master data management for the business professional

Master Data Management for the Business ProfessionalJim ParnitzkeAAJ Technologies

March, 2015

Introduction

Jim ParnitzkeBusiness Intelligence and Enterprise Architecture Advisor, Expert, Trusted Partner, and PublisherOver his career he has served in executive, technical, publisher (commercial software), and practice management roles across a wide range of industries.

3

What is Master Data Management?

Why this is important (What causes poor quality data?)

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

Discussion Topics

4Confidential and Proprietary

Essentials What is Master Data Management?



Master Data Management (MDM) is the practice of acquiring, improving, and sharing master data. The primary goal is to manage the consistent identity of business entities across multiple systems when needed.

Key disciplines adopted to improve data quality, share it broadly, leverage it for competitive advantage, manage change, and comply with regulations and standards.

Provides business capability to deliver value when data is:• accurate,• complete, • timely, and • consistent.


What is Master Data?

Reference data used across the organization…

• Party• Customer• Supplier• Employee

• Product• Contract Agreement• Pricing• Location• Asset• Hierarchies


Data Management – Types of Assets

• Master Data• 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

Why do we need this?We already capture this kind of data.What could possibly go wrong?

Real life example

Alyssa GreenMinneapolis, MN

Alyssa GreenAlbany, NY

Same DOB

Same SSN

Is this thesame person?

http://www.cnn.com/video/#/video/us/2010/06/24/dnt.women.same.ss.number.KARE.WNYT?hpt=T2

http://www.cnn.com/video/

How does this happen…

Aside from obvious privacy issues, this commonly believed way to uniquely identify a person living in North America – is well, not really unique…

• More than 40 million SSNs are associated with more than one (1) person

• More than 20 million people have more than one (1) SSN associated with their name

• More than 100,000 Americans have 5 or more numbers associated with their name

• More than 27,000 Social Security numbers are associated with 10 or more people

http://www.dailyfinance.com/2010/08/12/your-social-security-number-may-not-be-unique-to-you/

Facts

• 17% of Business Names change in a year (D&B)• 11 companies will change their name in the next 60 minutes…

• During 1995, 2.3 Million Marriages and 1.2 Million divorces took place (Census Bureau)• 6,400 marriages a day• 3,200 divorces a day

• 14% of the nation’s population moves every year• 45 million address changes (USPS) and• 18% result in telephone number changes

• Mail is big business (USPS) – over $900 billion a year

Extend this example to a business community…

ACME Devices Corporation1 Cedar St

Bob Jones9 Pine Center(8 Birch Way)

Bob Johnson (2)3 Oak Ct

Wendy Benson6 Redwood Ln

Peter Benson6 Redwood Ln

Drew Benson6 Redwood Ln

Roberta Smith (1)4 Cedar St

Regional Medical Associates9 Pine Center

St Elsewhere (1)8 Birch Way

ClientBroker+

Contact

Subscriber

Broker/Contact

Susan Jekyll (1)8 Birch Way

Isabella Johnson3 Oak Ct

Sam Smith5 Maple Dr

Big Insurance 2 Elm Rd

Bob Johnson (1)2 Elm Rd

Roberta Smith (2)5 Maple Dr

Subscriber

Member

Member

Member

Subscriber

Provider

Provider

Provider

Member

Client

Provider

Client

Susan Jekyll (2)7 Spruce Cr

Member

St Elsewhere (2)8 Birch Way

(same-as)

(same-as)

(same-as)

(same-as)

This is pretty complex…

In a complex environment

High Level Domains

Healthcare is changing rapidly and so is the industry’s need for reliable , trusted data…

Practice

Protocols

Processing

EDWAnalyzable data

Clinicians use diverse protocols and orders in daily care

Sub-Optimal State

© 2014 Denis Protti, Dale Sanders & Corinne Eggert

CDS:EDW:EHR:MTTI:

Clinical Decision SupportEnterprise Data WarehouseElectronic Health Record Mean Time To Improvement

Clinical Information SystemsDecisions and ActionsSupporting information

Clinical, EHR, EDW and Analytics Teams

Align metrics and data

Update EHR and EDW with new data items if needed where feasible

Start here

Monitor baselines and clinical processes

Select a problem

Set outcomes and metrics

Quality Governance

Clinical Variations and Needs

Internal EvidenceClinicians’ suggestions

External EvidenceLiterature, reports, etc.

Quality Governance

Use comparative data to identify best outcomes

Determine standard order sets, protocols and decision support rules

External EvidenceLiterature, reports, etc.

