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 Master Data?
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 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.
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 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 1
Master
Data versus Reference Data, Malcolm Chisholm, April 1, 2006.
