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Does Data Modeling
Have Business Value?
October 13, 2016
Joy Ruff
Product Marketing Manager
Joyce.Ruff@idera.com
2
Agenda
 The value of enterprise data
 Data trends
 Communication gaps
 Common understanding
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
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
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
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
Enterprise Architecture Framework
Enterprise Enablement
BusinessArchitecture
ApplicationArchitecture
TechnicalArchitecture
Data Architecture
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
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
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
12
Creating Value with Data Architecture & Modeling
 Build
• Manage redundancy
• Integrate and rationalize
• Increase quality
 Use & Maintain
• Increase discoverability
• Improve comprehension
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
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
The Need for Common Understanding
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
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
Justifying the Value Investment
 Benefits
• Consistency and standardization
• Improved data quality
• Reduced operating costs
 Results
• Greater reuse of assets
• More informed decision-making
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
Thanks!
Any questions?
You can find me at:
joyce.ruff@idera.com
@jfruff
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
Enterprise Collaboration:
Share Metadata Across Business & IT

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Geek Sync I Does Data Modeling Have Business Value?

  • 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