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Reference Data Management and Master Data: Are they Related ?
By Mala Narasimharajan on Dec 07, 2012
Submitted By: Rahul Kamath
Oracle Data Relationship Management (DRM) has always been extremely powerful as an
Enterprise Master Data Management (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 restructuring sales territories to enable equitable
distribution of leads to sales teams following the acquisition of new products, or adding
additional cost centers to enable fine grain control over expenses. 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? How does it relate to MasterData?
Reference data is a close cousin of master data. While master data is challenged with problems
of unique identification, 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 gives them contextual value. In fact, the creation of a new master
data element may require new reference data to be created. For example, when a European
company acquires a US business, chances are that they will now need to adapt their product line
taxonomy to include a new category to describe the newly acquired US product line. Further, the
cross-border transaction will also result in a revised geo hierarchy. The addition of new products
represents changes to master data while changes to product categories and geo hierarchy are
examples of reference data changes.1
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 State 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)
2. 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 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.
Change Management: 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
3. 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.
References
