master data management for the business … data management for the business professional discussion...
TRANSCRIPT
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Master Data Management
For the Business Professional
DiscussionOctober 22, 2009
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Agenda
• What is Master Data Management?
• Why is it important?
• What happens to produce bad quality data?
• Quick review of Customer, Product, and Agreement
• Detailed example of pricing How MDM impacts this critical success factor
• What to do next - How to deliver value quickly
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Essentials What is Master Data Management?
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What is Master Data Management?
• Business capability to deliver
– accurate,
– complete,
– timely, and
– consistent information
• …a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization providing processes for
– collecting, persisting, and distributing,
– aggregating,
– quality-assuring, matching, and consolidating data
throughout an organization to ensure consistency and control in the ongoing maintenance and use of this information. (source: wikipedia definition)
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Data Management – Types of Assets
• Master
– critical nouns of a business and fall generally into four domains: people, things, places, and concepts
• Hierarchical
– describes relationships between data elements
• Transactional
– sales, deliveries, invoices, trouble tickets, claims
• Unstructured
– e-mail, articles, intranet portals, product specifications, marketing collateral
• Metadata
– data about other data
Information Management Life Cycle
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What is Master Data Management?
Reference data
• Party
–Customer
– Supplier
– Employee
• Product
• Contract Agreement
• Pricing
• Location
• Asset
• Hierarchies
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What is Master Data Management?
Customer Product Asset Employee
CreateCustomer visit, such as to
Web site or facility; account
created
Product purchased or
manufactured; Supply Chain
Management optimization
Unit acquired by
opening a PO;
approval process
necessary
HR hires, numerous
forms, orientation,
benefits selection, asset
allocations, office
assignments
ReadContextualized views based
on credentials of viewer,
sales reports
Periodic inventory
catalogues, sales reports
Periodic reporting
purposes, figuring
depreciation,
verification
Office access, reviews,
insurance-claims,
immigration, HR needs
UpdateAddress, discounts, phone
number, preferences, credit
accounts
Packaging changes, raw
materials changes
Transfers,
maintenance,
accident reports
Immigration status,
marriage status, level
increase, raises, transfers,
contact information
RetireDeath, bankruptcy,
liquidation, do-not-call.
Canceled, replaced, no
longer available
Obsolete, sold,
destroyed, stolen,
scrapped
Termination, death
SearchCRM system, call-center
system, contact-
management system
ERP system, orders-
processing system
GL tracking, asset DB
management
HR Line of Business
system
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What is Master Data Management?
Master Data Life Cycle Management
Extensibility — maintainable
Hierarchy management
Ability to manage complexity
Industry knowledge
Matching, linking, indexing
Identification and recognition
Master Data Governance
Data-stewardship facilities
Batch and real-time
Access and assembly
Work Flow and Collaboration
Propagation to the central system
Publishing to peripheral systems
Database Management
Application server
Integration brokers
Compute platform environment
Query
Transaction processing
Batch processing
Information Delivery
Data encapsulation
Service Data Objects (SDO)
Granular and packaged
foundation for Service Orientated
Architecture (SOA) based
application use
Service Component Architecture
Data Modeling and Analysis Information Quality Integration and Synchronization
Performance and Scalability
Master Data Management
Business ServicesTechnology Base
Business Capability
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Why Master Data Management is Important
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Why this is important
• Companies struggle with the basics of fundamental PIM
• 80% companies are not confident in the quality of their product data
• 73% find it “difficult” or “impractical” to standardize product data ‘PIM Business & Technology Trends
- Survey’, Sept 2007
• Current methods [data quality] don’t work well
• 66% companies use “manual effort” or “custom code”
– 75% say it is too unreliable
– 64% say it is too slow
– 56% say it is too expensive
– >50% say ‘all of the above’
‘PIM Business & Technology
Trends - Survey’, Sept 2007
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Product Information Management
Product Life Cycle Management
Conceive to Design Design to Product Product to Deploy Deploy to Service
Campaign to Order Order to Cash Cash to Care
Campaign to Care
Idea to Product
Data Warehouse - Analytics
Product Master Reference Data
Sales Transaction Data
The Macro Processes
where Master Data is used
Fundamental to business success
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0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
Name-related
errors
Dupl icates Address errors Customer type Miss ing
Relationships
Best Case (US)
Worst Case (US)
Average (US)
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Customer (what to expect)
Source: Data from InformationWeek/Innovative Systems' 1999 Delphi Industry Study. North American data does not include Mexico. Mexico is included in International Data
This means we can expect:
• 5% name related errors • 8% duplicates• 8% address errors• 20% missing relationships
in our source systems populating the CUSTOMER entity…
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Customer (International – what to expect)
Source: Data from InformationWeek/Innovative Systems' 1999 Delphi Industry Study. North American data does not include Mexico. Mexico is included in International Data
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
Name-related
errors
Dupl icates Address errors Customer type Miss ing
Relationships
Best Case (INT'L)
Worst Case (INT'L)
Average (INT'L)
Are you kidding? (remember this is a worst case outlier)
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Lapsed Lapsed ActiveActiveProspectiveProspectiveSuspectSuspect
• Becomes a Customer if procures product or service
• Added to Do Not Contact list if meets certain predefined business rules (e.g. requests no contact)
• Can be deleted after some period of inactivity based on business rules
• Becomes a Lapsed Customer after some predefined period of no purchases of products or services
• Remains a Lapsed Customer for some time period.
