Transcript Implementing Enterprise Information Programs with MDM

DAMA-NY 2008
Implementing Enterprise Information
Programs with MDM-CDI & SOA
Larry Dubov, Sr. Director, Sales Consulting & Architecture
New York, NY
May 15, 2008
[email protected]
Copyright © 2008 Initiate Systems, Inc.
Agenda
 Definitions
 Why MDM and EDM Now and Key Challenges
 MDM and Data Hub Capabilities
 Data Stewardship Framework and Information Quality
 SOA and Data Services: Strengths and Weaknesses
 Information Management Methodology
 Lessons Learned and Accelerators
 What are the next disruptive things in EDM and MDM?
2
What is Master Data Management (MDM)?
 Master Data Management (MDM) is a framework of
processes and technologies aimed at creating and
maintaining an authoritative, reliable, and sustainable,
accurate, and secure data environment that represents a
“single version of the truth,” an accepted system of
record used both intra- and inter-enterprise across a
diverse set of application systems, lines of business, and
user communities.
 Master data are those data which are foundational
to business processes, are usually widely distributed, which,
when well managed, are directly contributing to the success
of an organization, and when not well managed pose the
most risk
3
Customer Data Integration (CDI) is the “Entry Point” to MDM
CDI Focus is
on Individual &
Organizational
Entities:
MDM
MDM Expands
the Problem to
Include New
Entities:
 Customers
 Products
 Prospects
 Equipment
 Patients
 People
CDI
 Financial Assets
 Vessels
 Citizens
 Containers
 Employees
 Weapons
 Vendors
 Locations
 Suppliers
 Drugs
 Trading Partners
 Vehicles
Party (CDI)
4
Product (PIM)
Key Challenges
 Very complex,
multidimensional,
and multi-disciplined and can
be risky
 Difficult to sell data initiatives
to the business and
executive management
 No single vendor provides
a comprehensive solution
 These factors mandate
development and reliance
on sound models, open
integration standards, and
methodologies in building
holistic solutions from
multiple best-of-breed
components
New Customer &
Relationship Centric
Business Processes
Data
Governance &
Standards
Metadata
Management
Customer
Identification,
Correlation &
Grouping
External Data
Providers
Visibility,
Security,
Confidentiality,
Compliance
Service
Oriented
Architecture
Information
Quality
Data Acquisition,
Distribution &
Synchronization
(Batch & Real Time)
5
CDI
Consuming
Applications
Business &
Operational
Reporting
Exception
Capture &
Processing
Enterprise Customer Data Managed by Lines of Business
 Enterprises organized by Lines of Business and manage customer information
in a product-centric model with overlapping customer domains
Business Line 1: Business Line 2: Business Line 3: Business Line 4: Business Line 5:
6
Business Line
Products
Business Line
Products
Business Line
Products
Business Line
Products
Business Line
Products
Business Line
Customers
Business Line
Customers
Business Line
Customers
Business Line
Customers
Business Line
Customers
So, What’s the MDM-CDI Focus?
The Need to Transition from Product/Account to Party…it’s a Big Deal
Current State:
Future State:
Household
Client Grouping
Account 123:
Product 1
Account 456:
Product 2
Account 789:
Product 3










E-Statement
KYC
Acct & Client
Docs Approval
E-Statement
KYC
Acct. & Client
Docs Approval
Paper Statement
KYC
Acct & Client
Docs Approval
Spouse
Joe
 E-Statement
 KYC
 Client Doc Approval
Owner
Joe
Mary
Joe
Mary
Mary
Derived
7
Mary 1
Mary 2
Owner
(Joint)
Account 123:
Product 1

Joe
Mary
Acct. Attr. Only
 Paper Statement
 KYC
 Client Doc Approval
Owner
Beneficiary
Account 456:
Product 2

