The document discusses the myth that an organization needs to be "ready" to implement Master Data Management (MDM) by cleaning up data quality issues first. It argues that data quality is actually one of the benefits achieved by an effective MDM implementation, not a prerequisite. MDM solutions can capture and enforce data quality rules across different data models and business processes in real-time, while standalone data quality projects are limited in scope and have a finite lifespan. Rather than waiting to fix all data issues, the document advocates that organizations should transform digitally by implementing MDM to improve data quality on an ongoing basis.

1. Bill O’Kane – VP & MDM Strategist
THE MYTH OF BEING "READY"
FOR MDM
5.6.2020
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2. 05_01_20
2Strictly Confidential © 2020 Profisee Group, Inc.
RECENT MDM
MARKET SURVEY
RESULTS
Top two reasons for not implementing MDM:
• Inability to articulate benefits based on business
outcomes. This is widely accepted and can be
dealt with using a framework such as Profisee’s
Business Impact Roadmap (BIR).
• The need to clean up isolated data quality issues
in advance. This both overstates the business
value of standalone data quality efforts and
underestimates the data quality capabilities of
MDM solutions.

3. 05_01_20
3Strictly Confidential © 2020 Profisee Group, Inc.
DATA QUALITY IS
NOT A
PREREQUISITE FOR
MDM
Data quality is one of the benefits achieved by an
effective implementation of MDM.
There are major differences and disconnects
between the disciplines of Data Quality and MDM.
They must be understood to deliver maximum
business benefits.

4. 05_01_20
2 3
DATA QUALITY ≠ MDM
4Strictly Confidential © 2020 Profisee Group, Inc.
Data quality efforts are
generally standalone IT
projects that target
datasets – application or
analytics related
Data quality projects are
reactive to specific issues
that are causing
operational or analytical
issues
Data quality projects
typically have a beginning,
a middle, and an end
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5. 05_01_20
DATA QUALITY ≠ MDM
5Strictly Confidential © 2020 Profisee Group, Inc.
In summary:
Data quality projects are frequently
unable to address upstream issues
Data quality projects are usually limited
to the data model within a business
application or a data warehouse/mart
Data quality projects do not address
business process, corporate culture,
or data governance

6. 05_01_20
6Strictly Confidential © 2020 Profisee Group, Inc.
DATA QUALITY ≠
MDM
Data quality efforts are characterized by
standalone cleanup projects that IT accepts
accountability for. These are often batch-
oriented and have a limited shelf life.
MDM is an ongoing program that embeds
data quality rules across different data
models, semantics, analytics, and business
process. MDM enables real-time access to
trusted data.

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DATA QUALITY ≠ MDM
7Strictly Confidential © 2020 Profisee Group, Inc.
MDM is an ongoing program
focusing on those master
data elements that will
provide the most business
benefit in a timely manner
MDM captures data quality
rules required to implement
new or improved processes
across semantic differences
between business applications
and analytics platforms
MDM will be required in order
to successfully implement any
data governance organization
and process

8. 05_01_20
DATA QUALITY ≠ MDM
8Strictly Confidential © 2020 Profisee Group, Inc.
MDM solutions can capture the business rules discovered as part of standalone data quality projects for future
use in broader initiatives spanning multiple data sources and targets with different data models and semantics
MDM solutions are designed out of the box to be integrated with business applications and
analytics platforms, and to enforce data quality rules proactively in real time if necessary
MDM solutions support broad-based data stewardship of master data –
independent of (but considering) business application and analytics requirements

9. NO ORGANIZATION IS “READY” TO DO MDM
FROM A DATA QUALITY PERSPECTIVE
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THOSE THAT TRANSFORM THEMSELVES
DIGITALLY DON’T WAIT

10. QUESTIONS?
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