Briefing note and overview on the benefits and risks of the R12 upgrade and its impact on data quality. Author: Alex Fiteni CPA, CMA Copyright 2009. http://www.fiteni.com

1. ‘Quality is inversely proportional to error rates’
Alex Fiteni CMA

2. Data Quality Management and
the R12 Upgrade
Why is data quality important?
 What are the key Challenges in Data
Quality for Tax?
 What tools are available to help in the
clean up?
 What is the business case and where
should we focus?
 What are the benefits we can expect in
R12?


3. DQM Value Proposition



Master Data
requires more
stringent levels of
validation
The cost of error
clean up rises
exponentially with
complexity
Accurate, timely,
trusted Master Data
is a business asset
•DeDuplication,
Relationship
Mgt
•Tax &
Geography
Validation
Legal
Parties
Accounts,
Transacti
ons
•Life Cycle
Processing
•Accounting
Methods
Addresses
Items,
assets
•Data
Sources,
Data Quality

4. Risks & Mitigations

Risk Mitigation
 Identify data quality




priorities
Set realistic
standards
Make adequate
resources available
Leverage the inhouse expertise
available
Set timely,
measurable,
incremental goals
Risks
Failed, misCustomer&
directed,
Tax
Supplier
stalled
Compliance
relationship
transactions
issues,
failures,
, missed
increased
supply
discounts,
fines &
chain
payment
penalties
problems
errors

5. Data Quality tools

Oracle TCA
 De-duplication; data enrichment (3rd party
resources); internal dictionary of qualified words &
abbreviations for use on imported customer and
address data.

Oracle Enterprises Data Quality
 Customer MDM – Profiler, Standardization, Address
Validation, Data Quality Matching services

3rd Party Tools
 APIs, and/or data resources directly from country
postal systems (Canada, USPS (ZIP), Australia, etc)
 DQM tools from several companies such as
○ Talend Profiler, Semarchy/Capscan (EU), esperian
QAS, Trillium

6. Quality as a strategic focus





Invest in Best Practices and continual,
incremental quality improvements
Seek to leverage in-house expertise
Build data quality standards into Supplier,
Customer, Product On Boarding processes
Access trusted 3rd party data quality
assessment and management tools
Mantra: The better the source data quality,
the less costly the post-upgrade clean up
due to transactional failures will likely be

7. Business Benefits






Faster transaction processing times
Reduced the number of likely failure points in
the transaction life cycle
Increase the perceived and actual value of
master reference data
MDM policies, well executed, builds an
atmosphere of trust for the master data resource
Creates new opportunities for the use of these
data repositories in the business, such as
Business Intelligence (BI)
Reduces duplicates and replication in local,
unregulated repositories