1. Enterprise Data Management
By Bhaven Chavan
bhaven2001@yahoo.com
6/23/2016
Confidential | 2016
DISCLAIMER
Note: It is understood that the material in this presentation is intended for general information only and should
not be used in relation to any specific application without independent examination and verification of its
applicability and suitability by professionally qualified personnel. Those making use thereof or relying thereon
assume all risk and liability arising from such use or reliance.

2. 6/23/2016
Definition
Enterprise Data Management is:
 Removing organizational data issues and conflicts by defining accurate, consistent and transparent content
 Ability to create , integrate, disseminate and manage data for all enterprise applications
 Requiring timely and accurate data delivery
 Defining structured data delivered strategy- from data producer to data consumer
 It goes hand-in-hand with IT Workable Data Governance practices and collaboratively helps in establishing
governance across the enterprise.
 It acts like framework for leadership, organizational structure, business process, standards, practices etc.
6/23/2016
Confidential | 2016

3. Current State & Why Now is the right time to address the
challenge
3
• Accurate, Consistent and Transparent content
• Ability to create , integrate, disseminate and manage
data for all enterprise applications
• Timely and Accurate data delivery
• A structured data delivered strategy- from data producer
to data consumer
Today’s Design is not addressing
the foundational needs of
enterprise data
But we are creating a new
reference architecture for
applications which should take
these needs into consideration
PL/SQL and Trigger base
integration
LOG, PQRY &
PRIMEUNIV
MINDRPT
GO
CRO
Export/Import
AIM
PQRY
PRIMEUNIV
CRO
Warehouse
DI
RDS
Affiliate
DI
RDS
Asset
DI
Salesforce
CRM
DI
DI
GOQRY
Oracle
Export/Im
port
DI
Report External Data
DI
CDB
DI
DI
CP
Data Marts
MVs
Report
Report Presentation
Data Layer
Report
Report
Report
Report
6/23/2016
Confidential | 2016

4. 6/23/2016
Important Enterprise Data Management Use Cases
1 Produce True Insights from
True Data
•Accuracy in search and match
•Reduce risk of errors
•Operational
•Analytical
•Single view of trusted data
•Reveal hidden relationships
and patterns
•360-degree enterprise view
of customer/consumer
•Gaines consumer viewership
opportunity
2 Leverage enterprise data
analytics more fully and reliably
•Performance and Scalability
•Real-time delivery of insights
•Consumer behavior
•Predictability
•Trends
•Competitiveness
•Over time scalability
•Minimize downtime
•Improved user experience
•Lower IT costs and expansion
efforts
3 Enable wider use of
enterprise data and analytics
for speed and innovation
•Pre-built Services and Data
Model
•Unify disparate sources of
data
•Extensibility
•Accelerate implementation
•Rapid MDM integration with
an increasing number of data
repositories
4 Evolve business overtime
•Deployment Flexibility
•Support strategic initiatives
•Move across implementation
styles with a single solution
•Accelerate implementation
time
•Increase time to value
Architecture Data Principles
• Accurate, Consistent and Transparent content
• Ability to create , integrate, disseminate and manage data for all enterprise applications
• Timely and Accurate data delivery
• A structured data delivered strategy- from data producer to data consumer
6/23/2016
Confidential | 2016

5. 6/23/2016
 Appropriately define and understand the enterprise data categories within organization
 Understand the current state of data architecture
 Define a future state enterprise data architecture based on founding data management principles
which begins with the “Enterprise Master Data Lineage Architecture”
 Review current application design and understand how the “enterprise data needs” will be addressed
and produce a gap analysis as needed
 Meet Architecture team to provide feedback and seek out methods to address enterprise data
concerns
The Approach
6/23/2016
Confidential | 2016

6. 6/23/2016
Enterprise Data Categories
 Reference Data:
 Is data that defines the set of permissible values to used by other critical business objects or entities. E.g.
Country, Language, Asset Type, Customer type, Customer role etc.
 Master/Critical Data:
 The critical data of a business, such as Asset, Customer, Address etc. that drives other data.
 Data that are shared and used by several of the applications that make up the system/application.
 It fall generally into four groupings:
• People: there are customer, employee, and salesperson.
• Things: there are product, part, store, and asset.
• Concepts: there are things like contract, warrantee, and licenses.
• Places: there are office locations and geographic divisions.
 Less volatile than transactional data.
 It holds key principle of reusability across the enterprise.
6/23/2016
Confidential | 2016

