William Mcknight - Master Data Management

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UnderstandingMaster Data

anagemen an eBenefits

William McKnight

Partner, Information Management

Lucidity Consulting GroupFebruary, 2009


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Agenda MDM Introduction

MDM Justification and Outcomes

MDM Architecture Approaches

Organiztional Roles for MDM Success


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ArchitectureWhat Is MDM?

MDM:MDM: The organization,management and

ETL

Modeling

distribution of corporatelyadjudicated, high-qualityinformation with

widespread use in theorganization for businessgain

Data Qualityand 3rd party

data

MDM ROI

Organization

Workflow

From Justifying and Implementing Master Data Management by William McKnight, DM Review April 2006


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MDM Subject Areas Customers (CustomerData Integration)

Products (ProductInformation Management)

Parts (Parts Data

Partners Policies

Stores Geography Org Calendar

#1

#2

anagemen Product (Product and

Content DataManagement)

Vendors

Warehouse Sales Hierarchy

Forrester sized the worldwide MDM market at $1.1 billion in 2006 andForrester sized the worldwide MDM market at $1.1 billion in 2006 and

forecasts that market will grow to $6.7 billion by 2010forecasts that market will grow to $6.7 billion by 2010


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Characteristics of MDM Subject

Areas

Areas of high interest to the corporation

Areas with high impact on many projects

Areas of potentially high controversy as to itsil

Areas with diverse input to its build

Areas with difficulty assigning stewardship for


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Challenges to Implementing

MDM Lack of centralization

Lack of documentation Lack of cross-departmental working relationships

Lack of data stewardship

Conflicting master files Data misunderstandings

Lack of metadata

Inconsistent lexicon Other priorities

Lack of subject-area culture

Poor Data Quality


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Tangible Deliverables of

MDM

Data Model suitable for enterprise needs Master data publish and subscribe mechanisms;Options:

O erational master data mana ement hub

Synchronize from existing operational masters

Role-based workflow for business process todevelop master data

Improved data quality Syndicated data take-on

Conformed Dimensions for the Enterprise


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MDM Justification andOutcomes


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Investments in MDM

As an enabler, means improved projectresults

projects

Project benefits are indirect

MDM also enables lower operating costs


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MDM Business Objectives Vary By

IndustryIndustry Scenario

Banking

Customer Intimacy: Effective direct marketing &Retention

Compliance, Privacy and Risk Management

Retail

Channel alignment & time to market

Strategic product sourcing, GDSN and EPC

Customer ex erience lo alt & direct marketin

Communications,Media & Utilities

Channel convergence

New Product Introduction Service creation

Customer experience, loyalty & direct marketing

Public Sector Case management

Single view of Taxpayer / Citizen / Terrorist

High-Technology &Manufacturing

Improve order accuracy and reduce inventory

Hierarchy Management capabilities for B2B

Product introduction, compliance and sourcing


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MDM Manages Master Data as a

Resource to Numerous CompanyProjects

6

8

10

-6

-4

-2

0

2

4

MDM

Costs

Project 1 Project 2 Project 3 Project 4 Project 5

MDM Project Cost

Data Management savings per

project

ROI

Hypothesis: 5 projects / year, 40% of costs of data management in each project, Reduction: 50%, 10%

MDM maintenance costs


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MDM (Banking, Customer)

Reduced Overexposure

Trade Financing

$35 MM

ACME

BankEurope

ACMEBank

Customer

Corp.Europe

Global CreditLimit $50 MM

CustomerCorp.

Lack of Single View of the Customer across silos leads to routineLack of Single View of the Customer across silos leads to routineoverexposure. ACME Bank should have denied one of the trade creditoverexposure. ACME Bank should have denied one of the trade credit

facilities or demanded a higher interest rate.facilities or demanded a higher interest rate.

