www.magnifictraining.com - "SAP MDM(MASTER DATA MANAGEMENT)" Online Training contact us:info@magnifictraining.com or +919052666559 By Real Time Experts from Hyderabad, Bangalore,India,USA,Canada,UK, Australia,South Africa. SAP MDM CONTENT MDM Overview – Introduction - Data Manager Data Modeling – Console - Import Manager - Qualified Table - Hierarchies - Taxanomy Advanced Features – Import Manager Advanced - Expressions - Matching Mode - Workflow - Relationships Outputs - Syndicator - MDM Automation - Multilingual Support IT and Business Scenarios - Master Data Harmonization - Rich product Content Management - Global data synchronization - Customer data integration XI integration SAP MDM CONTENT 2 SAP MDM Application Architecture Overview of MDM Repository Tables/Fields/Taxonomies/Hierarchy/Qualifiers MDM data modeling MDM console Starting and stopping MDM console Mounting and un Mounting MDM server Connecting to MDM server. Creating MDM repository. Creating Different Tables. Creating Different Fields. Working with users, roles, remote system and ports. MDM data Manager Different modes. Search techniques Assignments and validations Protect, checkin and checkout. Matching Qualifed table Hirearchy table Taxonomy table MDM import Manager Importing different source data(Excel, Access and xml) Different operations on source table. Source mapping and value mapping. Matching of records. Creating Maps MDM Syndicate Syndicating single table data Syndicating data with external source Syndication data to XML and Flat source Creating custom fields. SAP API overview connecting and diconnecting to repo basic core function API’s.

1. Sap MDM (master data management)
online training
Online | classroom| Corporate
Training | certifications | placements|
support
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info@magnifictraining.com
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• Contact Us : +91-9052666559

2. Sap MDM (master data management)
online training
5 Levels of MDM Maturity
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3. Master Data Management
• Largest part of the data quality
management effort (but not all)
• Industrial trends
– MDM tools, MDM vendors
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4. The 5 levels of MDM Maturity
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5. Level 0
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There is no common agreement of
data definitions across
organizations.
There is no common agreement of
data definitions among or between
different systems.
There is no standard for migrating
data between systems. It is a
highly manual process responding
to isolated needs.
The data originating from source
systems is generally deemed
unreliable and error-prone.
No one is responsible for data
correction.
Data is considered a mere byproduct of the applications that
use or generate it.
There are no data-specific job
tasks or roles.
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6. Level 1 – List Provisioning
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There is no systematic and rigorous
way of ensuring changes to the master
list.
Defining and maintaining master lists
involves significant meetings and
human involvement.
Master data access and usage is
supported by the data owner team.
This typically includes explaining data
file formats and content details.
Data conflicts, deletions, and changes
are handled manually.
Individual applications must
understand how to navigate to the
master list.
Data rigor and change management
are still considered application-based
processes.
The steps aren’t always apparent (or
communicated) to external data users.
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7. Level 2 - Centralization
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Applications are aware of a central
repository or list of master data,
and have hardcoded logic in order
to interact with that system.
A data model exists and serves to
uniquely identify each master
record.
Data access and integrity rules
exist, but must be managed by the
individual application systems.
Integrating new application
systems typically requires all data
and processing integrity logic be
replicated to each consuming
application.
Maintaining master identifiers and
business rules falls to the individual
applications that require access to
the master list.
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8. Level 3 – Centralized Hub
Processing
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Master reference data is centrally
managed.
Business-oriented data rules and
the associated processing have
been centralized.
There is an “owner” or steward of
the master data as well as a highly
functional data stewardship
process to support data conflict
resolution.
A data governance process exists
to resolve cross-functional or
cross-organization conflict.
Data cleansing and matching is
centralized and transparent to the
consuming applications.
The hub (as opposed to a human
being) is ensuring data accuracy
and consistency.
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9. Level 4- Reconciliation & Audit
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You’ve implemented a process-driven data
governance framework that supports
centralized business rules management and
distributed rules processing.
Your organization has a mature change
management process and includes both data
and business processes.
The IT architecture supports SOA as a means
to integrate common business methods and
data across applications.
Both data cleansing and data correction are
occurring on the hub.
A data stewardship process has been defined
to support the communication of data issues to
the responsible party–either an individual or
system.
There is an automated way to both enforce and
undo changes to master reference data.
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10. Level 5 – Enterprise Data
Convergence
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The hub is fully integrated
into the application
system environment,
propagating data changes
to all the systems that
need the master data.
Application development
is able to capitalize on
both business-based
application services and
business-based data
services.
Application processing
occurs independent of
physical system location
and data navigation.
MDM data access is
integrated with application
processing.
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11. Where we are today ?
Product
Customer
Master data from
business partners
- Product Info.
pool
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12. Where we want to be ?
A systematic solution to propagate internal master
data to external business partners such as supplier,
customers, or distributors
How far in the MDM level we want to go for
external business partners?
Prioritize master data types for external business
partners
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13. Mdm Contacts:-
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