Database Architechs is a database-focused consulting company for 17 years bringing you the most skilled and experienced data and database experts with a wide variety of service offering covering all database and data related aspects.

1. Creating Data Quality Rigor for
Your Core Data Categories
Paul Bertucci
Enterprise Data Architect

2. Agenda
• Initiative-based data strategy
• What must be done to execute on this strategy
• A data architecture to support you
• A data category example (Customer data)
• Making the strategy a way of life
• Q&A
2

3. Initiative-based data
strategy
3

4. Initiative-based Data Strategy
Strategy Initiatives
• Identifies key benefits • Short duration
• Seeks out alignment • Specific ROI
• Sets direction and priorities. • Incremental
Initiative-based Activities
Foundational
Implementations creating incremental value activities
... • Mandated
• Enterprise-wide
Architecture • Ensure business
Data alignment
Strategy • Focused on data
Data Quality management and
infrastructure.
Governance
Foundational Activities
6

5. What must be done to
execute on the strategy
7

6. What Must Be Done
• Focusing on enabling key business initiatives
with the data they need!
• Introduce data governance for all critical data
(orders, product, employee…)
• Enhance/increase data quality across the board
(rules, gates, process, tools)
• Move to a data services approach (highly
sharing/leveraging data)
• Provide data SLA’s for data availability,
integrity/quality,
8

7. What Must Be Done (cont’d)
• Eliminate data redundancies (across the board) -
decrease P2P’s
• Put into play data integration capabilities to
enable M & A, accelerate current systems
consolidations (merger), and support other
group or divisional data acquisition in line with
the business speed
• Move to data hub concepts for key enterprise
data (Customer, Product,..) and other enabling
tools (e.g. HM) to elevate your ability to do
Master Data Management
9

8. What Must Be Done (cont’d)
• Roll-out a data certification process for all data
sources across the enterprise
• Create and maintain an enterprise reference view
of data (reference layer) and leverage industry
based models where ever possible (e.g. Party–
based models)
• Protect/secure the data (security/privacy
guidelines, roles, DR, backup, archiving)
10

9. A data architecture to
support you
11

10. Strategic Data Architecture
Applications/Components
ODS
Transactional Systems
Data
Next xODS REPL Next Extranet System
Analytics ERP – Next Acquisition
xODS RC Extranet Transactional
Next REPL Other Transactional
DW
Customer Next ERP 1
Dimensions ODS
ODS Hier Mgmt ERP 2
ETL
Master Data Management
CDH
ODS Data Enrich
ERP 1
Data Meta ODS
WHSE ERP 2 Abstracted Services
Data Next
Data Hub
ODS
Customer Next Common
META SFA
Data Hub Service
DATA
Product/Pricing MMD
DW MetaData ODS Services
Data Hub
Services Services Services
Data Hub License Key
Services Generation
Application Integration Services
EAI
Backbone
Web Services * Business Objects * Portal * Other
12

11. A data category example
(Customer data)
13

12. Customer Data Strategy
“Defining and setting how you will effectively identify, manage
and leverage customers and their core attributes across all
segments to best serve the business now and into the future.”
BUSINESS CUSTOMER
CONSUMER SMALL BUSINESS MID-MARKET ENTERPRISE
PARTNER
CUSTOMER CUSTOMER CUSTOMER CUSTOMER
Software
Other Segments Government Education
Developers ... TBD
14

13. Some of the Problems (Symptoms)
• Can’t recognize your customers completely (or
not at all sometimes)
• Burn lots of energy/$ with duplicate data entry,
consolidations, roll-ups & reporting . . . . and still
don’t have good information.
• Must apply data hygiene, corrections,
reconciliation in multiple places (not scalable,
not consistently applied, out of control).
Data
Orders Warehousing Reporting
Transaction Processing
Finance
Customers Finance
EDI
Reports
Partners Direct CRM ERP Edu SFA
Partner Svcs
Leads CRM
Sales
Reports
Edu
= “data hygiene/correction/reconciliation”
ERP
15

14. Some of the Problems (Symptoms) (cont’d)
• Don’t share a common view of your
customers/partners, and can’t provide one to
THEM, even when they ask.
• Don’t know what customers own (licenses,
maintenance, subscriptions), and can’t assess
compliance, coverage or cross-sell
opportunities.
• Struggle to append external information
(enrichment)
• Have difficulty measuring sales effectiveness.
Data
Orders Warehousing Reporting
Transaction Processing
Finance
Customers Finance
EDI
Reports
Partners Direct CRM ERP Edu SFA
Partner Svcs
Leads CRM
Sales
Reports
Edu
= “data hygiene/correction/reconciliation”
ERP
16