Acct. Attr. Only
Owner
Account 789:
Product 3

Acct. Attr. Only
Account Grouping
Drivers
Business Area:
Business
Development, Sales
& Marketing
Customer Service
Risk, Privacy,
Compliance
& Control
Operational
Efficiency
Drivers:









Cross sell/up sell to existing customers
Effectiveness of marketing campaigns
Recurring revenue from existing customers
Retain “good” customers by reducing attrition rates
Recognize “bad” customers









Risk Management
Accurate Books & Records
Compliance with AML & KYC Regulations
Compliance with corporate standards and policies
Regulatory fines and penalties


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Account setup time
Customer service time
Customer intelligence and level of service
Consolidated statements
Account setup costs
Customer acquisition costs
Administrative overhead of redundant data entry
Operation costs: duplication, redundancies, transaction errors, data processing errors
& exceptions
Failed tactical initiatives
Reduces costs of planned initiatives due to CDI
MDM Is Adding Value When You…
… Purchase Software
… Pick Up Your Prescription
… Apply for a Loan
… Check In & Earn Points
… Identify Risks
… File Insurance Claims
… Register a Patient
9
MDM and Data Hub Capabilities
Copyright © 2008 Initiate Systems, Inc.
Single Version of The Truth
Merge and
Persist
or
Composite
View
INFORMATION
J. Jones
(Name)
35 West 15th Street
(Address)
Toledo, OH
Sales
(Address 2)
INFORMATION
James
INFORMATION
(First)
James Jones
Jones
(Name)
35 W. 15th Street
(Address)
Toledo, OH
(Address 2)
(Last)
35 West 15th Street
(Address)
Toledo
Customer
Support
(City)
OH
INFORMATION
Jim Jones
M
(Name)
Gender)
35 West 15th St.
(Address)
Toledo, OH
(Address 2)
11
(State)
30
(Age)
E.R.
Single Version of Truth: Commercial Customers
D&B
Name
Addr
Cont.
Phone
ABC Incorporated
9146 E VIA DEL SOL
NETOWN, CA 45883
Joe Smith
480-473-5620
Trusted System
of Record
Back Office
Name
Cont.
Phone
ABC Inc.
Will Jones
480-473-5620
Name
Cont.
Phone
ABCC Incorp.
Joseph Smithe
304-473-5602
AIU
Name
Addr
Phn
12
Provides accurate, real-time access to
complete customer or entity data across
disparate sources, systems and networks
AB&C
9146 VIA DEL SOL
NETOWN, CA 45883
480-473-5620
INFORMATION
Name
ABC Inc.
Addr
9146 VIA DEL SOL
NETOWN, CA 45883
Cont.
Joseph Smith
Cont.
Will Jones
Phone
480-473-5620
Product
Product
Why Do We Need MDM?
(DW is Only as Good as Its Dimensions)
Customer
Customer
Can we really ‘slice and dice?’
Traditional Deterministic ETL may not be sufficient…
This is where Probabilistic MDM enabled by Data Hubs comes in
13
Star Schema Hubs
 If your MDM solution is BI driven, align your
MDM solution with complex DW dimensions
Time
Customer
Hub
Customer
Branch
Hub
Branch
Facts
Account
Account
Hub
14
Product
Status
Product
Hub
The Initiate MDS Solution in an Enterprise Architecture
Transaction
Support
Security &
Audit Trail
15
Master Data
Views
Performance
Scalability
Relationships
Hierarchies
Data
Stewardship
Batch
Support
Messaging
Matching
Accuracy
Orchestrated
Services
Loads &
Extracts
Sales
Web
APIs
CRM
Orders
Self-Service
Events &
Alerts
Initiate Master Data Service™
Web
Services
Call Center
Profiles
Marketing
Implementation Styles
Batch
Near Real-Time
Consolidation Style
Real-Time
Link
Co-Exist
Combine
Registry/Slave
Hybrid
Transaction/Master
Ownership Style
16
More on Matching and Linking
 Step 1: Optimizes data for statistical comparisons
 Normalizes & compacts data, creates derived data layer,