BankUS

Trade Financing$45 MM

Total Credit Extended = $80 MM ACME Corp isoverexposedby $30 MM

Corp.US


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MDM (Customer) in Marketing

Improves Response and Reduces CostCustomer Example

24% Duplication rate+ 13% wrong addressx 4 marketing campaigns per year

x $0.50 Cost per customer= $1.2 MM Total annual marketing waste

Customer Example1.4 million prospects not reached

= 14,000 potential new customers missedx $983 lifetime revenue per customer= $14 MM Total potential revenue missed

Product Example30% item data error rate in retail files

X 25 minutes of manual cleansing perSKU per year

= $60-$80 cost per error+ 60% error rate for all invoices

generated= $40-$400 per invoice to reconcile

errors= 3-5% of total revenue


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MDM Improves Cross-Sell and

RetentionEconomics of Cross-Selling in Banking

Customer Households

2 Products, we earn an averageof $176/year

9+ Products, we earn an average

Wholesales Customers

3 Products, we earn $5,000

Every transaction is collected and combined with personal data that the customerEvery transaction is collected and combined with personal data that the customer

provides. The bank comes up with prospective offerings just at the right time toprovides. The bank comes up with prospective offerings just at the right time tocoincide with a lifecoincide with a life--changing event.changing event.

Source: Wells Fargo Investor presentation

of 795+/year

Current Average= 4+products/household

8 Products, we earn $58,000 14 Products, we earn $285,000

Current Average= 5 products percustomer


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How to Attain Business

Qualification Usually a matter of the best estimates of those who

should know

Through the project justification for projects with alarge component being master data

Throu h measurin the ro ect cost and risk of

managing master data


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MDM With Focus On the

Customer Master Enabling Projects

Clustering customers by commonalities

Ranking customers by important measures, suchas profit or spend

Customer Lifetime Value (CLV) ,

is a Straight-line projection of the last n years of profits or Average projected CLV of a representative customer segment

ValueChurn ManagementMarketing Expense Management

Targeted Marketing Cross- and Up- Selling


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MDM ArchitectureApproaches


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Information Architecture

Separate MDM

SyndicatedData

OperationalEnv

Master data can be

managed here

Master data can bemanaged here(synchronization)

Master data canbe managed here

hub

Data Warehouse


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Product

CRMERP

Master Data Landscape

Hub database

Customer Part

Promo

Brand

PartsDatabase

Marketing

Database


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MDM Architecture Approaches: Data

Warehouse as Master (Default)

DW is Master

DW provides unique ability for DQ

a ency an s r u on are ssues

Query master data in the DW

Unless its an active, operational DW, this is

seldom sufficient


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MDM Architecture Approaches:

Separate MDM Hub New MDM database into operational systems

May or may not be data entry system

To move data Flat file export (CSV, XML, other) and load

EII

oo n up

Web Services API

Can be Scheduled or triggered, full or subset ofsubject area

ETL/EAI/EII MDM data to operational systems and DWthat need it

Requires MDM understanding of target systems

Portal to MDM database for master data query


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MDM Architecture

Approaches: Synchronization

Identify location of master data within (mostly)operational environments

No specified data entry system Master data moved throughout environment

Portal to virtual master data database

Less performing than physical hub approach


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Wither the Data Warehouse?

Cannot access data in the operational

environment Need for integrated data

Need to have data modeled for access andanalytics somewhere in the organization

Need a place with data quality

Need a place for corporate master data


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Elements of a Master File Most elements available for the subject area

Data is documented and well-understood

Data is predictable

Data currency is high

Data is atomic level

You usually have little to no choice about what

is the master file, but you may have to dealwith many deviations from best practices inthose files


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Reconciling Customer Across

Multiple SourcesSource #1 (TX)Source #1 (TX)

SSN_NOSSN_NO X(9)X(9)Cust_NOCust_NO X(10)X(10)Div_eff_dtDiv_eff_dt X(10)X(10)

Source #2 (OK)Source #2 (OK)Pol_IDPol_ID 9(9)9(9)Cst_NOCst_NO X(10)X(10)Stt_dtStt_dt X(8X(8

Source #3 (LA)Source #3 (LA)Cust_IDCust_ID X(10)X(10)Claim_IDClaim_ID 9(9)9(9)Beg_dtBeg_dt 9(8)9(8)

MDMMDM

CLM_IDCLM_ID Dec(15)Dec(15)CUST_ID Dec(13)CUST_ID Dec(13) EFF_DTEFF_DT DATEDATE

MEMBERMEMBER CLAIMCLAIM GROUPGROUP


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Data Usabilit andQuality


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Investments in DataQuality

DQ = Absence of Intolerable Defects Investments Yield Cleaner Data

us ness o ec ves canno e me w ouquality data in support

Data Quality Returns are in the improved

efficacy of projects targeting businessobjectives

Data Quality should be an integral part of

most projects


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Data Quality Rule Categories: 1Category Example

