Overview of Master Data Management by focusing on the importance of data quality in an industry.

1. The University of Texas at Dallas
WhitePaper
Master Data Management
Product Information Management for HP Printing and Personal Systems
Atul Jena
Abhrajit Ghosh
Jagruti Dwibedi
By

2. Naveen Jindal School of Management, Paper
Atul Je The University of Texas at Dallas 1
Master Data Management
Product Information Management for HP PPS
University of Texas at Dallas
1. Executive Summary………………………………………………...2
2. Introduction…………………….……………...……………………3
3. Liabilities of bad data………………………….……………………4
4. PIM Capabilities……………………………………..……………...5
5. PIM Architecture..…………………………………………………..6
6. PIM Implementation at HP………………………………………….8
7. Data Governance……………………………………………………11
8. PIM Vendors……………….…….…………………………………12
9. Conclusion……………….…….…………………………………...13
References…………………….…….…………………………….…….14

3. Naveen Jindal School of Management, Paper
Atul Je The University of Texas at Dallas 2
Executive Summary
Working at Hewlett Packard, which is both a product as well as a service
based company, one thing was noticed over the years, is the amount of time
and money lost over poor-quality data. Clearly, an organization like HP
works on multiple departments, managing vast amount of data about its
customers, products, suppliers, location and more. With multiple
departments managing so much data, there are anomalies which results in
no single consolidated version of the truth about its business. It’s an
expensive problem.
Master Data Management is a framework that reasserts business processes
to present master data to the business users in a consistent and contextual
manner. Such presentation of accurate data will help business users in
making smarter and economical decisions. Broadly, two separate domain
specific streams emerged as a part of MDM: Customer Data Integration
(CDI) and Product Information Management (PIM).
This paper will discuss Product Information Management for HP printing
and personal systems. From stating the liabilities of bad data quality to
building a PIM architecture for product solutions, this paper will highlight
end-to-end solution that merges and centralizes product information across
the enterprise.
Disclaimer: This paper is a case study for HP PPS Global and is presented
as a view point of handling the data quality challenge. No internal product
information of the company has been used.

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Atul Je The University of Texas at Dallas 3
Introduction
The product data quality challenge in HP is formidable because of the
complexity of managing product information across numerous
departments, hundreds to thousands of suppliers, and thousand to
millions individual product items. Poor data quality leads to inefficient
internal processes and missed sales revenues. But as stated earlier,
cleansing product data alone isn’t the answer- retailers, distributors,
manufacturers need a comprehensive solution that provide much more.
"With numerous manual data entry processes across multiple
applications, product data errors are pervasive and result in purchase
order discrepancies, longer lead times and inefficient use of human
resources," said Andrew White, enterprise and supply chain
management research director at technology consultancy Gartner.
To meet this challenge one would need a system which combines
product information management with robust capabilities in data
integration and governance. As a single repository for all product data
for distribution in all sales channels, the PIM should provide a
cohesive, centralized platform for all channel commerce.
While everyone chases the customer insight part of the equation (the
360o
view of the customer) realizing the power and potential of product
information (the single view of products) should be the goal for HP to
be able to recommend and promote the exact products the customers
are likely to buy.

5. Naveen Jindal School of Management, Paper
Atul Je The University of Texas at Dallas 4
Liabilities of Bad Data
As a retailer HP needs to know:
 All about its customer
Their profiles, histories, preferences, behaviors across all channels
(web, mobile, social, call centers, in-store, customer service ,
etc.)
 All about its products
So a personal insight to things each customer is most likely to buy
can be mapped
If the management of product information is poorly done it may
become unsustainable in the market.
The problems faced with product information are
It’s incomplete: shoppers aren’t sure and click away
It’s Out-of-date: as it takes a lot of time to update each channel
It’s inconsistent: with different images or descriptions in different
channels
It’s boring: relying on generic data instead of on-brand descriptions,
images and video
Inconsistent database: For example, a mobile team has a different
database from the web team and the store team.
It takes ages to get to market: this causes ‘shelf lag’ that eats up sales
and margin.

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Atul Je The University of Texas at Dallas 5
PIM Capabilities
With a wide range of products in the ranging from printers to servers,
HP leads the market in delivering best experience through its products.
Clearly with the capabilities PIM provides the enterprise should benefit
the most from it.
A PIM solution will allow HP to do the following:
 Locate and use appropriate data from heterogeneous sources.
 Access structure product data, which consists such things as model
name, product number, technical description and features set.
Unstructured data are not easily modeled into a PIM repository like
warranty (PDF), videos about the product etc.
 Cleanse data and related content.
 Identify and create missing product information.
 Connect and transmit data.
 Unify and relate a single product instance to multiple types of
content. By collecting, validating, and approving the product
related content, the PIM provides one synthetic representation,
which is available on different purposes.
 Enable cross media publishing of product catalogs.
 Distribute disparate product information from a single source.
 Enable multi-lingual catalog creation and deployment
 Create personalized catalog views of the product information. Such
a view contains only the product information that the specific user
cares about.