source data remains intact
Phonetic equivalences, tokenization, nicknames, etc.
 Step 2: Finds all the potential matches
 Casts a wide net – all matches on current or historical attributes,

prevents misses
Partial matches, reversals, anonymous values, etc.
 Step 3: Scores accurately via probabilistic statistics
 Compares attributes one-by-one and produces a weighted score (likelihood ratio)



for each pair of records
Frequency weights specific to your business
Edit distance, proximity of match
Allows custom deterministic rules, e.g. false positive filters
Should be linked
 Step 4: Custom threshold settings




Should not be linked
Single or dual threshold models
Link, don’t link, don’t know – “learns” from manual input
Manage cost/quality trade-offs
Don’t
Manage the linkages, workflow review
link
Lowest
possible
score
Manual review
Lowest
threshold
17
Upper
threshold
Link
Highest
possible
score
Hierarchy Management
 The term hierarchy is used only as a simple hierarchy
with one and only one root, only one parent for each
node within one hierarchy
 Typically one hierarchy is selected as the Master
used as a foundation (e.g. D&B or custom)
 There is a notion of source precedence / tree of truth
 High performance match to build the hierarchy 40MM
records for 15 minutes
 One original systems record (member) or single version
of truth record (entity) can belong to multiple
hierarchies (e.g. corporate for D&B and geography
with territories, regions etc.)
 A data steward can edit the hierarchy manually,
(e.g. if there is a knowledge of a merger)
 When later the merger update is coming from the Master
source, the data steward can reconcile the source merge
with the node previously created manually
 Hierarchy query and navigation is done using various
types of methods that allow to navigate to the node,
node’s immediate children and all the sub-tree below or
navigate from the node up and across (to be checked)
 The product can export a hierarchy
(e.g. to build a DW dimension)
18
Hierarchy: Management & Services
Understand & Visualize Customer Relationships
 Establish business
& consumer hierarchies
Hierarchy Source Data
Customer Data
19
 Resolve logical master from multiple
internal or third party source hierarchies
 Rule based hierarchy & relationship
creation and management
 Maintain individual & organizational
hierarchies through web application
to support active data stewardship
Initial Hierarchy Harmonization:
Original State of Customer Records
 Customer’s Organization Records are Fragmented
 Utility of hierarchies housed in SAP & other apps is inconsistent
Disclaimer: (Example Data Only)
Legend:
10
20
30
Existing (SAP) Relationships
Shipping Location
Pricing
ID:
20
Source:
40
Bill To:
50
60
70
80
Name:
Address:
Phone:
10
SAP
Harley-Davidson Inc
3700 W Juneau Ave
Milwaukee, WI 53208-2865
4143424680
20
SAP
Harley-Davidson Motor Co
3700 W Juneau Ave
Milwaukee, WI 53208-2865
4145353500
40
SAP
20
Andy’s Harley-Davidson
Business Highway 81 N
Grand Forks, ND 58203
7017756098
60
SAP
20
Shumate Harley Davidson
6815 E TRENT AVE
Spokane Valley, WA 99212-1252
5099286811
30
.COM
Buell Motorcycle Co
2815 BUELL DR
East Troy, WI 53120-1366
2626422020
80
SAP
Buell Motorcycles
8272 Gateway Blvd E
El Paso, TX 79907-1511
9155925804
30
90
Initial Hierarchy Harmonization: DNB Reference Source
1
2
3
4
5
6
ID:
ID:
21
Source:
Source:
Parent:
Parent:
Name:
Name:
Address:
Address:
Phone:
Phone:
1
DNB
1
2
DNB
DNB
1
Harley-Davidson Inc
Harley-Davidson Financial
Services, Inc
3700 W Juneau Ave
Milwaukee, WI 532082865
3700 W Juneau Ave
150 S Wacker
Milwaukee,
WI Dr
532082865
Chicago, IL 606064103
65
DNB
DNB
2
1