7. 6/23/2016
Enterprise Data Categories Continue….
 Transactional Data:
 A organization’s operations are supported by applications that automate key business process.
 It trends to be more volatile than master data.
 Analytical Data:
 It describes an enterprise’s performance.
 It supports company’s decision making process.
6/23/2016
Confidential | 2016

8. 6/23/2016
Producer
Trusted
Master
Data
Govern
Share
Cleanse
Consolidation
Consumer
High Level Enterprise Master Data Lineage Architecture
Click For Conceptual View
6/23/2016
Confidential | 2016

9. 6/23/2016
OLTP 2
OLTP DB
MDM
Asset
Data Acquisition
Layer
Customer Users Address
MDM Data Layer
Time Asset Customer Address
MDM Dimension Data Layer
Country Language
Reference Data Layer
Master Data Push
Reference Data Push
Master Data Pull/Post
Reference Data Pull
Reference Master
Data Pull
Reference Master
Data Push
• MDM represents the business objects that are shared across more than one transactional
application.
• It represents the business objects around which the transactions are executed.
• It represents the key dimensions around which analytics are done.
• Master data creates a single version of the truth about these objects across the
operational and analytical IT landscape.
Conceptual View of Enterprise MasterData Lineage Architecture
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OLTP 1
Click For Logical View
Time
Zone
Other
References
6/23/2016
Confidential | 2016

10. 6/23/2016
Logical View of Enterprise Master Data Lineage Architecture
OLTP Asset DB
MDM
Asset
Data Acquisition
Layer
Customer Users Address
MDM Data Layer
Time Asset Customer Address
MDM Dimension Data Layer
Country Language Time
Zone
Reference Data Layer
Other
References
OLTP 1
Information Exchange Hub (a)
Reference
Data pull
OLTP 2
OLTP Asset Extension
DB
Data Acquisition
Layer
Master Data
Push for
MDM
Master
Data Pull
Ref. Data
Push
Master Data
Push For
Downstream
OLTP 3
OLTP Operational DB
Data Acquisition
Layer
Data Lake
UDL DB
Analytical
Data layer
UDL DB
Operational
Data
Dimensional
Data Push For
Analytics
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11. 6/23/2016
Logical View of Enterprise Master Data Lineage Architecture
ForReferenceData…..
OLTP Asset DB
Data Acquisition
Layer
OLTP 1
Reference Data Exchange Hub (a)
Country, Language,..etc.Reference
Data pull
LOTP 2
OLTP Asset Extension
DB
Data Acquisition
Layer
Ref. Data
Push
OLTP 3
OLTP Operational DB
Data Acquisition
Layer
Data Lake
UDL DB
Analytical
Data layer
UDL DB
Operational
Data
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MDM
Asset Customer Users Address
MDM Data Layer
Time Asset Customer Address
MDM Dimension Data Layer
Country Language Time
Zone
Reference Data Layer
Other
References
6/23/2016
Confidential | 2016

12. 6/23/2016
Logical View of Enterprise Master Data Lineage Architecture
ForMasterData…..
OLTP Asset DB
RDS
Affiliate
Data Acquisition
Layer
Asset Party Rights
MDM Data Layer
Time
Broadcast
Asset Party Rights
MDM Dimension Data Layer
Country Language DMO
Reference Data Layer
Party
Role
PAM
Information Exchange Hub (a)
PUMA
OLTP Asset Extension
DB
Data Acquisition
Layer
Master Data
Push for
MDM
Master
Data Pull
Master Data
Push For
Downstream
MIND
OLTP Operational DB
Data Acquisition
Layer
Data Lake
UDL DB
Analytical
Data layer
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H
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UDL DB
Operational
Data
OLTP Asset DB
Data Acquisition
Layer
OLTP 1
Master Data Exchange Hub (a)
Asset,Customer,..etc.
OLTP 2
OLTP Asset Extension
DB
Data Acquisition
Layer
Master Data
Push for DM
Master
Data Pull
Master Data
Push For
Downstream
OLTP 3
OLTP Operational DB
Data Acquisition
Layer
Data Lake
UDL DB
Analytical
Data layer
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a
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a
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D
a
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a
H
u
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UDL DB
Operational
Data
Dimensional
Data Push For
Analytics
MDM
Asset Customer Users Address
MDM Data Layer
Time Asset Customer Address
MDM Dimension Data Layer
Country Language Time
Zone
Reference Data Layer
Other
References
6/23/2016
Confidential | 2016

13. Intermission
Q&A
13
Thank You!
bhaven2001@yahoo.com
6/23/2016
Confidential | 2016