Analyze data quality and process/outcome variationsGenerate the internal evidence

Clinical Analytics

Other Data SourcesClinical, Financial, etc.

MTTILo Hi

EHR & CDSElectronic clinical data

Clinicians use standard protocols and orders in daily

care

Optimal State

Clinical, EHR, EDW and Analytics Teams

Update EHR protocols and EDW metrics

Enterprise Clinical Teams Act on performance information

Executive and Clinical Leadership

Set expectations for use of evidence and standards

Best EvidenceInformation that clinicians trust

Stan

dard

s

Performance

14

…trusted information is critical


Why Master Data Management is Important


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

17

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

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Name-relatederrors

Dupl icates Address errors Customer type Miss ingRelationships

Best Case (US)

Worst Case (US)

Average (US)


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…


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-relatederrors

Dupl icates Address errors Customer type Miss ingRelationships

Best Case (INT'L)

Worst Case (INT'L)

Average (INT'L)

Are you kidding? (this is a worst case outlier)

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 Contact Deceased – IndividualDissolved, M/A – Org, GroupDeceased – IndividualDissolved, M/A – Org, Group

Customer Relationships

Want to guess how well this works across independent applications and business operating units?


Results - Impacts the business where it hurts…

65%

10%

15%

10%

Contributors to Profitability

Market awareness, competitive position, leverage dominance, customer intimacy

Strategic moves initiated by business

Operating expense reductions

Blind luck and random events

Source: Empirimetric Corporation – from a sample of over 3000 businesses from over 300 corporations

Profit Impact of Marketing Strategy (PIMS) Project

Marketing Example

Source: William McKnight, SAP – Approach to Data Quality ROI 2008


What causes poor quality data?


A closer look…

Source: Aberdeen 2008

0%

10%

20%

30%

40%

50%

60%

70%

80%

At the data sourcelayer our data i s not

clean or managedproperly

At the integrationlayer our data

sources are notintegrated properly

At the end useraccess and

consumption layersusers introduce

errors

At the analyticsappl ication layer;

appl icationdevelopment

introduces errors

At the securi ty layer;access in not

control led properly


Essentials – How poor quality data is created

Existing ERP Application

Data Warehouse Analytical Systems

Existing CRM Application

Conforming Dimensions

Table (0) Table (0)Table (1)

Table (2)

Entity (1)Entity (0)

Master Data Tables


Table (2)


Master Data Tables


Table (2)


New Sales Force Automation Application

Master Data Tables


Table (2)


Transactional data

Transactional data Transactional data

Reference data

Reference data

Reference data

Existing applications have been designed and deployed by many independent developers separated by time, geography, and organization…

Most are complex, undocumented, and difficult-to-maintain; integrations solutions exist primarily because…

each connection or integration point was created locally

rather than globally optimized.

And worse…

2/3 of Data Management Survey respondents find that development teams go around their data management (DM) groups. Of those, 20% found that their DM group was too difficult to work with, 36% felt the DM group was too slow to respond, and 19% felt the DM group offered too little value. This is a clear indication a cultural impedance mismatch exists between developers and data professionals.

Data Group offers little value; 19%

Don't know it exists; 8%

Don't know they should work with them; 17%

Find the data group too difficult to work with; 20%

Find the data group too slow to work with; 36%

Source: Data Management Survey in the November 2006 issue of Dr. Dobb's Journal

http://www.ddj.com/dept/architect/193104893?cid=Ambysoft



http://www.ddj.com/

Results: 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

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

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

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

Why So Expensive?

Growth Hurts - $$$

Point-To-Point Integration

Components Interfaces

10 90

20 380

30 870

What about new information systems?

Hardware - Capital Equipment; 10%

Software; 10%

Training; 20%

Systems Integration; 10%

Data; 50%

• Hardware: The cost of additional infrastructure required for the project

• Software: The cost of licenses for the software used, or the cost of software developed

• Systems Integration: Cost of interfaces between applications in a system

• Data: The business cost of creating the data to configure and use a system

• Training: Cost of training and the 'cost' of getting accustomed to a new system

Source Daratech Inc., 2009

32

Example – How bad can it get?


