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Essential steps for Implementing MDM
Citation preview
The Six Key Ingredients for
Master Data Management
Success
David Loshin,
President, Knowledge Integrity
Daniel Teachey
Director of Corporate Communications, DataFlux Corporation
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
2
Principal MDM
Components and Capabilities
David Loshin
Knowledge Integrity, Inc.
www.knowledge-integrity.com
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
3
Agenda
Introduction to master data management
The MDM Component Layer Model
MDM Maturity
MDM Functional Services
Summary
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
4
The MDM Component Layer Model
Architecture
Identification
Management
Governance
Integration
Business
Process
Management
Master Data Model MDM System Architecture
MDM Service Layer Architecture
Data
Standards
Metadata
Management
Data
Quality
Data
Stewardship
Administration/Configuration
Hierarchy Management Identity Management
Migration Plan
Record Linkage Merging and Consolidation
Identity Search and Resolution
MDM Component Service Layer
Application Integration and Synchronization Service Layer
MDM Business Component Layer
Business Rules
Business Process Integration
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
5
Mixing It Up…
Order of review:
1. Identification
2. Business Process Management
3. Management
4. Governance
5. Integration
6. Architecture
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
6
Identification
Every object subject to “mastering” is managed using a unique representation within the master repository
Any time data intended to refer to that object is seen by an application, its unique representation must be found, verified, and presented back to the application by the MDM platform
IdentificationRecord Linkage Merging and Consolidation
Identity Search and Resolution
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
7
Identity Search and Resolution
David Howard Loshin
Loshin, Howard
Howard David LoshinHoward David LoshinHoward David Loshin
Objective: Provide the services that will seek the
matching record in the master index that represents
the “queried” object
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
8
Record Linkage – Parsing and Standardization
Parsing
Identifying and tagging pieces of each data value within a semantic context
Standardization
Correcting terms based on defined rules
Assembling components into recognized patterns
Transformation
Rule-based modifications into target canonical representations
Transformation into target format
Loshin,HowardD LoshinHowardDFirst HowardMiddle DLast Loshin
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
9
Consolidating “Replicated” Data
Knowledge Integrity, Inc. 301-754-6350
Loshin 301-754-6350DavidHoward
Knowledge Integrity Incorporated 301 754-6350
LotionDavid 1163 Kersey Rd
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
10
Consolidating “Replicated” Data
Knowledge Integrity, Inc. 301-754-6350
Loshin 301-754-6350DavidHoward
Knowledge Integrity Incorporated 301 754-6350
LotionDavid 1163 Kersey Rd
David Loshin
Knowledge Integrity
301-754-6350
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
11
Consolidating “Replicated” Data
Knowledge Integrity, Inc. 301-754-6350
Loshin 301-754-6350DavidHoward
Knowledge Integrity Incorporated 301 754-6350
LotionDavid 1163 Kersey Rd
David Loshin
Knowledge Integrity
301-754-6350
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
12
Consolidating “Replicated” Data
Knowledge Integrity, Inc. 301-754-6350
Loshin 301-754-6350DavidHoward
Knowledge Integrity Incorporated 301 754-6350
LotionDavid 1163 Kersey Rd
David Loshin
Knowledge Integrity
301-754-6350
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
13
Consolidating “Replicated” Data
Knowledge Integrity, Inc. 301-754-6350
Loshin 301-754-6350DavidHoward
Knowledge Integrity Incorporated 301 754-6350
LotionDavid 1163 Kersey Rd
David Loshin
Knowledge Integrity
301-754-6350
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
14
Business Process Management
Business Process Modeling
Business Process Integration
Business Rules
Business Component Layer
Business
ProcessManagement
MDM Business Component Layer
Business Rules
Business Process Integration
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
15
Mapping to the Business Processes
BusinessObjectives
BusinessPolicy
BusinessPolicy
BusinessPolicy
BusinessPolicy
Terms
Facts
BusinessLogic
Execution Model
ApplicationApplication
ApplicationApplication
ApplicationApplication
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
16
Business Rules
BusinessPolicy
Business rules contribute to the business process model, and can be isolated and managed as
enterprise content
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
17
Business Component Layer
Services reflecting business process needs
“create new customer”
“locate matching products”
“find purchase order”
“remove supplier”
“modify vendor status”
Business layer built on top of business and component object services
Business process models document the expected ways in which the business operates
The business process model exposes candidate master data objects and the components that use them
Business rules traditionally embedded within application code can be extracted and managed as master content as well
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
18
Management
Administration/Configuration
Hierarchy Management
Identity Management
Migration
Management
Administration/Configuration
Hierarchy Management Identity Management
Migration Plan
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
19
Management Issues
Configuration, administration, and ongoing maintenance
Identity management: For any object, enough identifying information must be managed to determine that
A record exists and no more than one record for the object, or
No record exists and one can be created that can be uniquely distinguished from all others
Hierarchy management – both historical and connectivity
Migration management
Application
MDM
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
20
Governance
Data Standards
Metadata Management
Data Quality
Data Stewardship
GovernanceData
Standards
Metadata
Management
Data
Quality
Data
Stewardship
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
21
Master Object Resolution
Resolution of candidate master data types requires a compete view of what composes the information architecture
This entails cataloging data sets, their attributes, data domains, definitions, contexts, and semantics
This view must facilitate the resolution of:
Format at the element level,
Structure at the instance level, and
Semantics across all levels
This introduces three challenges:
Collecting and analyzing master metadata
Resolving similarity in structure
Understanding and unifying master data semantics
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
22
Operationalizing Data Governance
Actualization of data governance activities enables:
The identification of explicit and hidden risks associated with data expectations
The actualization of the implementation of business policy
Oversight of the definition of critical data elements
Monitoring and auditing information quality rule compliance
Managing enterprise data ownership and stewardship
Coordination and oversight of enterprise data quality
In general, data governance provides management oversight for organizational observance of different kinds of information policies
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
23
Stewardship: Remediation and Manual Intervention
Issues with addressing data quality events:
Immediate remediation of flawed data – does this imply data correction?
