How to Write an RFP for Master Data Management by sol61731


									    Hub Solution Designs, Inc. 
    Trusted. Insightful. Experienced.

Vol. 1, No. 2
                                                March 2008
Welcome to the March edition of "Best Practices in Master Data Management", published by
Hub Solution Designs.

This month, we've got a great article on "How to Write an RFP for Master Data Management" by
Ravi Shankar of Siperian. We've also got a new team member, Maureen Butler, with a strong
background in MDM and SAP, and speaking engagements at the MDM Summit in San Francisco
(March 30 - April 1) and Oracle Application Users Group conference in Denver (Apr. 13-17).

If you'd like to discuss your Master Data Management or Customer Data Integration initiative,
we'd love to hear from you. Just call (781) 749-8910, or contact us via our web site.

Best regards --- Dan Power

How to Write an RFP for Master Data Management
by Ravi Shankar, Director of Product Marketing, Siperian

Critical master data management (MDM) functionality can easily be overlooked when a request
for proposal (RFP) is narrowly focused on a single data type—such as customer (Customer Data
Integration) or product (Product Information Management) — or on near-term requirements
within a single business function.

Consequently, IT teams and systems integrators alike run the risk of selecting and investing in
technologies that may be difficult to extend to other data types or difficult to scale across the
organization. Worse, such solutions will likely require costly and extensive custom coding in
order to add additional business data entities or data sources, or to extend the system to other
lines of business or geographies.

In order to avoid these costly pitfalls, bolster the return on investment, and reduce the overall
project risk, it is important that your RFP include key business data requirements across
several critical business functions including sales, marketing, customer support and

To avoid the common mistakes made by MDM software evaluation teams and ensure long term
success, you should make sure that key components are built into your master data
management RFP. By including these ten critical MDM requirements in your RFP, you will be
well on your way to laying the foundation for a complete and flexible MDM solution that
addresses your current requirements, and is also able to evolve to address unforeseen future
data integration requirements across the organization.

Ten Costly RFP Mistakes to Avoid

Mistake #1: Failing to ensure multiple business data entities can be managed within a single
MDM platform. When you select and deploy an MDM platform, make sure it is capable of
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managing multiple business data entities such as customers, products, and organizations, all
within the same software platform. By doing so, system maintenance is simpler and more cost
effective, which results in lower total cost of ownership. A less favorable alternative is to
deploy and manage separate master data solutions that each manage a different business data
entity. However, this approach results in additional system maintenance and integration
efforts and a higher total cost of ownership. Another advantage of an MDM platform which can
handle multiple data types is that implementation can begin with a single business data entity
like customer, and can later be extended to accommodate other master data types—resulting
in rapid return on investment.

Mistake #2: Ignoring data governance needs at the project- or enterprise-level. Data
governance is unique to each and every organization since it is based on the company’s
business processes, culture, and IT environment. However, companies typically select an MDM
platform without much thought to their enterprise data governance needs. It is critical that
the underlying MDM platform is able to support the data governance policies and processes
defined by your organization. In contrast, your data governance design could be compromised
and forced to adapt to the limitations of some MDM software platforms with fixed or rigid data
models and functionality. Controls and auditing capabilities are also important data
governance components. In order to properly support this functionality, your RFP should
require the MDM platform to integrate with your security and reporting tools to provide fine-
grained control over access to data and reliable data quality metrics.

Mistake #3: Failing to ensure the MDM platform can work with your standard workflow tool.
Workflow is an important component of both MDM and data governance, as it can be used to
approve the creation of a master data entity definition and to determine, in real-time, which
conflicting data entities survive. Workflow can also be used to automatically alert the data
steward of any data quality issues. So in preparing a master data management RFP, it is
important to raise the question of how the MDM platform will integrate with the standard
workflow tool that you have selected. Several MDM vendors bundle their own workflow tool
and may not offer integration with your standard workflow tool.

Mistake #4: Failing to ensure the solution supports complex relationships and hierarchies.
With a single entity master data hub, such as customer, hierarchies and relationships are
relatively straightforward. For example, organizational relationships are depicted as legal
hierarchies of parent and child organizations, while consumer relationships are those belonging
to a common household. On the other hand, hierarchies among multiple data entities can be
highly complex. Examples include: retail locations in the Eastern region stocking only certain
products; complex counterparty legal hierarchies determining credit risk exposure; or an
account holder’s spouse being a high net-worth individual. Make sure your MDM request for
proposal requires the solution to be capable of modeling complex business-to-business (B2B)
and business-to-consumer (B2C) hierarchies, along with the definitions of those master data
entities within the same MDM platform.

