enterprise data management

Enterprise Data Management

By Bhaven Chavan

[email protected]

6/23/2016

Confidential | 2016

DISCLAIMERNote: It is understood that the material in this presentation is intended for general information only and shouldnot be used in relation to any specific application without independent examination and verification of itsapplicability and suitability by professionally qualified personnel. Those making use thereof or relying thereonassume all risk and liability arising from such use or reliance.

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DefinitionEnterprise 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.

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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

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Export/Import

AIM

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RDSAffiliate

DI

RDSAsset

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SalesforceCRM

DI

DI

GOQRY

Oracle Exp

ort/Im

port

DI

Report External Data

DI

CDB

DI

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CP

Data MartsMVs

Report

Report PresentationData Layer

Report

Report

Report

Report

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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

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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

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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.

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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.

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Producer

Trusted

Master Data

Govern

Share

Cleanse

Consolidation

Consumer

High Level Enterprise Master Data Lineage Architecture

Click For Conceptual View

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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 Master Data Lineage Architecture

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OLTP 1

Click For Logical View

Time

ZoneOther

References

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Logical View of Enterprise Master Data Lineage Architecture

OLTP Asset DB

MDM

Asset


Customer Users Address

MDM Data Layer



Country Language Time

Zone


OtherReferences

OLTP 1

Information Exchange Hub (a)Reference Data pull

OLTP 2

OLTP Asset ExtensionDB


Master Data Push for

MDM

Master Data Pull

Ref. Data Push

Master Data Push For

Downstream

OLTP 3

OLTP Operational DB


Data Lake

UDL DB

Analytical Data layer

UDL DB

Operational Data

Dimensional Data Push For

Analytics

Operational

Data

Hub

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Logical View of Enterprise Master Data Lineage ArchitectureFor Reference Data…..

OLTP Asset DB


OLTP 1

Reference Data Exchange Hub (a)Country, Language,..etc.Reference

Data pull

LOTP 2



Ref. Data Push

OLTP 3

OLTP Operational DB


Data Lake

UDL DB


UDL DB

Operational Data

Operational

Data

Hub

MDM

Asset Customer Users Address

MDM Data Layer




Zone


OtherReferences

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Logical View of Enterprise Master Data Lineage ArchitectureFor Master Data…..

OLTP Asset DB

RDS

Affiliate


Asset Party Rights

MDM Data Layer

Time

BroadcastAsset Party Rights


Country Language DMO


Party

Role

PAM

Information Exchange Hub (a)

PUMA



Master Data Push for

MDM

Master Data Pull


Downstream

MIND

OLTP Operational DB


Data Lake

UDL DB


Information

Hub

UDL DB

Operational Data

OLTP Asset DB


OLTP 1

Master Data Exchange Hub (a)Asset,Customer,..etc.

OLTP 2



Master Data Push for DM

Master Data Pull


Downstream

OLTP 3

OLTP Operational DB


Data Lake

UDL DB


Operational

Data

Hub

UDL DB

Operational Data

Dimensional Data Push For

Analytics

MDM

Asset Customer Users Address

MDM Data Layer




Zone


OtherReferences

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Intermission

Q&A

13

Thank [email protected]

6/23/2016

Confidential | 2016

enterprise data management

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