Not all data flaws can be captured via automated processes –this implies manual reviews
Accuracy may only be measured by comparing values directly
Carefully integrate manual intervention when necessary in a controlled manner
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
24
Integration
Application integration
Synchronization
Component services
Integration
MDM Component Service Layer
Application Integration and Synchronization Service Layer
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
25
Business
Application
Functional
Interface
Data
Business
Application
Functional
Interface
Data
Wrapper
Facade
Business
Application
Wrapper
Facade
Service
Layer
MDM Repository
Phase 1 Phase 2 Phase 3
Application Transition
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
26
Synchronization and Coherence
Master RepositoryEAI/EII
Federated Consolidated
Issues to consider:
• Frequency of updates to master object attributes• Performance impacts• Bottlenecks
• Attribute overlap
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
27
Component Service – Object Locate
“Object Locate”
Master Index
“Object Factory”
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
28
Architecture
Master data model
MDM system framework
Service layer architecture
Architecture
Master Data Model MDM System Architecture
MDM Service Layer Architecture
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
29
Master Data Model
Limited universe of common master objects
Party, customer, product, part, supplier, claim, instrument
Universal models may be suitable as starting points
Challenges:
Resolution of metadata in a consistent manner
Creating a model that accommodates all applications properly
CUST
First VARCHAR(15)
Middle VARCHAR(15)Last VARCHAR(21)
Address1 VARCHAR(45)Address2 VARCHAR(45)City VARCHAR(30)
State CHAR(2)ZIP CHAR(9)
Nightingale-Patterson
CUSTOMER
FirstName VARCHAR(14)
MiddleName VARCHAR(14)LastName VARCHAR(20)
TelNum NUMERIC(10)
Nightingale-Patterso
Last VARCHAR(21)
LastName VARCHAR(20)
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
30
Central Master/Coexistence
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
31
Registry
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
32
Transaction Hub
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
33
Architecture - Summary
Architecture decisions rely on the requirements identified during the analysis of:
Business processes
Data requirements
Operational processing requirements
Levels of coherence and synchronization
Governance protocols
Component and application services
© 2007 Knowledge Integrity, inc.
www.knowledge-integrity.com
(301)754-6350
34
Questions?
If you have questions, comments, or suggestions, please contact me
David Loshin
301-754-6350
The Six Key Ingredients for
Master Data Management
Success
Daniel Teachey
Director of Corporate Communications, DataFlux Corporation
About DataFlux
Recognized as a leading provider of data quality and
data integration solutions
Data quality technology to improve corporate
information on customers, products, inventory,
employees, assets…
Over 1,000 customers worldwide
Offices throughout the US and in the UK
Founded in 1997
– Acquired by SAS in 2000
– Operates as a wholly-owned subsidiary
The Methodology for Data Governance
Analyze:The Cost of Bad Data
How is customer retention being affected by poor-
quality data?
How is poor data affecting the ability to do basic
revenue reporting and analysis?
What is the loss from failed business transactions
due to data issues?
How quickly can we respond to business
opportunities?
Analyze:Discover Poor-Quality Data
Create a scorecard of
current data quality
Build a plan to address
pressing data quality
issues
Develop reports for
management to detail
data quality problems
Improve:Solve Data Quality Issues
Problem Solution
Multiple occurrences of the same
customer (duplicate names and/or
addresses)
Create match conditions for
customers and apply the rule to all
enterprise applications
Thousands of duplicate,
unclassified products
Standardize product and item data
on uniform classifications
(UNSPSC)
No validation at point of entry Utilize business rules for data
quality as real-time services via
SOA architecture
Improve:Build Data Quality Rules
Create a business rule to
resolve data quality
Build jobs to standardize,
integrate and enhance
information
Use jobs in batch or real-time
processes
Control:Monitor Data Quality
Ensure information accuracy
Validate data against your business rules
Automatically identify invalid data
Control:Enforce Enterprise Standards
Enforce business
rules and data
governance
standards
Check the quality of
product information
over time
Create dashboards
to help track data
quality issues