Ebook - The Guide to Master Data Management

Hexaware is a leading global provider of IT and BPO services. The company has
achieved leadership position in domains such as Banking, Financial Services, Insur-
ance, Transportation, Logistics and HR-IT solutions, Hexaware focuses on delivering
business results leveraging technology solutions and specializes in Business Intelli-
gence & Analytics, Enterprise Applications, Independent Testing and Legacy Moderniza-
tion.
Hexaware has been providing business technology solutions for over 18 years and offers
world class service delivery, technology leadership and skilled human capital.

NA Headquarters : 1095 Cranbury South River Road,
Suite 10, Jamesburn, NJ 08831
Main: 609-409-6950,
Fax: 609-409-6910

INDIA Headquarters : 152, Sector - 3, Millennium Business Park
“A” Block, TTC Inductrial Area, Mahape,
Navi Mumbai - 400 710
Tel.: +91-22-67919595,
Fax: +91-22-67919500
Hexaware’s Q & A Guide to
EU Headquarters : 4th Floor, Cornwall House, 55-57 High Street, Master Data Management
Slough Berkshire SL1 1DZ, UK
Tele: +44(0)1753 217160, Actionable Intelligence Enabled
Fax: +44(0)1753 217161

APAC Headquarters : 180 Cecil Street,
#09-03, Bangkok Bank Building,
Singapore 069546
Tel.: +65-63253020
www.hexaware.com
Hexaware Technologies. All rights reserved.

01. What are the different types of data available in an organization? 1 Click

02. What data should constitute master data? 2 Click

03. What is Master Data Management (MDM)? 2 Click

04. What are the key characteristics of MDM system? 3 Click

05. What is MDM hub? 3 Click

06. What are the different architecture styles of MDM hub systems? 5 Click

07. What are the key benefits of implementing MDM? 7 Click

08. What is the difference between data warehouse and MDM? 7 Click

09. How can data warehouse systems leverage MDM? 9 Click

10. What are the challenges in implementing MDM? 9 Click

11. Do leading Vendors support MDM? 10 Click

12. Can MDM be implemented with Open Source? 11 Click

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Hexaware Technologies. All rights reserved.

1 What are the different types of data available in an organization? 2 What data should constitute master data?

Data available in an organization can be classified into five types: Master data are the core elements of the business that are applied to multiple
transactions and are used to categorize, aggregate, and evaluate the
Unstructured – Data that cannot be organized into identifiable structure. E.g. transaction data. Master data is not transaction data but is almost always
emails, web pages, word processor documents etc. involved with transactional data. For example: Consider a single transaction
"John Doe sold one laser printer, product number MX0315, from Scanners &
Transactional – Data that forms the transactions processed by the operational Printers product family for $90 on 12th December 2011". Master data
systems of the enterprise, e.g. sales, trades, etc. Transactional data typically elements in this transaction are Salesperson (John Doe), Product Group
describes the activities or transactions of the business. Transactional data (Scanners & Printers), Product (Laser Printer) and Product Number
typically captures the verbs, such as sale, delivery, purchase, email, and (MX0315). It can be observed that Master data is synonymous to Dimensions
revocation. in a data warehouse.

Metadata – Data that describes the data held in the enterprise information What is Master Data Management (MDM)?
3
architecture, e.g. definitions of tables and columns in the system catalog of a
database, or entities and attributes in a data model.
MDM is a set of tools, technology and processes required to create and
maintain master data. MDM ensures that an organization's critical information
Hierarchical – Stores the relationships between data such as organizational
(customers, vendors, products, employees, locations) is uniquely identified,
hierarchy, product lines etc.
accurately defined and consistently applied across that organization's
operational systems – spanning geographic, line-of-business, and application
Master – Master data are the critical nouns of a business and fall generally
silo boundaries.
into four groupings: people, things, places, and concepts. It should be the
single trusted source of data that everyone in an enterprise relies on and uses.

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4 What are the key characteristics of MDM system?
CRM ERP SharePoint HR Finance
A MDM system typically enables:

Data Governance – It should provide robust security for the underlying
models, define data governance policies and procedures, and support
workflows to implement data governance policies.
Master Data
Web Services
Metadata Management – MDM system should have the ability to manage Syncronization
business & process metadata. MDM Hub

Data Repository – MDM system should have the ability to model entities, Data Quality
attributes, complex hierarchies and relationships among the entities.
Metadata Store

Data Integration – MDM system should integrate with both source and Stewards hip and
Workflow
Governance
subscribing systems, ideally in both batch and real-time modes. It should
support system of entry and system of record operations.
Entity Version Hierarchy Version
Control Control
Data Quality – MDM system should have high data quality processes
supporting standardization, de-duplication, match and merge etc.

5 What is MDM hub?
Entity Management Hierarchy Management

The MDM hub is a database with the software to manage the master data that
is stored in the database and keep it synchronized with the transactional Fig 1: MDM Hub Source: Microsoft
and/or analytical systems that use the master data. MDM hub contains tools and functions required to maintain master data.

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6 What are the different architecture styles of MDM hub systems? master entity in the MDM hub, so that a significant number of MDM queries
can be satisfied directly from the hub database, and only queries that refer-
There are three basic architectures available namely Repository, Registry and ence less-common attributes have to reference the
Hybrid. application database.

