Master Data Management - Practical Strategies for Integrating into Your Data Architecture

Master Data Management: Practical Strategies
for Integrating into Your Data Architecture
Donna Burbank, Managing Director
Global Data Strategy, Ltd.
September 27th, 2018
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2018
Donna Burbank
Donna is a recognised industry expert in
information management with over 20 years
of experience in data strategy, information
management, data modeling, metadata
management, and enterprise architecture.
Her background is multi-faceted across
consulting, product development, product
management, brand strategy, marketing,
and business leadership.
She is currently the Managing Director at
Global Data Strategy, Ltd., an international
information management consulting
company that specializes in the alignment of
business drivers with data-centric
technology. In past roles, she has served in
key brand strategy and product
management roles at CA Technologies and
Embarcadero Technologies for several of the
leading data management products in the
market.
As an active contributor to the data
management community, she is a long time
DAMA International member, Past President
and Advisor to the DAMA Rocky Mountain
chapter, and was recently awarded the
Excellence in Data Management Award from
DAMA International in 2016.
Donna is also an analyst at the Boulder BI
Train Trust (BBBT) where she provides advice
and gains insight on the latest BI and
Analytics software in the market. She was on
several review committees for the Object
Management Group’s for key information
management and process modeling
notations.
She has worked with dozens of Fortune 500
companies worldwide in the Americas,
Europe, Asia, and Africa and speaks regularly
at industry conferences. She has co-
authored two books: Data Modeling for the
Business and Data Modeling Made Simple
with ERwin Data Modeler and is a regular
contributor to industry publications. She can
be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, USA.
2
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January - on demand Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February - on demand Building an Enterprise Data Strategy – Where to Start?
• March - on demand Modern Metadata Strategies
• April - on demand The Rise of the Graph Database
• May - on demand Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape
• June - on demand Artificial Intelligence: Real-World Applications for Your Organization
• July - on demand Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset
• August - on demand Data Lake Architecture – Modern Strategies & Approaches
• Sept Master Data Management: Practical Strategies for Integrating into Your Data Architecture
• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit
• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks
3
This Year’s Line Up for 2018

Today’s Topic
4
Implementing a Successful MDM Initiative
• Master Data Management (MDM) provides organizations with an accurate and comprehensive
view of their business-critical data such as Customers, Products, Vendors, and more.
• While mastering these key data areas can be a complex task, the value of doing so can be
tremendous – from real-time operational integration to data warehousing & analytic reporting.
• This webinar provides practical strategies for gaining value from your MDM initiative, while at the
same time assuring a solid architectural and governance foundation that will ensure long-term,
enterprise-wide success.

Global Data Strategy, Ltd. 2018 5
A Successful Data Strategy links Business Goals with Technology Solutions
“Top-Down” alignment with
business priorities
“Bottom-Up” management &
inventory of data sources
Managing the people, process,
policies & culture around data
Coordinating & integrating
disparate data sources
Leveraging & managing data for
strategic advantage
Copyright 2018 Global Data Strategy, Ltd
MDM is Part of a Wider Data Strategy
www.globaldatastrategy.com

What is Master Data?
• Master Data is the consistent and uniform set of identifiers and extended attributes that
describes the core entities of the enterprise including customers, prospects, citizens,
suppliers, sites, hierarchies and chart of accounts (sic).
• Master data management (MDM) is a technology-enabled discipline in which business
and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and
accountability of the enterprise's official shared master data assets.
- Source Gartner
6
Definition

Popularity of Master Data Management
• As more and more organizations struggle with obtaining a single,
consistent view of core data such as Product, Customer, Vendor, Location,
etc. Master Data Management (MDM) is seeing a rise in implementations.
• In a recent DATAVERSITY survey, over 65% of respondents are
pursuing a MDM strategy.
7
1 Trends in Data Architecture, 2017, DATAVERSITY, by Donna
Burbank and Charles Roe

Many Organizations are Just Getting Started
8
1 Trends in Data Architecture, 2017, DATAVERSITY, by Donna
Burbank and Charles Roe
Many organizations are in
the early stages of their
Master Data Management
(MDM) implementations.

Survey: Where are You in your MDM Journey?
• We are actively implementing MDM, but are in the early stages (low level of maturity)
• We are actively implementing MDM, and are at a high level of maturity.
• We are in the process of beginning MDM, but have not yet fully implemented.
• We are not considering implementing MDM at this time.
9
Live Poll

Understanding Your Customer
10
A 360 Degree View through Data
Stefan Krauss
Age = 31
Occupation = Ski Instructor Purchased €500 in
outdoor gear in 2015
100% of purchases online
Top Finisher in Engadin Ski
Marathon 2010-2015
Member of Loyalty
Program since 2010
Prefers Text Message
Address = Pontresina, Switzerland