Some Examples – MDM in Action

34

Customer

Table (0)Table

(0)Table (1) Table

(2)

Entity (1)

Entity (0)

Product

Table (0)Table

(0)Table (1) Table

(2)

Entity (1)

Entity (0)

Supplier

Table (0)Table

(0)Table (1) Table

(2)

Entity (1)

Entity (0)

Contract

Table (0)Table

(0)Table (1) Table

(2)

Entity (1)

Entity (0)

Location

Table (0)Table

(0)Table (1) Table

(2)

Entity (1)

Entity (0)

§ Right Party (Customer, Suppler)§ Right Product (Authorized)§ Right Terms and Conditions (Contract)§ Right Product Bundle (Components)§ Right Price§ Right Cross Reference§ Right Hierarchy§ Right Location (variant)

Sales Transaction

Pricing the sales transaction

35

Relationships (Party) – pretty simple, right?

36

Credit Risk for Financial Services…

CounterpartyCounterparty Id

Basel II Counterparty Type

Approved UCR Grade

Standard & Poor’s Debt Rating GradeMoody’s Debt Rating

GradeFitch Debt Rating GradeAAB Global Industry Classification

OrganizationObligor Group IdFinancial Statement Total AssetsFinancial Statement Total TurnoverResidence Country

IncorporationCountry

Financial StatementCurrency

Organization Size Measure

Type

Credit Facility ArrangementCredit Facility LimitReduction In Credit Facility LimitBorrowing Base LimitCredit Facility Accrued FeesSpecific ProvisionCredit Facility Start DateCredit Facility Maturity DateCredit Facility Term Out Date

Credit FacilityBasel II Product

Type

Committed Facility Indicator

Trading Book / Banking Book Indicator

Primary Currency

Approved Internal LGD Classification

Credit Facility Limit Reduction

Account Arrangement Applied Debit Interest RateDebit Interest Spread Rate

Drawing Basel II Product Type

Basel II Recognised Netting

Arrangement

Exposure As Claim Arrangement

ClaimClaim Accrued InterestClaim Accrued FeesPremium / DiscountNet Present Value Total Lease Payments

Exposure As Potential Claim Arrangement

Potential ClaimPotential Claim Accrued Fees

Debit Interest ComputationCalendar Basis

Collateral ArrangementGross Collateral Value

Deposit Arrangement

Cash BalanceDeposit Maturity Date

Basel II Eligible Collateral Type

EU Financial Eligible Collateral

TypePotentially Eligible

Collateral Type

Guarantee ArrangementGuarantee Nominal ValueGuarantee Materiality Threshold Guarantor

Credit Derivative ArrangementCredit Derivative Nominal ValueCredit Derivative Materiality Threshold

Credit Derivative Eligibility Type

Credit Derivativ

e ProviderCredit Derivative

Restructuring Clause

Involved PartyInvolved Party Unique Id .. Involved Party NameInvolved Part Description

Involved Party Type Individual

Ultimate ParentOrganization

Organization Unit

Product ArrangementBookingOrganization

Unit

AAB GAIN Reporting Entity

Indicator

AAB GAIN Reporting EntityHead Office Reporting Entity Number

Product Arrangement Type

Account Arrangement

ArrangementArrangement Unique Id ..Primary NameDescription

Arrangement Type

Arrangement Exposure Measurement Category

Credit Facility Management Type

Individually Managed Credit Facility Arrangement

Equity Investment Exposure Arrangement

Equity Investment Book ValueEquity Realised ResultEquity Accrued InterestEquity Specific ProvisionStandard Equity Maturity DateLoss Given Default % For Equity

Equity Investment Category

100% Risk Weight Indicator

Equity Investment Valuation Method

AccountArrangement

Type

Financial Instrument

ArrangementEligible Hedge

IndicatorHedge

ArrangementHedge AmountHedged

Financial Instrument

Arrangement

Hedge Provider

Financial InstrumentStandardised Securities Id

Standardised Securities Identifier

Type

Issuer

Equity Issuer

Deduct Regulatory Capital Indicator

EVCA Industry Category

Equity Issuer Stage Type

Financial Instrument Issue

TypePublic Issue

Exchange Organization Financial Instrument Product

Type

Equity Instrument

Credit Risk Mitigation Arrangement

Credit Risk Mitigation Maturity Date Credit Risk

MitigationArrangement

Type


Arrangement


Arrangement

Collateral Arrangement

Non Retail

Basel II Collateral

Type

Financial Instrument Collateral Arrangement

CollateralArrangement Type

Financial InstrumentStandardised Securities Id

Financial Instrument Product

Type

Equity Instrument

Moody’s Debt Rating Grade

Fitch Debt Rating Grade

Debt Security InstrumentDebt Security Maturity Date Mutual FundHighest Collateral Haircut

