In business, master data management comprises the processes, governance, policies, standards
and tools that consistently define and manage the critical data of an organization to provide
a single point of reference.
The data that is mastered may include: reference data - the business objects for
transactions, and the dimensions for analysis analytical data - supports decision making
In computing, an MDM tool can be used to support master data management by removing duplicates,
standardizing data, and incorporating rules to eliminate incorrect data from entering
the system in order to create an authoritative source of master data.
Master data are the products, accounts and parties for which the business transactions
are completed.
The root cause problem stems from business unit and product line segmentation, in which
the same customer will be serviced by different product lines, with redundant data being entered
about the customer and account in order to process the transaction.
The redundancy of party and account data is compounded in the front to back office life
cycle, where the authoritative single source for the party, account and product data is
needed but is often once again redundantly entered or augmented.
MDM has the objective of providing processes for collecting, aggregating, matching, consolidating,
quality-assuring, persisting and distributing such data throughout an organization to ensure
consistency and control in the ongoing maintenance and application use of this information.
The term recalls the concept of a master file from an earlier computing era.
Issues At a basic level, MDM seeks to ensure that
an organization does not use multiple versions of the same master data in different parts
of its operations, which can occur in large organizations.
A common example of poor MDM is the scenario of a bank at which a customer has taken out
a mortgage and the bank begins to send mortgage solicitations to that customer, ignoring the
fact that the person already has a mortgage account relationship with the bank.
This happens because the customer information used by the marketing section within the bank
lacks integration with the customer information used by the customer services section of the
bank.
Thus the two groups remain unaware that an existing customer is also considered a sales
lead.
The process of record linkage is used to associate different records that correspond to the same
entity, in this case the same person.
Other problems include issues with the quality of data, consistent classification and identification
of data, and data-reconciliation issues.
Master data management of disparate data systems requires data transformations as the data
extracted from the disparate source data system is transformed and loaded into the master
data management hub.
To synchronize the disparate source master data, the managed master data extracted from
the master data management hub is again transformed and loaded into the disparate source data
system as the master data is updated.
As with other Extract, Transform, Load-based data movement, these processes are expensive
and inefficient to develop and to maintain which greatly reduces the return on investment
for the master data management product.
One of the most common reasons some large corporations experience massive issues with
MDM is growth through mergers or acquisitions.
Two organizations which merge will typically create an entity with duplicate master data.
Ideally, database administrators resolve this problem through deduplication of the master
data as part of the merger.
In practice, however, reconciling several master data systems can present difficulties
because of the dependencies that existing applications have on the master databases.
As a result, more often than not the two systems do not fully merge, but remain separate, with
a special reconciliation process defined that ensures consistency between the data stored
in the two systems.
Over time, however, as further mergers and acquisitions occur, the problem multiplies,
more and more master databases appear, and data-reconciliation processes become extremely
complex, and consequently unmanageable and unreliable.
Because of this trend, one can find organizations with 10, 15, or even as many as 100 separate,
poorly integrated master databases, which can cause serious operational problems in
the areas of customer satisfaction, operational efficiency, decision-support, and regulatory
compliance.
Solutions Processes commonly seen in MDM include source
identification, data collection, data transformation, normalization, rule administration, error
detection and correction, data consolidation, data storage, data distribution, data classification,
taxonomy services, item master creation, schema mapping, product codification, data enrichment
and data governance The selection of entities considered for MDM
depends somewhat on the nature of an organization.
In the common case of commercial enterprises, MDM may apply to such entities as customer,
product, employee, and vendor.
MDM processes identify the sources from which to collect descriptions of these entities.
In the course of transformation and normalization, administrators adapt descriptions to conform
to standard formats and data domains, making it possible to remove duplicate instances
of any entity.
Such processes generally result in an organizational MDM repository, from which all requests for
a certain entity instance produce the same description, irrespective of the originating
sources and the requesting destination.
The tools include data networks, file systems, a data warehouse, data marts, an operational
data store, data mining, data analysis, data visualization, Data federation and data virtualization.
One of the newest tools, virtual master data management utilizes data virtualization and
a persistent metadata server to implement a multi-level automated MDM hierarchy.
Transmission of Master Data There are several ways in which Master Data
may be collated and distributed to other systems.
This includes: Data consolidation: The process of capturing
master data from multiple sources and integrating into a single hub for replication to other
destination systems.
Data federation: The process of providing a single virtual view of master data from
one or more sources to one or more destination systems.
Data propagation: The process of copying master data from one system to another, typically
through point-to-point interfaces in legacy systems.
See also Reference data
Master data Record linkage
Data steward Data visualization
Customer data integration Data integration
Product information management Identity resolution
Enterprise information integration Linked data
Semantic Web Data governance
Operational data store Single customer view
References
External links Master data management at DMOZ
Microsoft: The What, Why, and How of Master Data Management
Microsoft: Master Data Management Hub Architecture PolarLake: Reference Data Management and Governance
Open Methodology for Master Data Management Semarchy: Why do I Need MDM?
MDM Community Multi - Data Domain MDM
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