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Atul Je The University of Texas at Dallas 6
PIM Architecture
There are four PIM solution architectures: External Reference, Registry,
Reconciliation Engine, and Transaction Hub.
External Reference (or Consolidation) is the low-end PIM solution
architecture; a reference database that points to all data but does not actually
contain any data. ; does not define, create, or manage a centralized platform
where Master Data is integrated to create a “single version of the truth.”
The Registry architecture consists of a registry of unique master entity
identifiers. An entity resolution service identifies the master entity records
and the data-source links that were used to maintain the attributes are
maintained by the Data Hub.
The Reconciliation Engine (or Coexistence) architecture is a step up from
the Registry architecture. It harmonizes product Master Data across
databases and acts as a central reference point. This architecture provides
synchronization between itself and legacy systems; retailers will often
implement it as an intermediate architecture (i.e., after they have outgrown
the Registry architecture.
The Transaction Hub architecture stores the up-to-date product Master
Data with its associated enriched attribute data. It also supports new and
legacy transactional and analytical applications, and includes a business
service and data integration layer. This architecture is well-suited to
companies that need to collect information, cleanse it, build it on the fly,
and serve it to other destinations. Hence, it is a perfect solution for HP PPS.
The following figure illustrates the general PIM architecture. The PIM hub
contains the MDM Data Storage, the Validation Engine, the Workflow
Engine, References, and the Metadata. This information is made available
through the Security and Access Layer. The latter ensures that you present
content only to persons who are entitled to have it, even as you allow
authorized persons to modify that content.

8. Naveen Jindal School of Management, Paper
Atul Je The University of Texas at Dallas 7
Figure: The PIM Solution Architecture
The Enterprise service Bus is used to make available the information
both upstream and downstream using mechanisms such as PubSub,
Web Services or Batch FTP- —that will allow HP to collect the
information or publish it to its consumers whether they are supply
chain, e-commerce, publishing, or stores.

9. Naveen Jindal School of Management, Paper
Atul Je The University of Texas at Dallas 8
PIM Implementation at HP
HP PPS has broadly divided its products into two categories- Consumer
(Pavilion, Envy, Omen, Deskjets, etc.) & Commercial (Probook,
EliteBook, Z Workstations, and Officejet etc.) units. Features are
modified to both the series of units over the time. New products are
introduced in each segment and out dated products are recalled. Spare
parts list are maintained for all these units. Consumers constantly look
for products online or seek tech support depending on the information
they see. Immaculate data presentation is an absolute need.
The MDM services should be robust enough to manage the Master data,
Data Quality, services like authorization, introduction of new products
and much more. A PIM allows to create lots of metadata, including
description of product categories, descriptions of the information that
needs to be collected, the rules about the information, and the
exceptions to those rules.
So, HP would need a model that would be robust enough in handling
problems of duplication, wrong information, authorization, etc.
The following Transactional Hub Model handles all of these.
Working Model of a Transaction Hub MDM Architecture
The MDM services component is composed of the following components
shown in the Figure:
 Interface services: These services provide a consistent entry point to
invoke MDM services through a variety of technologies regardless of
how the service is called. In addition, the interface services have the

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ability to accept multiple request message formats through support for
pluggable parsers
 Lifecycle management services: Lifecycle management services
provide business and information services for all master data domains
such as customer, product, account or location to create, access and
manage master data held within the master data repository.
 Data quality management services: The services in this group can be
divided into two groups:
Data validation and cleansing services provide capabilities to
specify and enforce data integrity rules.
Reconciliation services provide matching services which check
whether or not a new product is a duplicate to an existing product,
conflict resolution services and merge, collapse and split services which
are used by data stewards to reconcile duplicates.
 Master data event management services: The master data event
management services provide the ability to create business rules to react
to certain changes on master data and to trigger notification events.
 Hierarchy and relationship management services: Hierarchy
services create and maintain hierarchies.
 Authoring services: Authoring services are used to define or extend the
definition of master data entities, hierarchies, relationships and
groupings.
 Base services: The base services component provides services in the
following four groups:
 Privacy and security services implement authorization on four
different levels:
o Service level: determines who is allowed to use the service
o Entity level: determines who is allowed to read/write a particular
entity
o Attribute level: determines who can read/write which attribute
o Record level: determines who can update which particular
records
 Audit logging services have the ability to write a complete history
of all transactions and events which occurred for a complete trace
on what happened in the MDM system which can also be used for
problem determination or to comply with certain legal requirements.
 The workflow services support collaborative authoring of master
data in processes like New Product Introduction and enable business
rules and delegation of tasks to external components.
 Search services allow you to look up and retrieve master data
 Master data repository: The master data repository has the following
parts:
The metadata: This part of the repository has all relevant metadata
stored such as a description of the data model for the master data.
The master data: This part of the repository is where the master data is
physically stored.
The history data: The history data is a complete history on all the master
data entity changes in the repository. This enables point-in-time queries
against the MDM data.