Harley-Davidson
Harley-Davidson Credit
Motor
Corp
Company Inc
4150 W
Technology
Way
3700
Juneau Ave
Carson City,WINV
897062009
Milwaukee,
532082865
7758863393
4143424680
3
DNB
1
Buell Motorcycle Company
2815 Buell Dr
East Troy, WI 531201366
2626422020
4
DNB
1
Harley-Davidson Europe
LTD
6000 Garsington Rd
Oxford, Oxfordshire OX4 2DQ
1865719000
5
DNB
1
Harley-Davidson Motor
Company Inc
3700 W Juneau Ave
Milwaukee, WI 532082865
4143424680
Harley-Davidson Inc
4143424680
4143424680
3123689501
Initial Hierarchy Harmonization: Target State
1
2
3
4
5
6
10
20
40
ID:
Source:
1
3
5
DNB
30
10
20
SAP
.COM
22
Parent:
Parent:
1
50
30
60
70
80
Name:
Name:
Address:
Phone:
Harley-Davidson
Harley-Davidson
Inc.
Buell
Motorcycle Motor
Company
Company Inc.
2815
Dr. Ave.
3700 Buell
W. Juneau
Milwaukee,
WI 531201366
532082865
East
Troy, WI
4143424680
2626422020
Buell
Motorcycle Motor
Co.
Harley-Davidson
Inc. Co.
Harley-Davidson
2815
DR Ave.
3700 BUELL
W. Juneau
East
Troy,
WI
Milwaukee, WI 53120-1366
53208-2865
2626422020
4143424680
4145353500
90
Resolve & Rationalize Hierarchies for Immediate Impact
10
2
Harley Davidson: Global Account Info:
Customer Locations / Bus Units
9
Current Yearly Purchasing
$900K
Added Potential
$300K
1
3
4
5
30
20
70
6
80
90
40
50
ID – Pricing Code:
20 – KFDRR
70 – [missing]
60
 Accurate Relationships Will Drive Revenue: DNB members
that are not yet Customers
 Will be properly included and targeted in marketing campaigns and in sales activity
 Misaligned Pricing or Territories: Unassigned or incorrect
track codes and rep assignments
Improved Customer Satisfaction, Improved Sales Coverage, Improved Sales Operations
 Incomplete Customer Profiles: Matched & organized
records required for accurate analytics
 Deliver complete customer relationships to Data Warehouse and Marketing Analytics apps
23
New Account Creation Scenarios
A
ID
24
Source
Parent
Name
A
New
Andya Harley Davidson
B
New
Buell Motor Cycles
C
New
Schumate Harley
Davidson
B
C
Address
Phone
HWY 81 N
Grand Forks, ND 58206
8272 Gateway Boulevard
El Paso, TX 77907
7001 E Trent Ave
Spokane, WA 99212
592-5804
Improve Account Creation Process & Data Quality
Pricing Code:
 Duplicate prevention
 Pricing alignment
 Territory assignment
KFFDE
1
10
2
3
A
4
B
C
60
70
5
30
20
Sales Territory:
6
840123
80
90
40
50
Duplicate: DO NOT ADD!
Assign Correct Track Code to New Account
Assign Correct Territory to New Account
ID:
Source:
Source:
Parent:
Parent:
Name:
Address:
6
90
40
SAP
.COM
SAP
2
30
20
Buell Motorcycles
Shumate
Harley Davidson
Andy’s
Harley-Davidson
8272
Blvd.
E.N.
6815 Gateway
E. TRENT
AVE.
Business
Highway
81
El
Paso,
TX
79907-1511
Spokane
Valley,
99212-1252
Grand
Forks,
NDWA
58203
N/A
840123
7017756098
B
C
A
[new]
[new]
Buell Motor
Cycles
Schumate
Harley
Davidson
Andya
Harley
Davidson
8272
Boulevard
7001 Gateway
E. Trent
Ave.
HWY
81
N.
El
Paso,
TX
Spokane,
WA77907
99212
Grand
Forks,
ND
58206
KFFDE
840123
25
Track Code:
Territory:
Phone:
Relationship Management
 Relationship is a much looser
construct than a hierarchy.
Relationship can be used to
associate people with group or
products with categories, etc.
 Relationships supports one-tomany and many-to-many
associations
 Relationships can be symmetric
and asymmetric
 For each relationship type its
cardinality (one-to-many or
many-to-many) is defined along
with its symmetry (symmetric
or asymmetric)
 Also when a relationship type is
defined, the types of records that
can be related are also defined
26
One-to-Many
Many-to-Many
Asymmetric
Symmetric
Information Quality and Data
Stewardship Framework
Copyright © 2008 Initiate Systems, Inc.
Approaches to Information Quality

