABORATION PLATFORM
Enterprise Data
Dictionaries
Logical & Physical Data Models
Dimensional Models
Visual Data Lineage
Conceptual Data Models
Business Process Models
Goals &
Strategies
Applications
Business
Units
Business
Rules
Stewards
Business
Glossaries
Business
Concepts
Reference
Data Sets
Policies
Alerts &
Notifications
Security
Follow
Capability
Discussion
Threads
Data
Sources

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POST-FLIGHT DEBRIEF
▪ Business is becoming increasingly complex
▪ Enterprise architecture is essential to decipher complexity
• Data – Process -- Technology
▪ Data is a fundamental building block of every organization
• Rooted in the past
• A key indicator of the present
• A strategic asset of the future
▪ Data architecture is the foundation for enterprise architecture and governance
• All things in the organization are represented by data
▪ We must leverage data and unlock the value chain
• Data – Information – Knowledge
• Knowledge provides strategic advantage
▪ Business architecture provides the context of how an organization works and allows it to adapt
• Business enablement
• Goals and strategies
• Who, what, where, when, why?
▪ Visual modeling is more important than ever before!
• Data modeling, process modeling, data lineage, metadata
▪ Governance helps us to comprehend and manage all of it.

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THANKS!
Any questions?
You can find me at:
ron.huizenga@idera.com
@DataAviator