15. How Do You Identify a Customer?
CONSUMER CUSTOMER
CONSUMER
Account_ID, Email Address, Per_ID, Order_ID,
CUSTOMER Login ID, Name [+], Renewal_ID, others?
BUSINESS CUSTOMER
SMALL BUSINESS MID-MARKET ENTERPRISE
CUSTOMER CUSTOMER CUSTOMER Contact ID, Party ID, Portal_ID,
Company ID, Customer Nbr,
PROSPECT
DUNS Nbr, Name, Canonical ID,
CONTACT LEAD OPPORTUNITY CUSTOMER Support ID, others?
PARTNER
PARTNER
Partner Nbr, Party ID, others?
PARTNER
CUSTOMER
CHANNELS
17

16. Customer Data Dilemma
ERP ERP CRM SFA D&B
(M&A) (enrichment)
Customer Customer Customer Customer Customer
A B B B B
Customer Customer Customer Customer Customer
B D (B) F H H
Customer Customer Customer Customer Customer
C E G I X
ERP ID Party ID CRM ID Contact ID DUNS #
No strategy or consistency within a silo, or across silo’s
18

17. A Customer Data Strategy Should Provide:
• Consistent customer identification & recognition
• A single, consistent technique for recognizing and enumerating
customers (identification abstraction), sophisticated matching
capabilities (Fuzzy, AKA’s, so on), de-duping, merging, etc…
• Model-driven (party-based models, so on)
• Customer relationships & hierarchies
• Enables complex associations to our other customer data
(services, sales, opportunities, support, marketing, renewals,
so on) to provide the needed 360-degree views of customer
data
• Support multiple customer hierarchy views for different lines of
business (Fin, Sales, …)
19

18. A Customer Data Strategy Should Provide:
(cont’d)
• Customer data enrichment (internal/external)
• Enables any critical data expansion or data enrichment
from both internal systems (i.e. “customer segment
classification”) and external sources (D&B, HH, Axxiom,
so on)
• Customer data stewardship
(reconcile/resolve/publish/ownership)
• Group with sole customer data management
responsibility with appropriate counterparts out in each
line of business (extended/federated model)
20

19. A Customer Data Strategy Should Provide:
(cont’d)
• Customer data quality/consistency/full life
cycle management
• Single “stable” approach to applying data standards, data
cleansing, data quality metrics measurement, auditing,
and exceptions processing across the full life cycle for
this core customer data
21

20. Aligned With the Business
• Supporting prospecting (lead, opportunity)
• Supporting order quoting
• Supporting order capture (all channels)
• Supporting marketing campaigns
• Supporting customer service/support
• Supporting cross-sell/up-sell opportunities
• Supporting customer loyalty programs
• Supporting licensing/entitlements
• Supporting renewal
• Resolve financial reporting inconsistencies
• Compliance evaluation/customer G2
• Enabling 360-degree views that span different systems
Marketing Sales Fulfillment Service Sales
Contact/
Market Response Lead Opportunity Quote Order Fulfill Service Support Renewal
22

21. Customer Data Across the Enterprise
Customer Data Management
Customer Order (ERP) Customer Sales (SFA) Customer Customer Support
• Customer ID • Customer ID • Customer ID
• Customer ID • ERP Customer Number • SFA Customer Number • CS Customer Number
• Customer Type • ERP Cust Master Details • SFA Cust Mast Detail • Titan Cust Master Details
• Initial Source • Sales Classifications • Support Classifications
• Primary Contact Details • Support Entitlements
• Hierarchy Info (D&B)
• Classification Details
Customer Intelligence Partner Master Marketing Customer
• Customer ID • Customer ID • Customer ID
• DUNS Info • Partner ID • Marketing Cust Details
• Customer Profile Data • Customer Profile Data
(Harte Hanke, D&B 1784, (Harte Hanke, D&B, SFA
SFA Intelligence) Intelligence)
ROLE
LOCATION
MDM
PURPOSE
ROLE LOCATION GROUP ADDRESS
CONTRACT LOCATION ADDRESS GROUP CONTACT
ROLE CONTACT METHOD METHOD
GROUP
ROLE IDENTIFIER
IDENTIFIER RELATION-
NAME SHIP
EQUIVA-
LENCY SALES ENTITY
MACRO PERSON
ROLE ORGANIZATION
GROUP
23