• Becomes a Customer if procures product or service
• May become a Prospect, based on predefined business rules
• May be added to Do Not Contact list
• Becomes a Prospect if added to a marketing campaign or if relationship is manually changed by a sales rep
• Deleted if does not become a Prospect within some predefined time period
Do Not ContactDo Not ContactDeceased – Individual
Dissolved, M/A – Org, Group
Deceased – Individual
Dissolved, M/A – Org, Group
Customer Relationships
Want to guess how well this works across independent applications and business
operating units?
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Results - Impacts the business where it hurts…
Marketing Example
Source: William McKnight, SAP – Approach to Data Quality ROI 2008
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How does bad quality data happen?
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A closer look…
Source: Aberdeen 2008
0%
10%
20%
30%
40%
50%
60%
70%
80%
At the data source
layer our data is not
clean or managed
properly
At the integration
layer our data
s ources are not
integrated properly
At the end us er
access and
cons umption layers
us ers introduce
errors
At the analytics
appl ication layer;
appl ication
development
introduces errors
At the securi ty layer;
acces s in not
control led properly
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Essentials – How bad data is created
§ Existing applications have been designed and deployed by many independent developers separated by time, geography, and organization…
§ Means complex, undocumented, and difficult-to-maintain integrations solutions exist primarily because…
each connection or integration point was created locally
rather than globally optimized.
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For each business unit…
• Independent Goals, Objectives
• Independent Operations
• Independent Systems
– Different Developers
– Different Goals
– Differing Business Rules
– Independent Results
• Silos
– Varying Views of Enterprise Master Data
• Customer
• Supplier
• Product
• Location
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Typical – Point-to-Point Integration
• Custom Coded
• Varying Development Methodologies
• Few or No Industry Standards
• Mixed Transport Technologies
• Isolated Knowledge– Small Teams
– Single Developer
• Little Documentation
• High maintenance costs
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Why So Expensive?
§ n components§ n ( n-1) interfaces§ Example
§ 5 components§ 5 (5-1) = 20 interfaces
§ May have to build many§ New flows could force more for
each reference entity
Point-To-Point Integration
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Challenges with this Approach
The number of possible integration points between any two objects (assuming two-way integration) grows at a rate of n(n-1).
For 5 applications managing product and customer, the minimum number of connections is 5 (5-1) (2) = 40. For 10 application components, the number grows to 180!
10 * (10-1)2 = 180
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Why So Expensive?
Growth Hurts - $$$
Point-To-Point Integration
Components Interfaces
10 90
20 380
30 870
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Example – How bad can it get?
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Sample Product Information Flow
25
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Pricing Example – MDM in Action
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Pricing the sales transaction
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The Master Data Management Solution
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MDM Solution – Simplify and Improve
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MDM Solution - Interface Reduction
§ No point-to-point connections§ All messages go through hub,
not directly to recipient§ Master Data is managed§ Shared governance and
stewardship is now possible§ Hub processes messages
ü Content-based routingü Data transformationü Transaction integrityü Workflow guides process
Ma
ste
r D
ata
Hu
b
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Interface Reduction (continued)
Brokered Master Data Management
Gentle, Manageable Growth
Components Interfaces
10 20
20 40
30 60
Messag
e Bro
ker
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Interface Reduction (continued)
Comparison of Master Data Management Integration Approaches
Master Data Components
Interfaces
Point-to-Point
Brokered
10 90 20
20 380 40
30 870 60
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What does the MDM solution look like?
