Master Data Management For the Business … Data Management For the Business Professional Discussion...

Master Data Management

For the Business Professional

DiscussionOctober 22, 2009

2Confidential and Proprietary

Agenda

• What is Master Data Management?

• Why is it important?

• What happens to produce bad quality data?

• Quick 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


Essentials What is Master Data Management?


What is Master Data Management?

• Business capability to deliver

– accurate,

– complete,

– timely, and

– consistent information

• …a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization providing processes for

– collecting, persisting, and distributing,

– aggregating,

– quality-assuring, matching, and consolidating data

throughout an organization to ensure consistency and control in the ongoing maintenance and use of this information. (source: wikipedia definition)


Data Management – Types of Assets

• Master

– 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



Reference data

• Party

–Customer

– Supplier

– Employee

• Product

• Contract Agreement

• Pricing

• Location

• Asset

• Hierarchies



Customer Product Asset Employee

CreateCustomer visit, such as to

Web site or facility; account

created

Product purchased or

manufactured; Supply Chain

Management optimization

Unit acquired by

opening a PO;

approval process

necessary

HR hires, numerous

forms, orientation,

benefits selection, asset

allocations, office

assignments

ReadContextualized views based

on credentials of viewer,

sales reports

Periodic inventory

catalogues, sales reports

Periodic reporting

purposes, figuring

depreciation,

verification

Office access, reviews,

insurance-claims,

immigration, HR needs

UpdateAddress, discounts, phone

number, preferences, credit

accounts

Packaging changes, raw

materials changes

Transfers,

maintenance,

accident reports

Immigration status,

marriage status, level

increase, raises, transfers,

contact information

RetireDeath, bankruptcy,

liquidation, do-not-call.

Canceled, replaced, no

longer available

Obsolete, sold,

destroyed, stolen,

scrapped

Termination, death

SearchCRM system, call-center

system, contact-

management system

ERP system, orders-

processing system

GL tracking, asset DB

management

HR Line of Business

system



Master Data Life Cycle Management

Extensibility — maintainable

Hierarchy management

Ability to manage complexity

Industry knowledge

Matching, linking, indexing

Identification and recognition

Master Data Governance

Data-stewardship facilities

Batch and real-time

Access and assembly

Work Flow and Collaboration

Propagation to the central system

Publishing to peripheral systems

Database Management

Application server

Integration brokers

Compute platform environment

Query

Transaction processing

Batch processing

Information Delivery

Data encapsulation

Service Data Objects (SDO)

Granular and packaged

foundation for Service Orientated

Architecture (SOA) based

application use

Service Component Architecture

Data Modeling and Analysis Information Quality Integration and Synchronization

Performance and Scalability


Business ServicesTechnology Base

Business Capability


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

11

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-related

errors

Dupl icates Address errors Customer type Miss ing

Relationships

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-related

errors

Dupl icates Address errors Customer type Miss ing

Relationships

Best Case (INT'L)

Worst Case (INT'L)

Average (INT'L)

Are you kidding? (remember 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 ContactDeceased – Individual

Dissolved, M/A – Org, Group

Deceased – Individual

Dissolved, 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…

Marketing Example

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


How does bad quality data happen?


A closer look…

Source: Aberdeen 2008

0%

10%

20%

30%

40%

50%

60%

70%

80%

At the data source

layer our data is not

clean or managed

properly

At the integration

layer our data

s ources are not

integrated properly

At the end us er

access and

cons umption layers

us ers introduce

errors

At the analytics

appl ication layer;

appl ication

development

introduces errors

At the securi ty layer;

acces s in not

control led properly


Essentials – How bad data is created

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

§ Means complex, undocumented, and difficult-to-maintain integrations solutions exist primarily because…

each connection or integration point was created locally

rather than globally optimized.

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

24

Example – How bad can it get?

Sample Product Information Flow

25


Pricing Example – MDM in Action

27

Pricing the sales transaction


The Master Data Management Solution


MDM Solution – Simplify and Improve

MDM 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

ü Content-based routingü Data transformationü Transaction integrityü Workflow guides process

Ma

ste

r D

ata

Hu

b

Interface Reduction (continued)

Brokered Master Data Management

Gentle, Manageable Growth

Components Interfaces

10 20

20 40

30 60

Messag

e Bro

ker

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?


Data Quality is embedded in the process…

Data Quality Process

Measure

Analyze

Standardize

Correct

Enhance

Match

Consolidate

Report

Normalize data values and

formats according to business

rules and third-party

references

Verify, scrub, and

appends data based upon

algorithms, business rules

provided from a

secondary source

Append additional data

enhancing the

information value

Identify duplicate

records within multiple

tables, databases

Combine unique data

elements from matched

records into a single

source

Provide reporting within

the data quality process

Quantifies the number

and types of defects

Assess the nature and

cause of the defects

Data Profiling

Data Cleansing

Data Enhancement

Match and Consolidate

Management Reporting and Oversight

ParseIsolate and identify

data elements in data

structures

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 deliver

Do not try to build a system whose complexity

exceeds the organization's capabilities

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

• 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:

to produce an accurate and consistent set of information within a unified process improvement initiative.


For the Business Professional

Thank You…


Mr. Parnitzke is a hands-on technology executive, trusted partner, advisor, software publisher, and widely recognized database management and enterprise architecture thought leader. Over his career he has served in executive, technical, publisher (commercial software), and practice management roles across a wide range of industries. Now a highly sought after technology management advisor and hands-on practitioner his customers include many of the Fortune 500 as well as emerging businesses where he is known for taking complex challenges and solving for them across all levels of the customer’s organization delivering distinctive value and lasting relationships.

Contact:[email protected]

Blogs:Applied Enterprise Architecture (pragmaticarchitect.wordpress.com)

The Corner Office (cornerofficeguy.wordpress.com)

Data management professional (jparnitzke.wordpress.com)

Essential Analytics (essentialanalytics.wordpress.com)

The program office (theprogramoffice.wordpress.com)

Master Data Management For the Business Professional

Date post:	12-Mar-2018
Category:	Documents
Upload:	hahuong
View:	217 times
Download:	4 times

Master Data Management For the Business … Data Management For the Business Professional Discussion...

Documents