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Reference
Data Management
Oracle Data Relationship Management (DRM) has always been
extremely powerful as an Enterprise MDM solution that can help manage changes
to master data in a way that influences enterprise structure, whether it be mastering
chart of accounts to enable financial transformation, or revamping organization
structures to drive business transformation and operational efficiencies, or
mastering sales territories in light of rapid fire acquisitions that require frequent sales territory refinement,
equitable distribution of leads and accounts to salespersons, and alignment of
budget/forecast with results to optimize sales coverage. Increasingly, DRM is also being utilized by
Oracle customers for reference data management, an emerging solution space that
deserves some explanation.
What is reference
data?
Reference data is a close cousin of master data. While
master data may be more rapidly changing, requires consensus building across
stakeholders and lends structure to business transactions, reference data is
simpler, more slowly changing, but has semantic content that is used to
categorize or group other information assets – including master data – and give
them contextual value.
The following table contains an illustrative list of
examples of reference data by type. Reference data types may include types and codes, business taxonomies,
complex relationships & cross-domain mappings or standards.
Types & Codes
Taxonomies
Relationships / Mappings
Standards
Transaction Codes
Industry
Classification Categories and Codes, e.g.,
North America Industry Classification System (NAICS)
Product
/ Segment; Product / Geo
Calendars
(e.g., Gregorian, Fiscal, Manufacturing, Retail, ISO8601)
Lookup Tables
(e.g., Gender, Marital Status, etc.)
Product Categories
City à State à
Postal Codes
Currency Codes (e.g., ISO)
Status Codes
Sales
Territories
(e.g., Geo, Industry Verticals, Named Accounts, Federal/State/Local/Defense)
Customer
/ Market Segment; Business Unit / Channel
Country
Codes
(e.g., ISO 3166, UN)
Role Codes
Market Segments
Country Codes / Currency
Codes / Financial Accounts
Date/Time, Time Zones
(e.g., ISO 8601)
Domain Values
Universal Standard Products
and
Services Classification (UNSPSC), eCl@ss
International
Classification of Diseases (ICD) e.g.,
ICD9 à
IC10 mappings
Tax
Rates
Why manage reference
data?
Reference data carries contextual value and meaning and
therefore its use can drive business logic that helps execute a business process,
create a desired application behavior or provide meaningful segmentation to
analyze transaction data. Further,
mapping reference data often requires human judgment.
Sample Use Cases of
Reference Data Management
Healthcare: Diagnostic Codes
The reference data challenges in the healthcare industry
offer a case in point. Part of being
HIPAA compliant requires medical practitioners to transition diagnosis codes
from ICD-9 to ICD-10, a medical coding scheme used to classify diseases, signs
and symptoms, causes, etc. The
transition to ICD-10 has a significant impact on business processes, procedures,
contracts, and IT systems. Since both
code sets ICD-9 and ICD-10 offer diagnosis codes of very different levels of
granularity, human judgment is required to map ICD-9 codes to ICD-10. The process requires collaboration and
consensus building among stakeholders much in the same way as does master data
management. Moreover, to build reports to understand
utilization, frequency and quality of diagnoses, medical practitioners may need
to “cross-walk” mappings -- either forward to ICD-10 or backwards to ICD-9
depending upon the reporting time horizon.
Spend Management: Product, Service & Supplier Codes
Similarly, as an enterprise looks to rationalize suppliers
and leverage their spend, conforming supplier codes, as well as product and
service codes requires supporting multiple classification schemes that may
include industry standards (e.g., UNSPSC, eCl@ss) or enterprise taxonomies. Aberdeen Group estimates that 90% of
companies rely on spreadsheets and manual reviews to aggregate, classify and
analyze spend data, and that data management activities account for 12-15% of
the sourcing cycle and consume 30-50% of a commodity manager’s time. Creating a common map across the extended
enterprise to rationalize codes across procurement, accounts payable, general
ledger, credit card, procurement card (P-card) as well as ACH and bank systems
can cut sourcing costs, improve compliance, lower inventory stock, and free up talent
to focus on value added tasks.
Specialty Finance: Point of Sales Transaction Codes and Product Codes
In the specialty finance industry, enterprises are
confronted with usury laws – governed at the state and local level – that
regulate financial product innovation as it relates to consumer loans, check
cashing and pawn lending. To comply, it is
important to demonstrate that transactions booked at the point of sale are
posted against valid product codes that were on offer at the time of booking
the sale. Since new products are being
released at a steady stream, it is important to ensure timely and accurate
mapping of point-of-sale transaction codes with the appropriate product and GL
codes to comply with the changing regulations.
Multi-National Companies: Industry Classification Schemes
As companies grow and expand across geographies, a typical
challenge they encounter with reference data represents reconciling various versions
of industry classification schemes in use across nations. While the United States, Mexico and Canada
conform to the North American Industry Classification System (NAICS) standard,
European Union countries choose different variants of the NACE industry
classification scheme. Multi-national
companies must manage the individual national NACE schemes and reconcile the differences
across countries. Enterprises must
invest in a reference data change management application to address the challenge
of distributing reference data changes to downstream applications and assess which
applications were impacted by a given change.
