The Perception of Master Data Management - VV Narendra Kumar

The Perception of Master Data Management

Abstract

Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, which provides a common point of reference. When properly done, MDM streamlines data sharing among personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications. The benefits of the MDM paradigm increase as the number and diversity of organizational departments, worker roles and computing applications expand. For this reason, MDM is more likely to be of value to large or complex enterprises than to small, medium-sized or simple ones. When companies merge, the implementation of MDM can minimize confusion and optimize the efficiency of the new, larger organization. For MDM to function at its best, all personnel and departments must be taught how data is to be formatted, stored and accessed. Frequent, coordinated updates to the master data file are also essential.



Introduction

Master data management (MDM) is meant to deliver a near real-time, hub-based and synchronized master record of information to any seat or point of view in the organization. Master records are created with data that is defined, integrated and reconciled from multiple systems (customer relationship management, financial, supply chain, marketing etc.) and classified by type (e.g. product master, customer master, location master etc.). MDM is often pursued by data type through programs that address Customer data integration (CDI) or product information management (PIM), though many observers believe true MDM requires reconciliation of all data types. Critical to MDM are the notions of data quality and matching, which technology tools can help to automate.



Master Data



Most software systems have lists of data that are shared and used by several of the applications that make up the system. For example, a typical ERP system as a minimum will have a Customer Master, an Item Master, and an Account Master. This master data is often one of the key assets of a company. It's not unusual for a company to be acquired primarily for access to its Customer Master data.



Essential data types

There are essentially five types of data in corporations:



Unstructured—This is data found in e-mail, white papers like this, magazine articles, corporate intranet portals, product specifications, marketing collateral, and PDF files.
Transactional—This is data related to sales, deliveries, invoices, trouble tickets, claims, and other monetary and non-monetary interactions.
Metadata—This is data about other data and may reside in a formal repository or in various other forms such as XML documents, report definitions, column descriptions in a database, log files, connections, and configuration files.
Hierarchical—Hierarchical data stores the relationships between other data. It may be stored as part of an accounting system or separately as descriptions of real-world relationships, such as company organizational structures or product lines. Hierarchical data is sometimes considered a super MDM domain, because it is critical to understanding and sometimes discovering the relationships between master data.

Master—Master data are the critical nouns of a business and fall generally into four groupings: people, things, places, and concepts. Further categorizations within those groupings are called subject areas, domain areas, or entity types. For example, within people, there are customer, employee, and salesperson. Within things, there are product, part, store, and asset. Within concepts, there are things like contract, warrantee, and licenses. Finally, within places, there are office locations and geographic divisions. Some of these domain areas may be further divided. Customer may be further segmented, based on incentives and history. A company may have normal customers, as well as premiere and executive customers. Product may be further segmented by sector and industry. The requirements, life cycle, and CRUD cycle for a product in the Consumer Packaged Goods (CPG) sector is likely very different from those of the clothing industry. The granularity of domains is essentially determined by the magnitude of differences between the attributes of the entities within them



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SAP MDM ONLINE TRAINING - COURSE CONTENTS

SAP MDM Course Content:
1. SAP MDM Console:
History Of MDM
MDM Overview
MDM Performance
MDM Components
•Server Components
•Client Components
•Admin Components
Console Introduction
Repository structures
Creating Repository
Slave Repository
Normalize Repository
Compacting Repository
Duplicating Repository
Load Repository with Indices
Appropriate Repository
Update Repository
.A2A files
Archive Repository
Un Archive Repository
MDM Transport Procedure
Export Repository Schema
Import Repository Schema
Working with different types of tables
•Flat
•Qualifier
•Taxonomy
•Hierarchy
•Special tables
•Object tables
Console Settings
•Creating Users & Roles
•Change tracking
•Remote systems
Console Security
•Repository Level
•Server Level
Repository Administration
Normalize Repository
Compacting Repository
Duplicating Repository
Appropriate Repository
Update Repository
Backup and Restore
Working with master/slave Repository

2. SAP MDM Data Manager:
Data Manager Overview
MDM Modes:
•Record Mode
•Hierarchy Mode
•Taxonomy Mode
•Match Mode
Free Form Search
Masks
Relationships
Named Searches / Free Form Search
Validations and Assignments
Matching Mode
Transformations
Rules
Strategies
Match & Merge
Working with Qualifier tables
Relationships
Working with Images
Check In / Check Outs
SAP MDM Workflow

3. SAP MDM Import Manager:
•Import Manager Overview
•Configure Import Manager
•Import Map File
•Pre-requisites for Data Import
•Monitoring Import Status
•Performing Data Import
•Transformations
•Table Joins and Lookup
•Splitting fields
•Building Hierarchy
•Partitioning Fields
•Field Mapping/Value Mapping
•Value Conversion
•mdis file
•Batch processing

4. SAP MDM Syndicator
•Syndicator Overview
•Exporting into diff file formats
•Record Suppression
•Syndication Map
•Maps and Map properties
•Item Mapping
•Destination Items
•Custom Items
•Merge Items
•Syndication Records

5. Overviews:
Master data Consolidation
Harmonization
Central Master Data Management

6. Integration Scenarios:
•XI Integration
•R/3 Communication

7. Real Time Scenarios on LIVE Servers

For details and demo, Please contact

Sree
info@sapmdm.co.in
Mob: +911-9949512008

Data Modelling and Designing in SAP MDM

Data Modeling and Designing in MDM:

Data Modeling is like organizing files in file cabinet. Effective organizing ensures effective performance.