Credit Protection Arrangement

Credit Protection

Arrangement

Credit Protection Provider

Credit ProtectionArrangement

Type

Protected Arrangement

Type

Credit Facility Protection

Arrangement

Equity Protection Arrangement

Credit Facility Arrangement

Equity Investment Exposure

Arrangement

Debt Security InstrumentDebt Security Maturity Date

Involved Party Role Type

Arrangement Measure

Measure

EventPrimary NameDescriptionEffective Date

Unit Of Measure

Arrangement Measure

Type

Period Balance

Type

Event Type

End Of Period Reporting

Arrangement Event

Principal Repayment

Interest Payment

Credit Related Fee Payment

Collateral Valuation Arrangement

Event TypeInvolved Party Measure

Measure

Event

Unit Of Measur

e

Involved Party

Measure Type

Period Balance

Type

Event type

Financial Statement Publication

Drawing Arrangement Credit Facility Drawing Maturity DateCredit Facility Drawing Start DatePrincipal Repayment AmountInterest Payment AmountCredit Related Fees AmountWrite-Off

Drawing Arrangement

Indicator

Equity Basel II Product Type

Hedge Provider

Credit Protection Provider

IssuerEquity Issuer

Credit Derivative Provider

Guarantor

Standard & Poor’s Debt Rating Grade

Issuer

Moody’s Debt Rating Grade

Fitch Debt Rating Grade

Standard & Poor’s Debt Rating Grade

Standardised Securities

Identifier Type

Example: MDM Party Use in a Credit and Risk Management Scenario

ORGANIZATION ROLE

PERSON ORGANIZATION

PARTY

PARTY ROLE

# PARTY ID

# PARTY ROLE ID

ROLE TYPE

PARTY RELATIONSHIP

CUSTOMER RELATIONSHIP

tofrom

involved in

acting as

for described by

the description for

# PARTY ROLE TYPE ID* DESCRIPTION

PERSON ROLE

PARTNER

REGULATORY AGENCY

INTERNAL ORGANIZATION

ORGANIZATION ROLE

# FROM DATEo THRU DATE

ORGANIZATION UNIT

PARTY ROLE TYPE

DISTRIBUTION CHANNEL

AGENT DISTRIBUTOR

PARENT ORGANIZATION

OTHER ORGANIZATION UNIT

involved in

ORGANIZATION ROLLUP

tofrom

made up of

within

EMPLOYMENT

employer of

employed within

fromto~

~ ~

~

~

~

~

HOUSEHOLD

SUPPLIER ASSOCIATION

COMPETITOR

DEPARTMENT

DIVISION

SUBSIDIARY

EMPLOYEE

CONTRACTOR

FAMILY MEMBER

CONTACT

SHAREHOLDER

PROSPECT

~

BILL TO CUSTOMER

SHIP TO CUSTOMER

END USER CUSTOMER

CUSTOMER

This is where the value of Customer (Party) MDM is realized...Where are relationships like this managed in your organization?


The Master Data Management Solution


Solution – Simplify and Improve

New Sales Force AutomationApplication

Existing CRM Application

Existing ERP Application

Data Warehouse Analytical Systems

Master Data Tables

Master Data Tables


Table (2)


Master Data Tables

Master Data Tables

Master Data Management Hub

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

Content-based routing Data transformation Transaction integrity Workflow guides processM

aste

r Dat

a H

ub

Interface Reduction (continued)

Brokered Master Data Management

Controlled, Managed GrowthComponents Interfaces

10 20

20 40

30 60

Mess

age B

roke

r

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

What does the MDM solution look like?

Master Data HubAdministrator

Master Data DomainAdministrator

ApplyDomainsDefinition

Contribute

MetadataRepository

Master Data Management Hub

NewProducts

Reference

Reference

ApplyBusiness

and SecurityRules

Taxonomy andClassification

ProductAdministration

ProductResearch

PublishTo

Targets

CollectFrom

Sources

GovernanceBody

DefineDomainsDefinition

DefineBusiness

and SecurityRules

EnterpriseSystems

Sales Force Automation

Siebel

BI

Others

New Applications

PeopleSoft

WorkflowNotifications

WorkflowNotifications

ExceptionNotifications

EnterpriseSystemUsers

Interactions


Data Quality is embedded in the process…Data Quality Process

Measure

Analyze

Standardize

Correct

Enhance

Match

Consolidate

Report

Normalize data values andformats according to business

rules and third-partyreferences

Verify, scrub, andappends data based uponalgorithms, business rules

provided from asecondary source

Append additional dataenhancing the

information value

Identify duplicaterecords within multiple

tables, databases

Combine unique dataelements from matched

records into a singlesource

Provide reporting withinthe data quality process

Quantifies the numberand types of defects

Assess the nature andcause of the defects

Data Profiling

Data Cleansing

Data Enhancement

Match and Consolidate

Management Reporting and Oversight

ParseIsolate and identify

data elements in datastructures

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

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


What to do next

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

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


Ensure the organization’s capability to deliverEnhance Organizational Readiness – identify baseline adoption management capability, create executive consensus, highlight missing operational capabilities