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Data Quality is embedded in the process…
Data Quality Process
Measure
Analyze
Standardize
Correct
Enhance
Match
Consolidate
Report
Normalize data values and
formats according to business
rules and third-party
references
Verify, scrub, and
appends data based upon
algorithms, business rules
provided from a
secondary source
Append additional data
enhancing the
information value
Identify duplicate
records within multiple
tables, databases
Combine unique data
elements from matched
records into a single
source
Provide reporting within
the data quality process
Quantifies the number
and types of defects
Assess the nature and
cause of the defects
Data Profiling
Data Cleansing
Data Enhancement
Match and Consolidate
Management Reporting and Oversight
ParseIsolate and identify
data elements in data
structures
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Measurable Benefits – Business
• Improve Customer experience and loyalty
• Shorten latency and response times
• Improve Quality in Delivery (e.g. perfect order fill rates)
• Improve Time to market (cycle compression)
• Improve productivity (more value-added activity)
• Preserve intellectual capital
• Encourage reuse - standardize on a repeatable processes
• Minimize Rework
• Improve management visibility into the business
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Measurable Benefits – Information Technology
• Modernize and Simplify Business Processes and Systems
• Define core master data once and use everywhere
• Standardize tools and processes– Adopt global data definitions, policies, and standards
– Adopt specific governance policies, procedures, and metrics
• Support the exchange of master data between disparate business systems
• Transform information and data from one structure and format to another and enrich the same data where needed or requested
• Reduce costs of operations and maintenance
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What to do next
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Next Steps
• Identify the extent of the master data problem– Choose which subject areas to attack first
• Quantify the business value – Business Value Index in prioritized order
– If needed, determine the total cost of ownership
• Define an architecture that delivers in measurable phases
• Evaluate Organizational gaps– Organization’s capability to deliver
– Organizational commitment
• Create models of the data to be managed– Common Information Model
– Canonical Model
– Operating Model
– Reference Architecture
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Understand the MDM Implementation Effort
• 10% MDM software implementation
• 40% Governance Establish governance and document master data architecture
• 50% Data remediation Clean-up to meet the new rules
– Find duplicates
– Eliminate discrepancies
– Fill gaps
• Get the right people involved early. The technicians can wait until the planning and business specifications are well defined, completely understood by stakeholders, and are ready to be applied
AMR Research - MDM Strategies for Enterprise Applications, April 2007
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Ensure the organization’s capability to deliver
Do not try to build a system whose complexity
exceeds the organization's capabilities
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Use Next-Generation Technology
;3.5 MM 20 MM* ^ | G = "MM" | ^ | G = "MM" | [{FM}="CATHETER" ] | [{T1}="BALLOON"]| [{BR}!="OPTIPLAST"]COPY [1] temp1COPY_A [2] {Q1}COPY "X" {C1}COPY [3] temp2COPY_A [4] {Q2}RETYPE [1] 0RETYPE [2] 0RETYPE [3] 0RETYPE [4] 0CALL Ballon_Measurements
1st Generation:
Coded rules
Built by: IT
Method: Syntactic(based on patterns)
Reuse: Poor (does not generalize)
Timeline: Years
2nd Generation:
Visual rules
3rd Generation:
Auto Learn Inference
Built by: SME
Method: Semantic (based on context)
Reuse: Very good (generalizes well)
Timeline: Months
Built by: System – assisted by non-technical SME
Method: Semantic (based on context)
Reuse: Very good (generalizes well)
Timeline: Hours
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and most importantly…
• Not a tool or “technology buy” alone
• A set of methods and processes accomplished through:
– discipline,
– organizational commitment, and
– the use of the right technology at the right time.
Master Data Management is:
to produce an accurate and consistent set of information within a unified process improvement initiative.
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Master Data Management
For the Business Professional
Thank You…
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Mr. Parnitzke is a hands-on technology executive, trusted partner, advisor, software publisher, and widely recognized database management and enterprise architecture thought leader. Over his career he has served in executive, technical, publisher (commercial software), and practice management roles across a wide range of industries. Now a highly sought after technology management advisor and hands-on practitioner his customers include many of the Fortune 500 as well as emerging businesses where he is known for taking complex challenges and solving for them across all levels of the customer’s organization delivering distinctive value and lasting relationships.
Contact:[email protected]
Blogs:Applied Enterprise Architecture (pragmaticarchitect.wordpress.com)
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