Referential

integrity

Order is placed for customer # 123,

who exists in the customer tableUniqueness Only one customer exists with

customer # 123

Cardinality We may expect to find between oneand three addresses for a customerno more and no less

Subtype/supertyp

e constructs

A customer can be divorced or not, but

only divorced customers have a valuein the ex-spouse name field

Value

reasonability

Last-year purchases should not be >

$500,000 or < 0


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Data Quality Rule Categories: 2Consistent Value

Sets

In a Name field, we would not expect

to find certain characters such as %and $

Formatting No period after middle initial

Data derivation While one system may contain discount

,

percentage, a simple divisioncalculation, could be a unrelatedcalculation

Completeness Sales data covers each day frombeginning to current date

Correctness William mcnight instead of WilliamMcKnight

Conformance to a In the gender column we would expect


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Data Quality Scoring

Scoring defines how well your data meets business expectations

Possibilities

a va ues , , :

43%+45%+8%=94%

. .,

Gender:

Multiple rules used to determine overall system score


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Data Quality Improvement

Three Actions to Perform on Data Quality

Quarantine Data to Prevent Improper Use

Report on Quality Violations to Raise

AwarenessChange or Repair Incorrect Data to Conform,

when possible


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Cost/Benefit of Adding Data

Quality Efforts

DQ Score investment *

ROI (return-investment/investment) *

additionalcost toimprove DQto the level

Revised Project ROI(return-(investment+DQcost)/ investment+DQ cost)

. .

90 400000 175.00% 200000 83.33%

85 390000 101.92% 75000 69.35%

80** 380000 64.47% 0 64.47%

*without considering costing for DQ improvements **Default score will happen without data quality improvements

BestValue

Proposition


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Or anizational Roles

for MDM Success


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Supporting Programs

Executive

Sponsor

ProgramGovernance

Program Governance influences data stewardshipProgram Governance influences data stewardship

and data stewardship is responsible for data qualityand data stewardship is responsible for data quality

Data Stewardship

Data Quality


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MDM MaturitySelective master data intransactional environmentData stewardship coverssubjects implemented

Beginnings of DQ programCorp. begins to understandMDM

Selective master data intransactional environmentData stewardship coverssubjects implemented

Beginnings of DQ programCorp. begins to understandMDM

All master data intransactional environmentFeeding ALL systemsneeding the data

Full DQ programAutomated distributionFull data stewardship

All master data intransactional environmentFeeding ALL systemsneeding the data

Full DQ programAutomated distributionFull data stewardshipM

oreR

eturnon

Investment

Selective data stewardsSelective master data in DWData quality catch as catchcan

Selective data stewardsSelective master data in DWData quality catch as catchcan

No Data StewardsApplication-focusedArchitectureNo focus on DQ

No Data StewardsApplication-focusedArchitectureNo focus on DQ

Immature MDM Mature MDM

Low

Returnon

In

vestment

MDM S S


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MDM Steps to Success

Subject area definition

Data Stewardship Program

Subject area prioritization

Architecture selection Data Quality Program

Technology selection

Iterative development


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Presented by:

William McKnight

Partner, Information Management

Lucidity Consulting Group(214) 514-1444

Fax: (800) 886-7033

[email protected]

www.luciditycg.com

Blog: www.b-eye-network.com/blogs/mcknight

S k Bi


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William McKnight

William functions as strategist, lead enterprise information architect,and program manager for complex, high-volume full life-cycle

implementations worldwide utilizing the disciplines of datawarehousing, master data management, business intelligence, dataquality and operational business intelligence. Many of his clientshave gone public with their success story. William is a Southwest

Speaker Bio

Entrepreneur of the Year Finalist, a frequent best practices judge,

has authored more than 150 articles and white papers and givenover 150 international keynotes and public seminars. His teamsimplementations from both IT and consultant positions have wonbest practices awards. William is a former IT VP of a Fortunecompany, a former engineer of DB2 at IBM and holds an

MBA. William is partner of Information Management at LucidityConsulting Group. He may be reached [email protected] or 214-514-1444.

William Mcknight - Master Data Management

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