Mistake #5: Relying on fixed Service Oriented Architecture (SOA) services. Reliable data is a
prerequisite to supporting SOA applications — applications that automate business processes by
coordinating enterprise SOA services. Since MDM is the foundation technology that provides
reliable data, any changes made to the MDM environment will ultimately result in changes to
the dependent SOA services, and consequently to the SOA applications. IT professionals need
to ensure the MDM platform can automatically generate changes to the SOA services whenever
its data model is updated with new attributes, entities, or sources. This key requirement will
protect the higher-level SOA applications from any changes made to the underlying MDM
system. In comparison, MDM solutions with fixed SOA services that are built on a fixed data
model will require custom coding in order to accommodate any underlying changes to the data

Mistake #6: Cleansing data outside of the MDM platform. Data cleansing includes name
corrections, address standardizations, and data transformations. Typically the number of
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source applications that provide reference data to departmental level Customer Data
Integration (CDI) or Product Information Management (PIM) solutions is relatively small. In
these cases, the data can be efficiently cleansed at the source using commonly available data
quality tools. In contrast, the number of sources for an enterprise MDM deployment spans
multiple departments and typically comprises tens or hundreds of systems. In this scenario,
cleansing the data at the source systems is not viable. Rather, data cleansing needs to be
centralized within the MDM system. If your company has already standardized on a cleansing
tool, then it is important to ensure the MDM solution provides out-of-the-box integration with
that tool in order to leverage your existing investments.

Mistake #7: Thinking probabilistic matching is adequate. There are several types of matching
techniques commonly in use—deterministic, probabilistic, heuristic, phonetic, linguistic,
empirical, etc. The fact is, no single technique is capable of compensating for all of the
possible classes of data errors and variations in the master data. In order to achieve the most
reliable and consolidated view of master data, the MDM platform should support a combination
of these matching techniques with each able to address a particular class of data matching. A
single technique, such as probabilistic, will not likely be able to find all valid match
candidates, or worse may generate false matches.

Mistake #8: Underestimating the importance of creating a golden record. For MDM to be
successful within an organization, it is not enough to simply link identical data with a registry
style, because this will not resolve inconsistencies among the data. Rather, master data from
different sources need to be reconciled and centrally stored within a master data hub. Given
the potential number of sources across the organization and the volume of master data, it is
important that the MDM system is able to automatically create a golden record for any master
data type such as customer, product, asset, etc. In addition, the MDM system should provide a
robust unmerge functionality in order to rollback any manual errors or exceptions—a typical
activity in large organizations where several data stewards are involved with managing master

Mistake #9: Overlooking the need for history and lineage to support regulatory compliance.
Today, business users not only demand reliable data, but they also require validation that the
data is in fact reliable. This is a challenging and daunting undertaking considering that master
data is continually changing with updates from source systems taking place in real-time as
business is being transacted, and while master data is merged with other similar data within
the master data hub. The history of all changes to master data and the lineage of how the
data has changed needs to be captured as metadata. In fact, metadata forms the foundation
for auditing and is a critical part of data governance and regulatory compliance reporting
initiatives. As a result, and because metadata is such an essential component of MDM, it is
important that your RFP defines the need for history and lineage.

Mistake #10: Implementing MDM for only a single mode of operation: analytical or operational.
An enterprise MDM platform needs to synchronize master data with both operational and
analytical applications in order to adequately support real-time business processes and
compliance reporting across multiple departments. In contrast, CDI and PIM solutions are most
often implemented at the departmental level with the objective of solving a single defined IT
initiative such as a customer relationship management migration or a data warehouse rollout.
These deployments will typically only synchronize data back to either operational or analytical
applications but not both. Without the ability to synchronize master data with both
operational and analytical applications, your ability to extend the MDM platform across the
organization will be limited.

MDM Success Begins with Selecting an Integrated and Flexible MDM Platform

Once your organization starts to make its departmental master data management projects
operational, you will find that your larger enterprise requirements will expand to include other
business data types and other lines of business or geographies. Therefore, it is important to
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first seek out and evaluate an MDM solution that adequately addresses these ten essential MDM
capabilities. It is also important to assess the MDM platform’s ability to support these ten core
capabilities out-of-the-box, as they should be integrated components of a complete enterprise-
wide MDM platform. In this way, you will be able to mitigate technology risk and improve your
return on investment since additional integration and customization will not be necessary in
order to make the system operational. Another benefit gained by having these ten MDM
components integrated within the same MDM platform is that software deployment is much
faster and easier to migrate over time. Finally, it is wise to check customer references to
evaluate their enterprise-wide deployment and to ensure that the vendor’s MDM solution is
both proven and includes all ten enterprise MDM platform capabilities.

