Creating Data Quality Rigor for Your Core Data Categories

Paul BertucciEnterprise 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

3

Initiative-based data strategy

6

Initiative-based Data StrategyStrategy• Identifies key benefits• Seeks out alignment• Sets direction and priorities.

Initiatives• Short duration• Specific ROI• Incremental

Foundationalactivities• Mandated• Enterprise-wide• Ensure business

alignment• Focused on data

management and infrastructure.

. . .

DataStrategy Data Quality

Governance

Architecture

Implementations creating incremental value

Initiative-based Activities

Foundational Activities

7

What must be done to execute on the strategy

8

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,

9

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

10

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)

11

A data architecture to support you

12

Strategic Data Architecture

META DATA

DataWHSE

CustomerDimensions

DWServices

MetaData Services

ODS Services

ERP 2

ERP 1

Web Services * Business Objects * Portal * Other

ERP – Next Acquisition

NextDW

Data Analytics

Mas

ter D

ata

Man

agem

ent

Applications/ComponentsTransactional System

s

MetaData

Other Transactional

ODS SFA

ODS ERP 2

ODS ERP 1

ODS CDH

NextODS

ODS

Extranet TransactionalNext Extranet System

xODS RC

Next xODS

ETL

REPL

REPL

EAIBackbone

Application Integration Services

Data Enrich

Hier Mgmt

Abstracted Services

Data Hub Services

Product/PricingData Hub

Customer Data Hub

NextData Hub

License KeyGeneration

MMDServices

Next Common Service

13

A data category example(Customer data)

14

Customer Data Strategy

ENTERPRISE CUSTOMER

MID-MARKET CUSTOMER

CONSUMER CUSTOMER

SMALL BUSINESS CUSTOMER PARTNER

BUSINESS CUSTOMER

Government Education SoftwareDevelopers

Other Segments

“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.”

TBD. . .

15

Some of the Problems (Symptoms)

Orders

Finance

SFA

CRM

Edu

ERP

DataWarehousing

FinanceReports

SalesReports

Partners

Customers

Direct

EDI

Partner Svcs

EduCRM ERP

Transaction Processing

Reporting

Leads

= “data hygiene/correction/reconciliation”

• 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).

16

Some of the Problems (Symptoms) (cont’d)

Orders

Finance

SFA

CRM

Edu

ERP

DataWarehousing

FinanceReports

SalesReports

Partners

Customers

Direct

EDI

Partner Svcs

EduCRM ERP

Transaction Processing

Reporting

Leads

= “data hygiene/correction/reconciliation”

• 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.

17

CONTACT LEAD OPPORTUNITY

PROSPECT

CUSTOMER

PARTNERCHANNELS CUSTOMER

PARTNER

How Do You Identify a Customer?

Account_ID, Email Address, Per_ID, Order_ID, Login ID, Name [+], Renewal_ID, others?

Contact ID, Party ID, Portal_ID,Company ID, Customer Nbr, DUNS Nbr, Name, Canonical ID,Support ID, others?

Partner Nbr, Party ID, others?

CONSUMER CUSTOMER

ENTERPRISE CUSTOMER

MID-MARKET CUSTOMER

SMALL BUSINESS CUSTOMER

BUSINESS CUSTOMER

PARTNER

CONSUMER CUSTOMER

18

Customer Data Dilemma

CustomerA

CustomerB

CustomerC

CustomerB

CustomerD (B)

CustomerE

CustomerB

CustomerF

CustomerG

ERPERP(M&A)

CRM

CustomerB

CustomerH

CustomerI

SFA

ERP ID Party ID Contact IDCRM ID

CustomerB

CustomerH

CustomerX

D&B(enrichment)

DUNS #

No strategy or consistency within a silo, or across silo’s

19

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, …)

20

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)

21

• 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

A Customer Data Strategy Should Provide: (cont’d)