Stage the Transformation - consciously choose maturity jumps, understand the expected change in process consistency and complexity, articulate associated operational impacts

Develop Capability-Based Plans – account for internal deployment bandwidth, factor in time to stabilize the foundations, articulate critical dependencies, secure the participation of critical players

Right-Fit Software Solution

Build Organizational

Capability

Drive Organizational Commitment

Market the Compelling Vision – quantify and repeatedly communicate the value to the organization, develop “what’s in it for me” messaging for critical stakeholders

Proactively Manage Stakeholder Buy-In – create opportunities for stakeholder involvement, design usage metrics and incentives to align behavior

Maintain Strong Governance – execute active executive sponsor involvement, define performance outcomes to direct and track success, hold managers accountable for progress

Refine the Operating Model – balance the trade-offs between structure and process, formally assign decision rights, define the new roles

Enhance Change Leadership – develop manager’s communication, expectation and capacity management skills, assign dedicated transition management resources

Develop User Skills – enhance domain specific skills, increase decision management competency

Do not try to build a system whose complexity

exceeds the organization's capability to deliver

Manage Risk and Complexity

Business Intelligence/Analytic

Applications

CollaborativeApplications

Service AdoptionData Acquisition

OperationalApplications

Risk

Platform Foundation

Analytic Application Support

Collaborative Application Support

Operational Application Support

Low High

Analytic MDM Example: Lift customer and party relationship integration out of ETL code base, publish only to analytic systems.

Acceptable latency (no operational requirements for near real time performance).

Should probably use ETL stream processing rather than services for most activity and populate conforming dimensions within the analytic platform.

Mastering generally automated with interaction limited to data steward (s).

Collaborative MDMMastering of customer data more collaborative, will require multiple people to interact with hub, data can be manipulated directly within the hub.

Master data agreement is encapsulated in a workflow that will incorporate both automated and manual tasks, both supported by collaborative capabilities.

Master data being processed is passed from task to task within the workflow and is governed throughout its lifecycle.

Operational MDMIntegration is near real time to applications.

MDM publishes master data changes out to applications as well as receiving changes for processing.

The MDM server acts as an Online-Transaction Processing (OLTP) system that responds to requests from multiple applications and users providing stateless services in a high-performance environment

Will have to deal with collaboration as well as automated mastering (identity and match processing).

Iterative Party

PARTY

ORGANIZATION INDIVIDUAL

BUSINESS TRANSACTING

ACCOUNT

LOCATION LOCATING

CUSTOMER VENDOR

PRODUCT

RETAIL PRODUCT

PACKAGING UNIT

INFLUENCING

PURCHASING

SUPPLYING

INTEREST EXPRESSING

FORMER PURCHASING

EMPLOYEE

PROVIDER

NON-PROVIDER

MARKETING EVENT

DEMOGRAPHIC SEGMENT

SEGMENTING

TARGETING

PROMOTING

ETL Integration

Low HighComplexity

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

and most importantly…

• Not a tool or “technology buy” alone

Master Data Management is not:

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:

Summary

Thank You!


Why this is important (What causes poor quality data?)

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

Questions and reference links

• Beyond Reporting - Delivering Insights with Next-Generation AnalyticsTDWI 2009, Wayne W. Eckerson

• Applied Enterprise ArchitectureJames Parnitzke http://www.pragmaticarchitect.wordpress.com

• Analytic Bridge http://www.analyticbridge.com/

• Emerging Standards

The Predictive Model Markup Language (PMML) http://www.dmg.org/

Web Analytics Standards: http://www.webanalyticsassociation.org/

Example Conformance (Google) http://cutroni.com/blog/2008/09/21/google-analytics-compliance-with-waa-standard-metrics/

http://www.pragmaticarchitect.wordpress.com/

http://www.analyticbridge.com/

http://cutroni.com/blog/2008/09/21/google-analytics-compliance-with-waa-standard-metrics/

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