Repository Model Gartner has identified four different implementation styles for MDM based on
Master data for an enterprise is stored in a single database. The repository criteria like authoring sour ce, data persistence or storage, data latency etc.
data model must include all the attributes required by all the applications that This is depicted below:
use the master data. The applications that consume, create, or maintain
master data are all modified to use the master data in the hub, instead of the
master data previously maintained in the application database. For example,
the Order Entry and CRM applications would be modified to use the same set
of customer tables in the master-data hub, instead of their own data stores. Consolidation Registry Centralized Coexistence

Author Master Data Author in Author in Hub Author anywhere
Registry Model distributed
authored in
Registry model is opposite of repository model. Master data is maintained in Operational systems
the application databases, and the MDM hub contains lists of keys that can be Systems

used to find all the related records for a particular master-data item. None of Persistence Hub stores a Hub Stores index Hub persists Persist anywhere
copy apart from of master data master data,
the master-data records is stored in the MDM hub. For example, if there are copies exist in
author
records for a particular customer in the CRM, Order Entry, and Customer Ser- edges
vice databases, the MDM hub would contain a mapping of the keys for these Hub is System Hub is System System of Refer-
Validation Hub is System
three records to a common key. of Refrence of Reference of Record ence / Record
Hybrid Model
Consumer of Downstream Operational and Upstream Upstream
As the name implies, the hybrid model includes features of both the repository Master Data Analytics and Analytical Operations Operations
and registry models. The hybrid model leaves the master-data records in the Reporting
application databases and maintains keys in the MDM hub, as the registry Data Latency Batch to real Batch to event Real time Publish / Subscribe,
model does. But it also replicates the most important attributes for each time driven event-driven

Source: Gartner

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7 What are the key benefits of implementing MDM? Characteristics Datawarehouse MDM

Goal Provide analytical Create and maintain a
Organizations can achieve number of benefits including:
capabilities to analyze single, consistent version
• Improves data sharing and reuse. data across dimen- of reference data only.
• Eliminate redundant data management and integration activities. sions.
• Improve product and customer management.
• Promote consistent use of data. Data Datawarehouse MDM contains only refer-
• Reduce supplier on boarding cost. contains transactional ence data along with any
• Improved operational efficiency due to streamlining business (Facts) and Dimen- associated hierarchies or
processes and good data quality. sional data including relationships, typically
• Refined fraud prevention. any associated hierar- corresponding to dimen-
• Improved decision making. chies. sions in a data warehouse.
• Improved quality and compliance.
End user Reports and / or The touch points with
presentation / dashboards are business users revolve
8 What is the difference between data warehouse and MDM? primarily used to around data governance.
Touch points with
business users present data to end Emphasis is on maintaining
Although by definition both look similar and complement each other, they are users. data quality, governance
not. Fundamental difference between data warehouse and MDM are: and compliance.

Data Write back Data write back to A master data system can
source system is not write back data / provide
supported golden copy of data to
source system to ensure
consistency.

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9 How can data warehouse systems leverage MDM? • Integration with existing applications could provide a challenge unless
common formats are defined to exchange data across disparate
Managing Dimensional table becomes easier if dimensions in a applications.
datawarehouse are modeled based on master data. Due to its design, master • Differing code sets, identifiers for master data across systems
data consists of business entities which are nothing but dimensions in provides a challenge in defining a unique identifier unless an appro
datawarehouse parlance. ETL work to load dimension data will be greatly priate global standard exists that can be adopted.
reduced if they are drawn from master data. Also, data enters MDM system • Existing business processes and timelines for creation of reference
only if all business rules were fulfilled. This ensures that dimension data is of data could be different across systems and based on the needs of
high quality in data warehouse. those systems. This could be a challenge when trying to implement a
centralized process for data governance if the MDM hub is used to
10 What are the challenges in implementing MDM? author master data as data synchronization issues could arise.

11 Do leading Vendors support MDM?
Some of the challenges in implementing MDM are given below:

• As an MDM program requires changes to existing / setting up of new Most of the well-established technology vendors are providing MDM solutions
data governance processes, there would be a challenge in gaining as part of their product and solution offerings. Some of the notable ones that
acceptance and support for the program, unless backed by a strong figure in the Gartner Magic Quadrant are given below:
Business Change program.
• An MDM program is not limited to its implementation as a one-time IBM – IBM has three products for MDM – Infosphere MDM Server, Infosphere
activity. It requires to be run as a continuous program to ensure that MDM Server for Product Information Management and Initiate Master Data
the data governance processes remain relevant and efficient with the Service. IBM has announced a convergence roadmap to integrate the prod-
passage of time and that the master data is consistently used by ucts in a phased manner, starting with Infosphere Master Data Management
existing applications and new applications. This requires a contin- v10.0.
uous commitment from management.
Informatica – Informatica acquired the former Siperian Hub and has made it
available in its portfolio as the platform for multi domain MDM.

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Oracle – Oracle has 3 products as part of its MDM portfolio – Oracle Fusion
Customer Hub, Oracle CDH and Oracle Siebel UCM.
Business Intelligence & Analytics
12 Can MDM be implemented with Open Source?
Our Business Intelligence & Analytics solutions help you transform
into a dynamic enterprise through actionable intelligence. We have
Not many Open Source vendors exist in the MDM space. Talend, a global
more than 50 patent pending innovations which help you with faster
company operating in the open source data integration space, has introduced
& efficient deployment & over 85 plus satisfied customers across
an open source MDM as part of its portfolio. The product comes in 2 editions
diverse industries. From consulting, articulation and development, to
– a community edition that is available at no charge and a licensed Enterprise
deployment and support, Hexaware can architect and implement
edition.
data warehouses and BI systems, employing solution accelerators,
process frameworks and jumpstart analytical kits, for any part of your
business process.

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