You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/8WsS50A5fZZ Data modeling is an essential component of enterprise architecture that is often overlooked or underestimated in importance. Whether dealing with a single database or a combination of multiple platforms in a complex environment, data modeling provides meaning and value to the business and can serve as the foundation for a data governance or master data management initiative. Join IDERA and Joy Ruff to learn why data modeling matters for building your enterprise architecture. About the Presenter: Joy is the product marketing manager for ER/Studio, IDERA’s flagship data modeling and architecture platform, plus several database management and security products. With nearly 25 years of experience in high-tech hardware and software, Joy enjoys communicating product value to customers.

1. Does Data Modeling
Have Business Value?
October 13, 2016
Joy Ruff
Product Marketing Manager
Joyce.Ruff@idera.com

2. 2
Agenda
 The value of enterprise data
 Data trends
 Communication gaps
 Common understanding

3. 3
Value of Enterprise Data
“Business executives and IT managers
are increasingly referring to
information as one of their
organization’s most critical and
strategic corporate assets. Certainly
there is a sharp rise in enterprises
leveraging information as a
performance fuel, competitive
weaponry and a relationship
adhesive.”

4. 4
Keeping pace with the rapid growth of data, change and compliance
Evolving Database
Ecosystems
Volume, Velocity,
Variety
Agile Development
Cycles
Maximizing IT
Infrastructure
ComplianceLimited
Resources
Database Professionals Need the Right Tools

5. 5
Enterprise Data Trends
Increasing volumes,
velocity, and variety of
Enterprise Data
30% - 50% year/year
growth
Decreasing % of
enterprise data which is
effectively utilized
5% of all Enterprise data
fully utilized
Increased risk from data
misunderstanding and
non-compliance
$600bn/annual cost for
data clean-up in U.S.

6. 6
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%

7. 7
Enterprise Architecture Framework
Enterprise Enablement
BusinessArchitecture
ApplicationArchitecture
TechnicalArchitecture
Data Architecture

8. 8
Complex Data Landscape
 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 …

9. 9
Usability: Design –> Metadata
 Discovery
• Where do I find Customer information?
 Comprehension
• What does it mean? Is “11” good or bad?
 Compliance
• Can I share this part of the Customer record?

10. 10
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…
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?

11. 11

12. 12
Creating Value with Data Architecture & Modeling
 Build
• Manage redundancy
• Integrate and rationalize
• Increase quality
 Use & Maintain
• Increase discoverability
• Improve comprehension

13. 13
Types of Models
 Conceptual
• Technology-neutral, high-level layout of entities and their relationships
• Used to establish contextual consensus among modeling domain
stakeholders
• Usually owned by information workers
 Logical
• Adds detail to conceptual models in a technology-neutral rendering
• More context on the entity relationships, including terms and definitions
• Used by data architects and application developers
 Physical
• Tied to a particular database implementation
• Includes implementation-level details such as indexing and federation
• Usually created and maintained by systems architects and administrators

14. 14
Addressing Complexity through Models
 Reverse engineering: wide variety of platforms including Big
Data
 Multi-level sub-models: allow business decomposition
 What and where?
• Naming standards
• Universal mappings
 Document and define
• Metadata extensions (attachments)
• Business glossaries
 Data in context: business processes
 Data lineage
 Repository, collaboration & publishing

15. 15
The Need for Common Understanding

16. 16
Apply Meaning with Business Glossaries
 Maximize understanding of the core business concepts and
terminology of the organization
 Minimize misuse of data due to inaccurate understanding of the
business concepts and terms
 Improve alignment of the business organization with the
technology assets (and technology organization)
 Maximize the accuracy of the results to searches for business
concepts, and associated knowledge

17. 17
Functional Comparison of Excel & Visio
Microsoft Excel and Visio Professional Data Modeling Tool
Not intuitive as data modeling tools – not
for this function
Extremely intuitive and easy to use as a data
modeling tool, for new users and very
data management professionals
No rules to guide the user through a modeling
process, and no error handling
Provides many integrated tools for rule-based
model development, error handling, logical and
physical data modeling
Provide mostly static pictures of data models – no
code-generation capabilities and no reporting
Includes interactive capabilities for the
of sophisticated, complex data models
No support for data dictionary or the ability to re-
data models – extremely difficult to develop
in a consistent fashion
Users can easily share, re-use, re-purpose, and
reverse engineer data models, improving data
and compliance through standardization and
collaboration

18. 18
Justifying the Value Investment
 Benefits
• Consistency and standardization
• Improved data quality
• Reduced operating costs
 Results
• Greater reuse of assets
• More informed decision-making

19. 19
Summary
 Enterprise architecture is vital to the
business
• Defines alignment with business strategy
• Builds on a data architecture foundation
• Drives business value from IT solutions and assets
• Brings order from chaos
 A robust and comprehensive data
architecture and modeling program is the
foundation for:
• Compliance
• Data governance
• Master data management
• Business intelligence and analysis
Enterprise
Enablement
Business
Architecture
Application
Architecture
Technical
Architecture
Data Architecture

20. 20
Thanks!
Any questions?
You can find me at:
joyce.ruff@idera.com
@jfruff

21. 21
ER/Studio Enterprise Team Edition
 Collaborate to capture corporate knowledge
and establish consistent business terms
 Provide access to models and metadata
across the organization
 Automate and document model deployments
Collaboration, coordination and automation for
high-performance teams

22. 22
Enterprise Collaboration:
Share Metadata Across Business & IT