11
Stefan Krauss
Age = 62
Understanding Your Customer
A 360 Degree View through Data
Occupation = Banker
Member of Loyalty
Program since 1990
Football Fan
Prefers Physical Mail
100% of spending in store
75% of spending is while
on holiday
Purchased €3.500 in
outdoor gear in 2015
Address = Zurich, Switzerland

Transaction Data vs. Master Data
Customer Date Product Code Price Quantity Location
Stefan Kraus 1/2/2017 Scarpa Telemark Ski Boot SC1279 €250 1 St. Moritz, CH
Donna Burbank 1/5/2017 Scarpa Telemark Ski Boot SCU1289 $150 1 Boulder, CO
Stefan Kraus 1/2/2017 North Face Down Jacket NF8392 €450 1 Zurich, CH
Stefan Kraus 1/2/2017 Garmin Sports Watch GM29384 €200 2 Zurich, CH
Wendy Hu 3/4/2017 Prana Yoga Pant PN82734 $51 5 New York, NY
Joe Smith 4/1/2017 Garmin Sports Watch GM29384 $150 1 Albany, NY
12
Consider the following retail transaction data
Transaction Data
• Describes an action (verb): E.g. “buy”
• May include measurements about the action: (Who, When,
What, How Many, Where, How Much, etc.)
• E.g. Stefan Kraus, 1/2/2017/, Scarpa Telemark Ski Boot, St.
Moritz, CH, €250
Master Data
• Describes the key entities (nouns), e.g. Customer, Product,
Location
• Provides attributes & context for these nouns
• e.g. Wendy Hu, age 25, female, resident of New York, NY,
Customer since 2005, preferred customer card, etc.

Customer Date Product Code Price Quantity Location
Stefan Kraus 1/2/2017 Scarpa Telemark Ski Boot SC1279 €250 1 St. Moritz, CH
Donna Burbank 1/5/2017 Scarpa Telemark Ski Boot SCU1289 $150 1 Boulder, CO
Stefan Kraus 1/2/2017 North Face Down Jacket NF8392 €450 1 Zurich, CH
Stefan Kraus 1/2/2017 Garmin Sports Watch GM29384 €200 2 Zurich, CH
Wendy Hu 3/4/2017 Prana Yoga Pant PN82734 $51 5 New York, NY
Joe Smith 4/1/2017 Garmin Sports Watch GM29384 $150 1 Albany, NY
Transaction Data vs. Master Data
13
Master Data:
Customer
Master Data: Product
Master Data: Location
Reference Data:
Country Codes
Reference Data:
State Codes
Transaction
Data

ETL
Master Data Overview
14
CRM In-Store
Sales
MarketingFinance Online
Sales
Supply
Chain
Each system has its own unique
functionality and associated data model.
MDM
“Golden Record”
Data
Warehouse BI & Reporting
Data Model
Lookup
End User Applications
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe

ETL
15
CRM In-Store
Sales
Sales
Supply
Chain
MDM
“Golden Record”
Data
Data Model
Lookup
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe
The MDM data model is a selected
super/sub set of the source system models.

ETL
16
CRM In-Store
Sales
Sales
Supply
Chain
MDM
“Golden Record”
Data
Data Model
Lookup
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe
MDM can feed the dimensional model for
the data warehouse (e.g. customer,
location, etc.)

ETL
17
CRM In-Store
Sales
Sales
Supply
Chain
MDM
“Golden Record”
Data
Data Model
Lookup
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe
MDM can feed the dimensional model for
the data warehouse (e.g. customer,
location, etc.)
Applications can reference
the “Golden Record” for
lookup.

Governance & Business Process for MDM
• While the implementation of the hub and population strategies is complex, more
complex is understanding the business processes and governance processes
around the populating and publishing systems.
• In fact, the top two reasons for failure of MDM systems cited by the Gartner
analyst group1 are :
18
1 Top Four Reasons Your MDM Program Will Fail, and How to Avoid Them, Gartner, 2016, ID:
G00223675, by Bill O’Kane. Note: The remaining two reasons are: Failure to Manage Initial Master
Data Quality & Defining Transactional (Fact) Data as Master Data
Failure of IT to Align With
Business Process Improvements
and Document Business Value
Delaying or Mismanaging
Information Governance
Implementation

Master Data
Management
Data
Architecture
Data
Governance &
Stewardship
Business
Process
Alignment
• Accountability & stewardship
• Business rule validation
• Conflict resolution
• Business Prioritization
• Business process models
• Data mapping to process
• CRUD and usage matrices
• Optimizing business process
for data improvement
• System Architecture & data flow
• Data models & hierarchies
• Match/merge and survivorship rules
• Data integration & design
Successful MDM Combines Data, Process, and Accountability

The Importance of Business Process
• Process models are a helpful tool for describing core business processes (e.g. BPMN).
• “Swimlanes” outline organizational considerations
• Data can be mapped to key business processes to understand creation & usage of information.
• Understanding business process is critical to Data Governance
• Who is using data?
• How is it used in business processes?
• Are there redundancies, conflicts, etc.?
20
Identifying key data dependencies in core business processes