“Upstream” at the point of entry of customer information
 Better validation
 Change in business process is likely required
 Change in applications, workflows and data flows

“Downstream”





Focus on ETL
Includes data stewardship
Less invasive – does not require changes in business processes
Less effective – always on the flow of dirty data
Combination of both “Upstream” and “Downstream” approaches
required to accomplish best results
28
Moving Resolution of IQ Issues Closer to Point of Entry:
Account Opening & Client On-boarding
Account-centric:
Customer-centric:
Data Entry
Data Entry
Customer
Information
File
Product 1:
Account 1:
Account
Attributes
Customer
Attributes
Account 2:
Account
Attributes
Customer
Attributes
Account 1:
Account
Attributes
Customer
Attributes
Product 2:
Householding
System
Data Hub
Product 3:
29
Account 2:
Account
Attributes
Customer
Attributes
Product 2:
Account 3:
Account
Attributes
Customer
Attributes
Account 4:
Account
Attributes
Customer
Attributes
Product 1:
Account 3:
Account
Attributes
Customer
Attributes
Product 3:
Account 5:
Account
Attributes
Customer
Attributes
Data
Enrichment
Vendor
Account 4:
Account
Attributes
Customer
Attributes
Account 5:
Account
Attributes
Customer
Attributes
Enterprise Data Stewardship Framework
Data Stewards
Data
Governance
Policies, standards,
processes, roles,
responsibilities,
metrics, & controls
Validity of Identifiers
Updates overlaying identity
Link
Merge
Conflicts between match
& hierarchy / relationships
associations
 Summary reports
on data quality metrics
30
Information
Technology
 Improves data
entry validation
 Configures
data quality task
generation & reports
Systems
Data Quality
Improvement
Loop
Technology Supports
Customizable Data Quality
Task Resolution Queues:





 Performs
ongoing data
quality task
resolution
InitiateTM Inspector: High Level Capabilities
31
InitiateTM Inspector: Primary Tasks
32
InitiateTM Inspector: “Potential Linkage”
33
InitiateTM Inspector: “Potential Linkage” – Select and Action
(Page 1 of 2)
34
Service Oriented Architecture and Data
Services: Strengths and Weaknesses
Copyright © 2008 Initiate Systems, Inc.
Service Oriented Architecture
 Software design and implementation architecture characterized by the
following:
 Logical View – abstraction of loosely coupled, reusable business needs
and functions;
 Message Orientation – exchange between provider and requester
 Description Orientation – machine-processable metadata to support
interoperabilty
 Granularity – combination of coarse-grained and fine-grained
 Network Orientation – typically used over network
 Platform Neutral
 Software architecture in which software components are exposed as
services on the network, which can be reused
as necessary for different applications
 When implemented with web services, offers a standard foundation for
functionality reuse and data access within a federated enterprise and
services provided externally
 What is in common between SOA initiatives and EDM or MDM?
 Data Services
36
Data Services: Format Agnostic & Source Agnostic Data Management
1
Executive
End User Interfaces with the
System by Requesting a data
source agnostic
Business/Data Service
Manager
Find
Client
List
Reports
Report
Premium
Revenue
Analyst
Create
New
Account
Create
New
Report
2
Data Services Metadata
Data Services Metadata
translates the course-grain
business service call into an
orchestrated set of data location
aware service calls. The Metadata
processes parameters of the
request and security access
eligibility and data visibility
parameters of the requestor.
Calculate
Transform
Data
CRUD
Record
Get Best Source
Data and Process It
3
Note: Metadata insulates user
applications and business services
from data sources. Thus the data
sources can be changed or replaced
seamlessly with no changes in user
interfaces and user experience
37
Capture
Exception
Data Sources
4
Execute and
Return to
Requestor
Data Hub: SOA Architecture Viewpoint
Service Consumers – Business Applications
External (Exposed) Services Layer







