Reference master data management

 Introduction  MDM Architecture
 Master Data Vs Other Data  Types Of MDM Strategies
 Master Data Categories  Steps In The Process For Managing And
Maintaining Master Data
 What Is Master Data Management?  Scoping An MDM Project
 Common Misconceptions About
Master Data
 Three Scenarios For MDM
 Traditional Approaches To MDM  MDM Implementation Options
 Why Do Enterprises Need MDM?  Data Quality & MDM
 MDM Challenges  Requirements For MDM Solutions
 Selected Case Studies  MDM Sample Products
2Reference/Master Data Management, by Dr. Hamdan Al-Sabri

 Two categories of structured data [1]:
 Master data: is data associated with core business entities such as customer,
product, asset, etc.
 Transaction data: is the recording of business transactions such as orders in
manufacturing, loan and credit card payments in banking, and product sales in
retail.
 Reference data: is any kind of data that is used solely to categorize other data
found in a database, or solely for relating data in a database to information beyond
the boundaries of the enterprise [2].

 Master data differ from other types of data in four ways [11]:
 Unlike transaction data and inventory data ,master data describe always basic
characteristics (e.g. the age, height, or weight).
 Pieces of master data usually remain largely unaltered.
 Instances of master data classes (data on a certain customer, for example) are
quite constant with regard to volume .
 Master data constitute a reference for transaction data.

Business Category People Places Things
Abstract Objects
(Concepts)
Examples of
Domain Areas
» Customer
» Employee
» Salesperson
» Office locations
» Customer locations
» Product
» Store
» Asset
» Contract
» License
» Warranty

 Master data management (MDM) is a set of processes, policies, services and
technologies used to create, maintain and manage data associated with a
company’s core business entities as a system of record (SOR) for the enterprise [1].
 Many organizations are implementing master data management solutions–under a
number of different labels, including MDM, Customer Data Integration (CDI),
Product Information Management (PIM), or Global Supplier Management (GSM)
[9].

 Master data management is a new problem.
 Master data is equivalent to metadata
 A data warehouse solves master data management issues.
 Managing master data is equivalent to ensuring data quality.
 Enterprise application suites solve the problem by providing a single point of
master data management.

 The enterprise application
 The data warehouse
 Integration middleware

 Redundant Data [3].
 Inconsistent Data [3].
 Data confects [1].
 Process delays and process defects caused by data errors [1].
 Additional operational cost [1].
 Mergers and acquisitions [4].
 Multiple ERP instances [4].
 Compliance [4].
 Service oriented architecture [4].
 Supporting Business change [5].

 Creating a Partnership between Business and IT [8].
 Managing Change [8].
 Globalization [1].
 Risk [1].
 Customer loyalty [1].
 Supply chain complexity [1].
 Cost‐cutting [1].

 MDM Applications for managing and publishing master data and metadata.
 A Master Data Store (MDS) containing consolidated master data.
 A Master Metadata Store (MMS) containing the master data business model, and
master data rules and definitions. The master data business model documents
master data entities, attributes, relationships and their business meaning.
 A set of Master Data Integration (MDI) services for consolidating, federating and
propagating master data.

 Three Types of Master Data Management Strategies [3]:
 Operational MDM aims to integrate the data for operational applications such
as ERP, CRM, financial reporting, etc.
 Analytic MDM is focused on the creation and support of a data warehouse
and business intelligence (BI) platforms.
 Enterprise MDM focuses on creating enterprise-wide strategy that
encompasses both operational and analytic MDM.

