Strategic imperative the enterprise data model

1© 2019 IDERA, Inc. All rights reserved.
STRATEGIC IMPERATIVE: THE ENTERPRISE DATA MODEL
APRIL 16, 2019
Ron Huizenga
Senior Product Manager, Enterprise Architecture & Modeling
@DataAviator

2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2019 IDERA, Inc. All rights reserved.
PREFLIGHT BRIEFING
▪ Information capability
▪ Data environment complexity
▪ Enterprise data model
• Context
• Approach
• Focal point for all data architecture
• Related implementation models
▪ Service Oriented Architecture (SOA)
• Canonical model
▪ Data governance considerations
▪ Wrap-up

INFORMATION CAPABILITY STUDY – HOW ARE WE DOING?
▪ Very few organizations utilize information to its full potential
▪ Deficiencies in technical capability, skills, lacking data culture
▪ Lack of investment in value-driven information strategies
▪ Very few understand how to derive maximum value from information
• This will erode corporate value if not corrected
* Based on 2015 PwC/Iron Mountain study: Seizing the Information Advantage

INFORMATION MANAGEMENT DISPARITY
▪ Misguided Majority – 76%
• Informed but constrained
• Uninformed and ill-equipped
▪ Data seen as a byproduct, or taken
for granted
• Low comprehension of commercial
benefits that can be gained
▪ Constrained by legacy approaches,
regulations
▪ Weak analytic capability, or
• strong analytic capability, lacking
value focus
• Low analytical capacity
▪ Can be overwhelmed by data volume
▪ Data is domain of data architects
▪ IT led rather than business led
▪ “Spreadsheet hell”
▪ Information Elite – 4%
▪ Proactive Action
• Diversify business models
• Improve operating efficiency
• Identify / implement new market
opportunities
▪ Tangible data value
• Linked to organizational KPIs
▪ Exploit data for competitive advantage
▪ Balanced approach between security
and value extraction
▪ Holistic approach
• Governance is part of normal business
▪ Well defined information strategy
• Reflects business objectives
▪ Often in following sectors
• Healthcare, Manufacturing & Engineering

SOME IMPORTANT PERSPECTIVE
“Organizations that do not understand the overwhelming importance of
managing data and information as tangible assets in the new economy will
not survive.”
- Tom Peters
“Your Organization is all about Data,
until it's not about Data"
- Peter Aiken

BUSINESS STAKEHOLDERS’ DATA USAGE
Suspect that business stakeholders
INTERPRET DATA INCORRECTLY
Yes, frequently
14%
Yes, occasionally
67%
No, never
9%
I don’t know
10%
Suspect that business stakeholders make decisions
USING THE WRONG DATA?
Yes, frequently
11%
Yes, occasionally
64%
No, never
13%
I don’t know
12%

DATA MODEL USAGE & UNDERSTANDING
13%
3%
16%
19%
31%
18%
0% 5% 10% 15% 20% 25% 30% 35%
We don’t use data models
Other
Our data team does most data models but
developers also build them as needed
Our database administrators own data modeling
Developers develop their own data models
We have a data modeling team that is
responsible for data models
Completely
understand
20%
Understand
somewhat
60%
Don’t understand
17%
I don’t know
3%
87%
What is your organization’s approach to data modeling?
How well does your organization’s technology leadership team
understand the value of using data models?

Logical Data Lake
DATA ECOSYSTEM
RDBMS
DataIngestion
Approved
Raw Data
Sandboxes
(Data Science)
Raw Transient
Data
Refinery Refined
Data
Trusted
Data
MDM
Store
Self-serve
Analytics &
Reporting

MAPPING THE DATA
▪ Data Models
• Conceptual
• Logical
• Physical
• Dimensional
• Enterprise/Canonical
▪ Visual Data Lineage
▪ Enterprise Data
Dictionaries
• Naming Standards
• Attachments
▪ Metadata Repository
• Business Glossaries

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

ENTERPRISE DATA MODEL
▪ Long-term focus (ongoing)
▪ Investment
▪ Basis for communication
▪ Foundation for standardization
▪ Continually evolving - incrementally
▪ Framework for all enterprise data assets
▪ Business focused
▪ Strategic imperative
▪ Requires both business and data modeling
expertise
▪ A rationalized, intelligent design
▪ Platform independent
▪ Common sense and practical
▪ A project
▪ Operational expense
▪ Just a pretty picture
▪ Incompatible with agile
▪ Ivory tower artefact
▪ Application/business area specific
▪ For IT use only
▪ Waste of time
▪ Something that can be created by
developers or junior modelers
▪ Documentation after the fact
▪ For a specific database
▪ An academic exercise
What it is What it is NOT