By including these critical MDM requirements in your RFP you will achieve greater success with
your MDM initiative along with a more rapid deployment and faster time to value. Not to
mention, a well thought out RFP will allow you to quickly reap the returns from selecting an
integrated and flexible MDM platform that is able to address both your current and future
business requirements.

About the Author

Ravi Shankar is Director of Product Marketing at Siperian, Inc., an innovative provider of the
most flexible master data management platform. For more information, contact the author at or visit

Maureen Butler Joins Hub Solution Designs; Brings Extensive SAP
We're very happy to have Maureen Butler joining us as a Senior Consultant. She brings
significant SAP information management, marketing, supply chain, organizational design and
change management expertise to the team.

Prior to joining Hub Solution Designs, she was Director, Customer Information Management for
a Fortune 500 company, W.W. Grainger, Inc., where she led a team of 35 people in cleansing &
migrating customer data into an enterprise-wide SAP system. She also developed the Data
Governance and Quality programs, and implemented Grainger's Center of Excellence for
Customer Information.

By launching a cross-functional Master Data Management initiative at W.W. Grainger to improve
data quality and usability, she helped drive significant revenue growth, cost savings, and
process improvements for customer acquisition and penetration.

Ms. Butler has more than seventeen years of strategic leadership experience in integrated
market planning and communications, customer brand strategy, organizational design, change
management, product management, and supply chain with companies such as W.W. Grainger,
Inc., National Education Training Group, and Educational Resources.

She is a Six Sigma Champion and a graduate of Grainger’s Leadership Development Program.
She received an MBA from Lake Forest Graduate School of Management, and a business
marketing degree from Loyola University Chicago. Ms. Butler is located in Palatine, Illinois.

Ms. Butler will help us to establish and grow our partnership with SAP, the world's largest
business software company.
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Catch Us at the MDM Summit and Oracle Applications User Group
We're going to be speaking at the upcoming Spring 2008 MDM Summit in San Francisco, and the
Oracle Applications Users Group COLLABORATE 08 conference in Denver.

The MDM Summit conference has grown to be one of the largest gatherings of data integration
professionals, with 600+ visionaries and vendors in one location. The session, which will be
presented jointly by Dan Power from Hub Solution Designs and Shirlee Collins from ADP Dealer
Services, is entitled "Real World Data Governance", and will be presented on Tuesday, April 1st
from 4:00-4:30 pm PDT. Attendees will learn about establishing a data governance
organization, improving underlying customer data quality, and creating a robust process to
enrich customer data in Oracle Customer Hub with D&B information. The ADP Dealer Services
story reflects a pragmatic approach, weaving together Sales Operations and Finance, and
balancing each group's needs and priorities in managing customer master data.

Dan Power is also speaking at COLLABORATE 08 in Denver, CO, which is the annual conference
of the Oracle Applications Users Group (OAUG), and which will have a Master Data Management
track for the first time. Dan is a member of the OAUG Education Committee, and helped to
plan & organize the MDM track of the upcoming conference. The session, "Best Practices in
Master Data Management and Data Governance", will be presented on Tuesday, April 15th from
9:45-10:45 am MDT. It will present some useful MDM and Data Governance best practices, and
will also cover what works and what doesn't, the importance of a holistic approach, how to get
the political aspects right, and how to address more than just the technology elements.

DM Review magazine recently published a cover story by Dan Power on "The Political Aspects of
Master Data Management and Data Governance" in its March issue. Dan was published
previously in February 2007 in a DM Review Special Report. This article suggests several ways
to deal with the difficult political aspects of MDM projects to make those initiatives more

Our Vice President & Partner, Tim O'Sullivan, wrote an article that will be published as the key
feature of an upcoming MDM supplement in the June issue of DM Review. The article on
"Project Management Challenges within a Changing Landscape" explores project management
best practices for MDM initiatives, and provides a framework for addressing MDM's typical
political, technological and data stewardship challenges.

For more information …
Please call (781) 749-8910 or visit

Thank you!

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