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Atul Je The University of Texas at Dallas 10
The reference data: Here lookup tables such as country codes,
measurement units for products, marital status, and the like are stored.
How it works
Master Data is scattered across applications when MDM is applied to it.
Thus in the Figure, (1), the master data (both HP consumer and commercial
units) from the source application system has to be extracted, cleansed,
standardized, de-duplicated, transformed and loaded into the MDM system
(2).These steps are performed in the Master Data Integration phase. For HP,
once the MDM system built with the Transaction Hub MDM pattern is
complete, all redundant copies of the master data in the source application
systems can be deleted as indicated by the white colour of the master data
parts of the persistence. Furthermore, the source applications are "MDM
enabled".
This means, whenever a transaction (3) is invoked on the source application
system which affects transactional data (for example, billing data of a
pavilion laptop) and master data, the master data portion of this transaction
invokes a master data service of the MDM system for processing. Only the
transactional part is processed locally.
Customers/Consumers access applications (UI) which consume master data
by (4) invoking the MDM services to retrieve master data in a read-only
way.
An MDMUI (5) on enterprise level is used to create and change master data.
An MDM UI can be part of an enterprise portal implementation, for
example. The key imperative is that all changes to master data by any source
system are only performed through services of the MDM system. This
guarantees the required level of master data consistency at all times and
enables customers reach out to the correct/desired products.

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Data Governance
Data Governance can be defined as the mechanism by which we ensure
that the right corporate data is available to the right people at the right
time in the right format with the right context through the right
channels.
With the unprecedented growth in the amount of Data in the recent
years, the data needs to be controlled and understood in order to process
it effectively in a secure manner. Data governance isn’t a definable
solution; rather, it’s a journey toward transparency—offering a clearer
understanding of what information you have, how to manage it, and
how it can be used to advance the enterprise.
A product information governance project may appear to be a daunting
effort when one begins to structure the data rules.
The best practice is to develop a data roadmap to provide a clear and
precise understanding of the data and its use within HP. The road map
should detail how data is required and submitted for use within the
enterprise, account for the multiple uses of the data (purchasing,
engineering, marketing, and maintenance), plus the required data
elements and structure needed to accommodate each software system.
Benefits as a Result of Data Governance
There are many benefits of implementing an innovative data governance
and master data management system. Many of the basic benefits, both in
process and cost, are:
 Reducing inventory through identification of duplicate items,
 Facilitation of inventory sharing and internal purchasing programs,
 Reduced employee time spent searching for items,
 Common spare part usage strategies,
 Reduced downtime in manufacturing equipment due to lack of
information availability,
 Ability to manage inventory using a just in-time model.
 Data Governance supports both indirect and direct cost savings.
Businesses can begin to embrace the definition of operational data as an
asset of the corporation, ensuring improved data accuracy and
confidence of the data users.

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PIM Vendors
It has been said that data outlasts applications. This means that an
organization’s business data survives the changing application landscape.
Technology advancements drive periodic application reengineering, but the
business products, suppliers, assets and customers remain.
The dominant PIM solutions are IBM InfoSphere, Oracle Product Hub, and
SAP NetWeaver. All three vendors, IBM, Oracle, and SAP, have been
involved with MDM for the past 10 years. They have reached their positions
of dominance through multiple acquisitions.
All three vendors offer a full MDM ecosystem, including data integration,
data quality, databases, messaging, and sometimes hardware.
This Magic Quadrant by Gartner provides insight into the segment of the
constantly evolving packaged MDM system market that focuses on
managing product data to support supply chain management (SCM), CRM
and other customer-related strategies. It positions relevant technology
providers on the basis of their Completeness of Vision relative to the
market, and their Ability to Execute on that vision.

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Conclusion
PIM is Master Data Management applied to the product space. PIM is
enabled through business process improvements, organizational
improvements, and the alignment of multiple information technologies.
In this paper it was shown how HP could lose business because of poor
data. Having a customer insight is not enough. As a retailer HP relies
heavily on its products. And in the product domain it is all about knowing
the afflictions, meeting the challenges and delivering on the promise of the
personalized customer experience.
Retail business is all about staying ahead of competition. A PIM integrated
system will provide just that to HP, staying ahead by giving a wonderful
customer experience. Like HP says “If you are going to do something,
make it matter”.

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References
 http://www.informatica.com/us/products/master-data-
management/product-information-management/#fbid=sBHsv9WFkAk
 Product Information Management: Definition, Purpose, and Offering,
By Christophe Marcant, Senior Specialist in Sapient
 http://h30507.www3.hp.com/t5/Journey-through-Enterprise-IT/Data-
Governance-It-is-the-data-stupid-govern-it/ba-
p/125983#.VIZrSTHF_d2
 http://www8.hp.com/h20195/V2/GetDocument.aspx?docname=4AA4-
9093ENW&cc=us&lc=en
 Product Information Management (PIM) Data Governance, by Jackie
Roberts, VP at DATAFORGE™
 http://www.gartner.com/technology/reprints.do?id=1-
1QTLTLC&ct=140214&st=sb