Individual Identification & Recognition
Privacy Preference Capture & Notification
Identification of Associations, Roles & Relationships
Customer Grouping Management
Compliance Notification & Reporting
Customer Information Maintenance
Customer Insight Reporting
Internal Services Layer






38
Key Services
Third-Party Data Interface Service
Data Synchronization & Queue Management
Data Archival & Versioning
Coordination & Orchestration
Visibility, Entitlements, Privacy & Security






Rules / Workflow Administration
Metadata Management
Transaction Logging & Auditing
Content Management & Caching
Event / Notification Management
Error Management
Data Provider
Service Interface
Data Provider
Service Interface
Data Provider
Service Interface
Legacy Data Store
CDI
Customer Hub
Other Data Store
ILLUSTRATIVE
Reference Architecture
Business Processes Layer
Contact Mgmt.
Campaign Mgmt.
Process Mgmt.
Orchestration
Relationship
Mgmt.
Document
Mgmt.
Hub Data Management Layer
Client/Suspect
Identification
Profile Mgmt.
Grouping
Mgmt.
Party Mgmt.
Enrichment &
Sustaining
Hub Data Rules Layer
Identity
Matching
Aggregation &
Split Rules
Synchronization
Rules
Visibility
Rules
Transformation
Rules
Rules Capture
& Mgmt.
Hub Data Quality Layer
GUID
Mgmt.
Address
Standardization
Data Quality
Mgmt.
Transformation
& Lineage
Reporting
Hub Systems Services Layer
Security
Visibility
Services
Orchestration
Transaction
& State Mgmt.
Data Sources
39
Persistence
Synchronization
Legacy
Connectivity
Pros & Cons of SOA: When to Use or Not to Use SOA
Pros
 Reduced data redundancy
 Business, data governance and
data stewards can define SLA
via services
 Data services provide level of
abstraction – no need to work at
the data and data model levels
 Standardized interaction within
the enterprise, external and
vendor provided services
 Increased productivity of
development and agility to
support evolving requirements
Cons
 Performance problems if multiple pieces of
information are to be joined
 If implemented with web services,
solutions do not support transactional
integrity for synchronous processes;
compensating transactions required
 SOA initiatives don’t meet expectations if
not supported by data strategy
 Use of data services requires strong
governance and a new culture for the data
governors, data stewards, and testers
When implemented properly SOA and data services provide
significant benefits for MDM and EDM
40
Testing Data Hub (Not Just Data But Also Services)
 Testing SOAP messages