Option MDM Functional Scope
1 Master data synchronization
2 Option 1 + Master data integration + Master data vocabulary
3 Option 2 + A consolidated master data system of record (SOR)
4 Option 3 + Master data hierarchy management
5 Option 4 + Master data CRUD services
6 Option 5 + Single Master data entry system (MDES)
7 Option 6 + New master data business processes

 Management reporting. Enabling reporting by rolling up of business data (such
as revenue or profit) according to business dimensions such as customer or
product.
 Data synchronization. In this second scenario, the main objective shifts from
reporting to operations. The local systems continue to update master data records
or attributes, causing periods when master data may not be consistent across all
systems. Master data consistency is reestablished upon completion of the
synchronization process.
 Single point of origination. This is the most demanding scenario, all changes to
master data must originate at the hub, rather than at local systems.

 Is the goal improved reporting or increased efficiency of operations? For
improved reporting use Management Reporting. In case of efficient operations,
Data Synchronization or Single Point of Origination is better.
 How often must updates to master data be propagated from the hub to the
participating systems? The single point of origination scenario provides the
capability to get to near real-time synchronization if the business decision cycle
warrants that level of consistency.
 Can business processes be altered? The single point of origination scenario
requires change of processes, such as new product introduction or customer
registration.

BUILD BUY
Data store already in existence, with most of
the master data already integrated
Data is heavily fractured with a large number
of data entry systems
Maximum flexibility Sometimes not easy to modify
Ensures conformance with
corporate policies and strategy
May not supporting all corporate scenarios
Too expensive if the enterprise lacks
personnel qualified
Well-qualified
service providers

 There are four approaches for implementing MDM [5]:
 Consolidate the master data from multiple operational systems of entry in a
single master data store (MDS) that becomes the system of record for master
data.
 Propagate and synchronize master data changes between operational systems
of entry so that master data in all systems of entry is kept consistent.
 Consolidate and propagate master data using combination of first and second
approaches.
 Migrate and centralize operational master data systems of entry to a new
enterprise MDM system that acts as both the system of record and system of
entry for master data.

Consolidate
Propagate
Propagate +
Consolidate
Centralize
MDM
System
Operational
System
Data
Warehouse
System
Of Entry
System
Of record
Master data can be
used in BI for both
historical and
predictive analysis

 Data quality is the First Step on the Path to MDM.
 In a recent MDM study carried out by The Data Warehousing Institute (TDWI), 83%
of respondents reported that their organizations have suffered problems due to
poor quality master data, and 54% claimed to have derived benefits from good
master data.

 Some of the problems associated with poor quality master data outlined in the
TDWI report:
 Poor customer service.
 Inefficient marketing or purchasing.
 New products delayed.
 Inaccurate reporting.
 Arguments over which data is appropriate.
 Bad decisions based on incorrect definitions.
 Data governance and stewardship limitations.
 Limited visibility for data lineage.
 No understanding of master data homonyms.

 Most of the benefits of high-quality data referred to by respondents to the TDWI:
 Risk reduction.
 Superior customer service.
 Supply chain optimization.
 Accurate reporting.
 Better decision making.
 Easier auditing of information’s origins.
 Consistent definitions.

 Support for definition of roles with access rights enforced depending on the
responsibilities assigned for that role in the master data management process.
 ETL (extract-transform-load) capabilities for extracting master data/reference data
files or tables from multiple sources and loading the data into the master data
repository.
 Data cleansing capabilities for de-duplication and matching of master data records.
 A collaborative platform for coordinating decisions on master data reconciliation
and rationalization.
 Data synchronization and replication support for applying changes established in a
central server to each consuming application.
 Version control at the central policy hub combined with change monitoring across
all of the participating systems.

 IBM InfoSphere
 Oracle Master Data Management Suite
 SAP NetWeaver Master Data Management
 TIBCO Collaborative Information Manager

 Components of the product family are [10]:
 IBM InfoSphere Warehouse.
 IBM InfoSphere Information Server.
 IBM InfoSphere MDM Server.

 Components of Oracle Master Data Management Suite are [10]:
 Business Rules.
 Customer Data Hub / Product Data Hub.
 Data Integrator.
 Data Quality Matching Server / Data Quality Cleansing Server.
 Hyperion Data Relationship Management.