CRUD Matrix – Understanding Data Usage
Product
Development
Supply Chain
Accounting
Marketing Finance
Product Assembly Instructions C R
Product Components C R
Product Price C U R
Product Name C U,D
Etc.
21
Create, Read, Update, Delete
• CRUD Matrices shows where data is Created, Read, Updated or Deleted across the
various areas of the organization
• This can be a helpful tool in data governance & data quality to determine route cause
analysis.
Data entities
or attributes
Users, Departments, and/or Systems

Optimizing Restaurant Revenue through Menu Data
• An international restaurant chain realized through its digital strategy that:
• While menus are the core product that drives their business…
• They had little control or visibility over their menu data
• Menu data was scattered across multiple systems in the organization from supply chain to kitchen prep to marketing,
restaurant operations, etc.
• Menu data was consolidated & managed in a central hub:
• Master Data Management created a “single view of menu” for business efficiency & quality control
• Data Governance created the workflow & policies around managing menu data
• Process Models & Data Mappings were critical
• Business Process diagrams to identify the flow of information
• CRUD Matrixes to understand usage, stewardship & ownership
Managing the Data that Runs the Business
Product Creation &
Testing
Menu Display &
Marketing
Supply Chain Point of Sale &
Restaurant Operations

The Intersection of MDM and Data Governance
Successful MDM require governed alignment between business & technical roles
Enterprise-wide Business Prioritization
Enterprise Conceptual
Data Model
Map to Customer & Logistics
Journey
Prioritize Subject Areas / MDM
Domains (e.g. Product)
Prioritize Geo Rollout
Business & Technical Aligment & Implementation
Logical Model
o Business Rules
o Match-Merge Criteria
o Survivorship Rules
o Harmonization Strategy Data Steward Tasks &
Workflow
Business Process
Workflow
Data Domain Steward Business Process
Steward
Regional/Geo
Steward
Physical Model
Enterprise Data
Architect
Business
Analyst
MDM
Architect
Business-level
Technical-level
o Data Profiling
o Data Quality Remediation
o Augmentation (e.g. address)
o Data Quality Dashboards
o Data Integration
o CRUD Matrix
o Publish & Subscribe
Data Quality Data Integration MDM Platform Admin
o DB Admin
o MDM Platform Admin
Business Data Owners Enterprise Data
Architect
Business
Analyst
Roles
Business Technical
System
Steward
MDM
Architect
Data Quality
Specialist
Data Integration
Specialist

Optimizing Customer Experience through Data
• A major Retail Vendor wanted to become a data-driven company
• Enhancing the customer experience by mapping the Customer Journey to the Data Lifecycle
• Using IoT product data to improve product design & customer service
• Optimizing product supply chain & delivery
• Developing a Tactical Data Strategy determined that
• Master Data Management was needed to manage customer contact data throughout the Customer Journey
• Data Governance was required to manage data across organizational siloes: product development, marketing, sales, etc.
• Data Architecture was needed to understand the data ecosystem: data flow diagrams, data models, process models, etc.
24
Using Data to Build Customer Loyalty & Increase Sales
Customer SupportCustomer Discovery &
Purchase
Product Delivery &
Tracking
Product Usage Tracking Product Development

Summary
• Interest in Master Data Management (MDM) is on the rise as more organizations look to gain a
common, consistent source for their core data assets (Customer, Product, Supplier, Employee, etc.)
• Successful MDM is part of a wider data strategy and requires integration with:
• Data Architecture
• Business Process Alignment
• Data Governance & Stewardship
• Getting this combination right can have a positive impact on the success of the business.

DATAVERSITY Data Architecture Strategies
• January - on demand Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February - on demand Building an Enterprise Data Strategy – Where to Start?
• March - on demand Modern Metadata Strategies
• April - on demand The Rise of the Graph Database: Practical Use Cases & Approaches to Benefit your Business
• May - on demand Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape
• June –on demand Artificial Intelligence: Real-World Applications for Your Organization
• July – on demand Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset
• August – soon on demand Data Lake Architecture – Modern Strategies & Approaches
• Sept – soon on demand Master Data Management: Practical Strategies for Integrating into Your Data Architecture
• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit
• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks
26
This Year’s Line Up for 2018 – Join Us Next Month

White Paper: Trends in Data Architecture
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Free Download
• Download from
• Under ‘Resources/Whitepapers’

About Global Data Strategy, Ltd
• Global Data Strategy is an international information management consulting company that specializes
in the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
• Business-Driven: We put the needs of your business first, before we look at any technology solution.
• Clear & Relevant: We provide clear explanations using real-world examples.
• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s
size, corporate culture, and geography.
• High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of
technical expertise in the industry.
28
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

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