 Testing WSDL files and using

them for test plan generation
 Web service consumer

and producer emulation
 Testing the publish, find, and

bind capabilities of a SOA
Data Hub
 Testing the asynchronous

capabilities of Web services
 Testing dynamic run-time

capabilities of Web Services
 Web services orchestration testing

 Web service versioning testing

41
Information Management Methodology
Copyright © 2008 Initiate Systems, Inc.
Importance of Information Management Methodology
 Implementation of enterprise information management projects
(DW, ODS, MDM, CDI, etc) require well structured methodology
 Methodologies used for data intensive projects are different from
traditional application development methodologies
43
Methodology – Need and Overview
 Mike2.0 (Method for an Integrated Knowledge Management) is an open
source for Enterprise Information Management
(www.openmethodology.org)
 Developed by BearingPoint
 Available for Open Source Community since December 2006
 Transition to Open Source “Creative Commons” license completed in May
2007
 Over 2000 online pages and growing
 Contains Phases, Activities and Tasks
 Open Source Mike2.0 allows global community to use this methodology
right now for their Information Development initiatives
 Organizations and individuals can sign-up to become a contributing
member of Mike2.0
44
Mike2.0 – Usage Model Details
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Mike2.0 SAFE Architecture
SAFE (Strategic Architecture for the Federated Enterprise) provides the technology solution
framework for MIKE2.0.
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Lessons Learned and
Accelerators
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The Three Dimensional Socialization Roadmap
Level of Involvement
Training
Testing
Development
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Lifecycle Phases/Releases
Planning
 Ownership:
Demonstrated
commitment to
the change and
accountability
 Buy-in: Agreement
with the concepts
and ideas &
expressed support
 Understanding:
Internalizing the
concepts and
ideas and grasping
the implications
of the change
 Awareness:
Becoming cognizant
and developing
a sense of
appreciation
for the change
Security
Front Office
Back Office
Senior Management
Technology & Infrastructure
Stakeholders
Legacy Systems
Helps Program Managers Build Communications Plan
Typical Implementation Work Streams:
Organizing for Success & Breaking the Problem Down
 It is much easier
to discuss, define
and plan MDMCDI when the
problem is broken
down into more
manageable areas
and specialty
domains
 Master Entity Identification
 Entity groups &
relationships
 Data governance,
standards, quality,
& compliance
 Data architecture
 Metadata management &
administrative applications
 Initial data load
 Inbound data processing
(batch & real-time)
 Outbound data processing
(batch & real-time)
 Changes to legacy
systems & applications
 Visibility & security
 Exception processing
 Infrastructure
 Data Hub applications
 Reporting requirements
of a stratified user
community
 Testing
 Release management
 Deployment
 Training
Helps Program Managers Build Project Plan
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Complexity vs. Manageability
Manageability
Complexity
Plan a Release Here
Critical Point
Helps Program Managers Define Phases & Releases
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Fastest ROI
High
Potential
End State
Business
Value &
ROI
ROIInitial
Resolve…
Synchronize…
Relevant
Relationships
in the Data
Data, Systems,
Processes &
People
Master…
Start w/ Resolve
Your Data
Start w/ Master
Low
CDIStart
Initial Phase
6
12
Time - Months
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18
24
Implementation Continuum
Pure Registry
Mastered
Customer Process
Management
Customer
Transaction
Management
Customer
Data Access
Customer Data
Synchronization
Real-time/
Operational
Batch/
Analytical
Cross Reference
Management
Create linkages
amongst all records
Prepare data for
new systems
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Establish and
maintain a trusted
source for analysis
Provide people
with on demand
search
Provide bidirectional update
between sources
of customer
information through
messaging, APIs
and other
integration
methods
Transactional
applications
built on top
of customer
definition
across sources
Transference of
record
ownership – Hub
owned and
maintained
Manage
business process
associated with
customer data/
transaction
management
Enterprise Information
Program
Develop Repeatable Initiative On-boarding
Processes & Templates
Program Initiation:






Business Case & Value Proposition
Business Requirements
Target State Solution
Detailed Roadmap
Data Governance, Standards, Data Quality
Architectural Principles
Program
Initiated
Global Geography
Program Planning & Definition
On-boarding Initiative 1
On-boarding Initiative 2
On-boarding Initiative 3
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DATA MODELING
DW DEVELOPMENT
CDI-MDM
DATA PROFILING &
DATA QUALITY
DATA SERVICES FRAMEWORK
DATA GOVERNANCE
& STEWARDSHIP
Develop Repeatable MDM Systems on-boarding
Processes and Templates
 In the first year and first implementation phase the number of legacy
systems integrated in scope of MDM-CDI is limited (typically 2-3)
 How to accelerate on-boarding of new systems in the consequent
phases given that it is not unusual that 20-50 systems can be in scope
of MDM-CDI integration?
 A well-defined set of system on-boarding standards and procedures
determines common rules that each legacy system should comply with
to be integrated into the evolving MDM-CDI solution
 Enables a repetitive on-boarding process and enables sustainable accelerated
solution growth in terms of the number of systems and LOBs
 Preserves integrity and consistency of the MDM-CDI solution
 Improved data governance
 Enables highly sustainable pace of
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Two Schools of Thought on Hub Data Model
Hub with Out-of-the-box Data Model
Data Model Agnostic Hub Product
Pros
Pros
 Seems attractive to have the “right”
data model out-of-the-box

Flexible to accommodate any data
model and its changes
 The product has some pre-built
coarse-grain business transactions

Can generate fine-grain services on
top of any data model
Cons
Cons
 How flexible is it to support ongoing changes?

Development work required to build
coarse-grain services to support
composite transactions

Possible performance and
maintenance impact due to
additional metadata lookups
 Overhead of having multiple
entities and attributes that never
used by your specific solution
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Hub Implementation: Buy vs. Build vs. Data
Enrichment Partner
 Traditional “buy or build” question is typically resolved in favor of “buy”
 An additional consideration is the use of an External Data Enrichment
vendor
 Can we outsource the primary function of CDI hub and do customer match
externally?
 Use of an External Data Enrichment partner has its own pros and cons
 Pros
 Higher match accuracy that based on the Knowledge Base (US NCOA and other
Libraries)
 Ability to recognize new customers and prospects
 Additional data from the Knowledge Base – “data enrichment”
 Cons
 Need to share customer data with external vendor
 Capabilities and Knowledge Base quality depends on the country – domestic
better than international
 Additional cost
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Focus on Data Mapping
 Data Mapping is an activity that “maps” the legacy system attributes to the
new customer-centric model and vice versa
 This activity is performed by business analysts
 The produced data maps are used by ETL and EAI developers
 The mapping process is time-consuming, can cause numerous errors and
can be on the critical path of development
 A data mapping vendor product can help accelerate delivery
 Drag-and-drop interface
 Open source mapping metadata
 Ability to integrate the mapping metadata with ETL, EAI and EII tools and
share the metadata rules
 Ability to reverse the transformation rules when possible
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Creation and Protection of Test Data
 Sensitive Customer data must be anonymized (obfuscated, cloaked) to
disguise it from unauthorized personnel in test and development
environments.
 Some anonymization techniques are as follows:
 Masking Data
 Substitution
 Shuffling records .
 Number Variance
 Gibberish Generation
 Encryption / Decryption
 Key challenges in using data anonymization techniques include:
• Ability to preserve logic potentially embedded in data to ensure that
application logic continues to function
• Need to provide consistent transformation outcome for the same data
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Some Key Reasons for Project Failure to Avoid at
Project Kick-off
 Lack of executive support and budgetary commitment
 Lack of cooperation and/or coordination between business and
technology
 Lack of consuming applications – “if we build, they will come…”
 Lack of end-user adoption
 Underestimation of legacy impact
 Insufficient socialization throughout the enterprise to include all
stakeholders at the right level
 Underestimation of the need for layered architecture provided by
SOA
 Gaps in data governance, stewardship, and information quality
strategy
 Miscalculated staffing needs
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What are the Next Disruptive Things in MDM and EDM?
 Match and link evolution: From entities to
relationships
 Integration of MDM with Business Rules Engines
and Work Flows
 Data Stewardship Framework
 Metadata Integration
 Externalization of data visibility and security
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Q&A
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