22. Making the strategy a
way of life
24

23. Model-driven Customer Data Management
IT/Data Architecture
ROLE
LOCATION
PURPOSE
ROLE LOCATION GROUP ADDRESS
CONTRACT LOCATION ADDRESS CONTACT
ROLE GROUP
CONTACT METHOD
METHOD GROUP
ROLE IDENTIFIER
IDENTIFIER RELATION-
NAME SHIP
EQUIVA-
LENCY
SALES
MACRO ENTITY
PERSON
ROLE ORGANIZATI
ON
GROUP
Customer Model
Standardization
Systems/Applications Business (CDM)
Marketing Sales Fulfillment Service Sales
Contact/
Market Response Lead Opportunity Quote Order Fulfill Service Support Renewal
25

24. How the Strategy Becomes Reality
Common Project-level
Party-based Customer Models
Model
ERP
ROLE
LOCATION
PURPOSE
ROLE LOCATION GROUP ADDRESS
CONTRACT
ROLE
LOCATION ADDRESS
GROUP
CONTACT
METHOD GROUP
CONTACT
METHOD Drives
ROLE IDENTIFIER
IDENTIFIER RELATION-
NAME SHIP
EQUIVA-
LENCY
MACRO
ROLE
SALES
ENTITY
PERSON
ORGANIZATI
ON
Consistent with
GROUP
CRM
26

25. Movement to Data Hubs (MDM)
First up, a customer data hub
Customer Data Interactions
Partner Other
Integration Services
ERP CRM Partner Other
Finance
Reporting
Customer
Data Hub
Sales
Reporting
ERP CRM Partner
Customer DB Customer DB Customer DB
Data Quality
ERP CRM
27

26. Data Hub Criteria (To Qualify)
Data that is created/updated/deleted in more than one place
Data that has a need to be highly consistent (across many sources)
Data that requires many views (e.g. 360 view of Customer)
Data/Attributes that must live on their own
Data that must be correlated with other sources (e.g. D&B)
Data that must be highly available
Data that must be readily accessible (high performance)
Data that must have the high integrity
Data that requires a formal change management process
Data that requires abstracted (enterprise) rules enforcement
such as Global Customer ID's (canonical ID's).
28

27. SVC . . . . M&A
360 °
Customer
Transaction
Views
SFA
Customer
Customer Data Hub ID Mgmt
Customer Customer
CRM Data Hub Service
Data Data Business
Recognition Enrichment Rules Customer
Data Data Data
Loyalty
Standardization Cleansing Purge/Arch
ERP Data Customer Data
Auditing Data Model Versioning
Etc.
ODS
Analytics
Views
Integration services
BPEL Real Time
WS EAI ETL/EII DW Analytics
Historical
Business Objects/Portal/Applications Analytics
29 DM

28. 360-degree View of Customer
• ERP
• Customer support
• Services
• Partner systems
• Consulting services
• Sales force automation
• CRM
• Contacts/leads
• Data enrichment (D&B, Harte Hanks, …)
30

29. Customer Abstraction
Sales Entity “100022” [“General Electric”]
1
N
Role/Relationship “ERP System” “CRM System”
N “DUNS System”
N
Specific Reference “118902”
“342990667”
“29903689”
• Provides the insulation from moving parts (“n” customer sources)
• Provides a consistent representation to apply data rules, standards, and guidelines
• Provides a strategic basis for tools or systems (Data Hubs, ERP, CRM, Reporting…)
• Highly flexible for M & A and data leveraging (exposing customer views)
31

30. Summary
• Make sure you are aligned with what the
business needs
• Go after one core data category first !
• Leverage industry tools/models if possible
• Establish a data quality paradigm/group
• Be initiative based with incremental value
32

31. Abstract
Trying to solve the data quality issues across multiple divisions,
acquisitions, and user realms often leads to failure. Fundamental
process and tooling can greatly reduce these failures across the
board if they are focused on the primary (core) data categories of
your business. Raising the quality of this core data has a ripple affect
throughout the organization. In this session, you’ll learn how to
identify what the data quality problems are, what needs to be fixed,
what type of organization structure is needed, what type of data
guidelines and data strategy must be present, and which tools of the
trade you need to be successful in delivering all the benefits of high-
quality data to your organization.
33