22

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

Market Contact/Response Lead Opportunity Quote Order Fulfill Service Support

Marketing Sales Service Sales

Renewal

Fulfillment

23

Customer Data Management

Customer Data Across the Enterprise

Customer Support

MDM

Customer Order (ERP) Customer Sales (SFA) Customer

Customer Intelligence Partner Master Marketing Customer

• Customer ID• Customer Type• Initial Source• Primary Contact Details• Hierarchy Info (D&B)• Classification Details

• Customer ID• ERP Customer Number• ERP Cust Master Details

• Customer ID• SFA Customer Number• SFA Cust Mast Detail• Sales Classifications

• Customer ID• DUNS Info• Customer Profile Data

(Harte Hanke, D&B 1784, SFA Intelligence)

• Customer ID• Marketing Cust Details

LOCATION GROUP

SALES ENTITYPERSON

ORGANIZATION

EQUIVA-LENCY

NAMERELATION-

SHIP

CONTRACTROLE

IDENTIFIER

CONTACT METHOD

ADDRESS GROUPCONTACT METHOD

GROUP

ADDRESSROLELOCATION

ROLEIDENTIFIER

ROLELOCATIONPURPOSE

MACROROLE

GROUP

• Customer ID• Partner ID• Customer Profile Data

(Harte Hanke, D&B, SFA Intelligence)

• Customer ID• CS Customer Number• Titan Cust Master Details• Support Classifications• Support Entitlements

24

Making the strategy a way of life

25

Business (CDM)

IT/Data Architecture

Model-driven Customer Data Management

LOCATION GROUP

SALES ENTITYPERSON

ORGANIZATION

EQUIVA-LENCY

NAMERELATION-

SHIP

CONTRACTROLE

IDENTIFIER

CONTACT METHOD

ADDRESS GROUPCONTACT

METHOD GROUP

ADDRESSROLELOCATION

ROLEIDENTIFIER

ROLELOCATIONPURPOSE

MACROROLE

GROUP

Systems/Applications

Customer Model

Standardization

Market Contact/Response Lead Opportunity Quote Order Fulfill Service Support

Marketing Sales Service Sales

Renewal

Fulfillment

26

How the Strategy Becomes Reality

CRM

CommonParty-based

Model

ERP

Drives

Consistent with

Project-level Customer Models

LOCATION GROUP

SALES ENTITYPERSON

ORGANIZATION

EQUIVA-LENCY

NAMERELATION-

SHIP

CONTRACTROLE

IDENTIFIER

CONTACT METHOD

ADDRESS GROUPCONTACT

METHOD GROUP

ADDRESSROLELOCATION

ROLEIDENTIFIER

ROLELOCATIONPURPOSE

MACROROLE

GROUP

27

Movement to Data Hubs (MDM)

ERPCustomer DB

CRMCustomer DB

PartnerCustomer DB

ERP CRM Partner

FinanceReporting

Sales Reporting

Other

Customer Data Interactions

Data Quality

Integration Services

CustomerData Hub

ERP CRM

Partner Other

First up, a customer data hub

28

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 enforcementsuch as Global Customer ID's (canonical ID's).

29

BPELWS EAI ETL/EII

AnalyticsViews

Integration services

ODS

360 °CustomerTransaction

Views

CustomerData Hub

HistoricalAnalytics

Real TimeAnalytics

CustomerService

CustomerID Mgmt

CustomerLoyalty

Etc.

DW

Business Objects/Portal/ApplicationsDM

. . . .

Customer Data Hub

ERP DataAuditing

CustomerData Model

DataVersioning

Data Standardization

DataCleansing

DataPurge/Arch

Data Recognition

Data Enrichment

BusinessRules

CRM

SFA

SVC M&A

30

• ERP• Customer support• Services• Partner systems• Consulting services• Sales force automation• CRM• Contacts/leads• Data enrichment (D&B, Harte Hanks, …)

360-degree View of Customer

31

Customer Abstraction

Sales Entity

Role/Relationship

Specific Reference

1N

NN

“ERP System”

“342990667”

“100022” [“General Electric”]

“29903689”

“DUNS System”

“CRM System”

“118902”

• 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)

32

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

33

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.