 Components of SAP NetWeaver MDM are [10]:
 MDM Console.
 MDM Import Manager.
 MDM Data Manager.
 MDM Syndicator.

 CIM comprises the following components [10]:
 Information Repository.
 Data Governance.
 Process Automation.
 Synchronization.
 Reporting and Business Intelligence.

 SAP NetWeaver MDM at Oerlikon Textile
 Employing more than 7,400 people
 Is a manufacturer of textile machinery and a provider of textile industry solutions covering the
entire value chain.
 Has branches for development, manufacture, and distribution in more than fifty locations
world‐wide.
 Oerlikon’s business units are supported by seven independent systems based on the SAP ERP
application.
 Over time, more and more data silos came into existence, leading to more and more isolated
data repositories partially containing conflicting data. Inconsistent and redundant master data
led to problems.
 Oerlikon Textile searched for a solution capable of consolidating data from disparate source
systems and making harmonized data available to all users working in the seven business
units.

 SAP NetWeaver MDM at Oerlikon Textile
 It was decided to use SAP NetWeaver MDM, as this product was considered to suit perfectly
the needs of Oerlikon Textile regarding master data consolidation and harmonization.
 To do so, it was first necessary to consolidate, cleanse, and update all data repositories
accommodated by the SAP ERP systems. This effort took six months, with SAP Consulting
offering support during implementation. Estimates put up by SAP experts prior to the project’s
start turned out to be stable, so that deadlines were met and costs remained within an
acceptable frame.
 The individual business units of Oerlikon Textile now benefit from high consistency and
relevance of master data facilitating cross‐unit business activities and helping exploit
cross‐selling and up‐selling potentials. By centralizing MDM, risks and shortcomings in data
processing could clearly be reduced, and the quality of the database could substantially be
improved.

 IBM Master Data Management at PostFinance
 Employing over 3,500 people
 A business unit of Swiss Post, is a major provider of financial services in Switzerland.
 In 2008, about 120,000 new customers opted for the services of PostFinance, increasing the
number of accounts by 311,000.
 Master data used to be processed in more than thirty application systems, both by online
access and by data replication. Because of this, customer account management became more
and more complex and difficult to oversee, particularly when customers had more than one
account.
 After a short phase of evaluation PostFinance decided in April 2006 to implement IBM
WebSphere Customer Center (which is a component of IBM InfoSphere MDM Server since the
beginning of 2009).
 Since November 2008, more than 1,000 employees of PostFinance have been working with
this new system.

 IBM Master Data Management at PostFinance
 Although it has been only a couple of months since the new era of MDM started at
PostFinance, the benefits have become quite obvious already.
 Apart from the benefits for the company itself, the new MDM concept at PostFinance has
positive consequences for the customers too.

[1] Mike Ferguson, “Getting Started With Master Data Management,” Intelligent Business Strategies, March 2008.
[2] Malcolm Chisholm, “Master Data versus Reference Data,” Information Management Magazine, April 2006.
[3] Info-Tech Research Group, “Master Data Management for the Masses of Data,” February 24, 2009.
[4] IBM, “Master Data Management: One Step at a Time,” February 2006.
[5] Colin White, BI Research, “Using Master Data in Business Intelligence,” March 2007.
[6] Henry D. Morris, ”Managing Master Data for Business Performance Management: The Issues and Hyperion' s
Solution,” April 2005.
[7] IBM, ”IBM Multiform Master Data Management: The evolution of MDM applications,” June 2007.
[8] Deloitte, ”Getting Started with Master Data Management,” 2005.
[9] INFORMATICA, ” Data Quality: the First Step on the Path to Master Data Management,” June 2007.
[10]Boris Otto, Kai M. Hüner, ”Functional Reference Architecture for Corporate Master Data Management,” May 31, 2009.
[11] David Waddington, “Master Data Management –Research Agenda for 2005” 2005.

Reference master data management

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