ALIGN ENTERPRISE SUB-MODELS TO BUSINESS AREAS
Supply Chain
Example
Corporate
Structure
Document
Management
Facilties and
Equipment
Equipment
Object
Facilities
Infrastructure
Maintenance
Planning
Operations
Inventory/Linefill
Sampling
Transit Metrics
Order Processing
Change Requests
Nominations and
Splits
Nominations -
Basic
Nominations -
Enhancements
Nominations -
Scenarios
Planning and
Scheduling
Capacity
Management
Scheduling
Rules Framework
Planning
Portal Framework
Portal
Permissions
Products Supply Contracts

THE NEED FOR COMMON UNDERSTANDING

THE KEYS TO COMPREHENSION
• Business Data Objects
• Business Entities
• Relationships
• Business rules
• Canonical Model
Enterprise
Data Model
• Classification Metadata
• Data Types
• Domains
• Naming Standards
• Security Properties
Enterprise
Data
Dictionary
• Terms/Definitions
• Business area glossaries
• Technical glossaries
• Governance Catalog/Policies
• Reference & Master Data Catalog
Business
Glossaries

Consult
Accountable
Responsible
Inform

ENTERPRISE DATA MODEL: GOVERNANCE METADATA

LINK ENTITY INSTANCES
Enterprise Data Model
Implementation Model 1 Implementation Model 2
Purchase Order Header
Purchase Item
Customer Address
Address
Sales Order Header
Manufacture Item
City
Country
Sales Order Line
Supplier Address
Purchase Order Line
Vendor
Supplier
SuppliersProduct
Item
Part
Customer
Client CustomersProvince State
State / Province
Universal Mappings - Repository

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

DATA LINEAGE (TYPICAL ANALYTICS/BI ETL)

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

DATA FLOW AND LINEAGE (POINT TO POINT INTERFACE)

SERVICE ORIENTED ARCHITECTURE (SOA)
▪ An architecture design pattern based on distinct components providing services
to other components
• Application functionality
• Data services
▪ Independent of
• Any specific vendor
• Any specific product
• Any specific technology

CANONICAL MODEL
▪ SOA design pattern that uses
standardized data model(s) across
all data services
▪ Based on Enterprise Data Model
▪ Standardized and consistent data
elements
▪ Standardized messages
• And payload
• All transformations based on
canonical schema
• From data sources
• To data subscribers
Enterprise Data
Model
Canonical
Model/Schema
Canonical
Messages

SERVICE ORIENTED ARCHITECTURE WITH CANONICAL MODEL
Managed Service Connectivity
Change & ControlMediationConnection
Business Process
Collaborative Team Workflow
Event DetectionProcess Orchestration Business Activity Monitoring
Business Intelligence Rules Management
Service Consumers
Mash UpsComposite ApplicationsPortals
Service Enablement
StandardsContracts
Deployment Hosting
Configuration Endpoints
Data Services
Integration
Services & ETL
Distributed
Query
Data Replication
EII
Messaging
Legacy
Adaptation
Protocol & API
Adaptation
Durable &
Reliable Delivery
Quality of Service
Identity & Authorization Management
Service Oriented
Management &
Governance
Registry
SLA
Management
Health
Monitoring
Policy
Enforcement
Canonical Model
&
Canonical Message
Based

DATA LINEAGE: SOA PUBLISH & SUBSCRIBE

INFORMATION IN THE RIGHT PLACE, AT THE RIGHT TIME
Data
Governance
Metadata
Management
Data
Quality
Enterprise
Data Model
Business
Concepts
+ Rules
Data
Integration
(SOA)
Data
Rationalization
• A major investment
• With major returns
An Enterprise Data Model is the focal point for comprehension of
information, in context, facilitating effective communication throughout
the organization.

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

Corporate
Accounting
Customer
Service
Human
Resources
Marketing Sales
Supply
Chain
Governance
policies
GDPR
HIPAA
PCI
PIPEDA
SOX
MDM
catalog
Reference
Data
Master Data
BUSINESS GLOSSARIES

GLOSSARY & TERMS

GOVERNANCE POLICY CATALOG

HIPAA: SPECIFIC POLICY STATEMENTS

HIPAA: RELATED POLICY STATEMENTS FOR THE OBJECT

MODEL DRIVEN SECURITY ALERTS

REFERENCE DATA SET LIBRARY

SPECIFIC REFERENCE DATA SETS (LINK TO SOURCE)

FINAL THOUGHTS: ENTERPRISE DATA MODEL BENEFITS
▪ Strategic business alignment
• Improved understanding
• Improved communication
▪ Manages complexity
• Context for all organizational data assets
• Consistency for data assets
▪ Lower implementation costs
• Reduced rework
▪ A framework for service oriented architecture
• Canonical model
• Data Services
▪ A foundation for enterprise data governance

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

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