News
10 Blunders to Avoid When Writing an RFP for MDM
By Ravi Shankar, director of product marketing at Siperian, Inc
By including the most important MDM (master data
management) requirements in your RFP (requests for
proposal), you will achieve greater success with
your MDM initiative along with a more rapid
deployment and faster time to value. A well
thought-out RFP will allow you to quickly reap the
returns from selecting a complete and flexible MDM
platform that is able to address both your current
and future business requirements.
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 managing multiple business data
entities such as customers, products and
organizations all within the same software platform.
By doing so, system maintenance is simplified 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 would result in additional
system maintenance and integration efforts and a
higher total cost of ownership. Another advantage of
an MDM platform that 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 ROI.
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 access to
data and reliable data quality metrics.
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 about 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.
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.
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. Because 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 model.
6. Cleansing data outside of the MDM platform. Data
cleansing includes name corrections, address
standardizations and data transformations. Typically
the number of source applications that provide
reference data to departmental-level CDI or 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 the cleansing tool
in order to leverage your existing investments.
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.
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 roll back any
manual errors or exceptions - a typical activity in
large organizations where several data stewards are
involved with managing master data.
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.
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.
By including these 10 critical MDM requirements in
your RFP, you will be well on you 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 your organization.
---Source: Reprinted from DM Review December 20, 2007 issue www.dmreview.com. Ravi Shankar can be reached at rshankar@siperian.com
|
|
|
|
 |
|
Save money on postage using leading
mail preparation software and other
direct marketing products. |
|
|
|
|
 |
Update & standardize addresses and
find out more about contacts in your
database.
|
|
|
|
|
 |
Find new customers perfect for your
business with our online and
specialty mailing lists.
|
|
|
|
|
 |
Locate the business information you
need such as ZIP Codes, address
verification, maps.
|
|
|
|
|
Facebook
Twitter
