DAS Slides: Master Data Management – Aligning Data, Process, and Governance

Copyright Global Data Strategy, Ltd. 2020
Master Data Management
Aligning Data, Process, and Governance
Donna Burbank
Global Data Strategy, Ltd.
April 23rd , 2020
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

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Global Data Strategy, Ltd. 2020
Donna Burbank
2
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 awarded the Excellence in
Data Management Award from DAMA
International.
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.
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
3
This Year’s Lineup

What We’ll Cover Today
• Master Data Management (MDM) provides organizations with an accurate and
comprehensive view of 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.
4

Global Data Strategy, Ltd. 2020 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 2020 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

What is Master Data?
7
Real-world examples
The “dead” living organismThe $1M cheese slice The $2M baby bottle
Which Dr. Smith is credentialled
for heart surgery?
Which Michael Jones is the
high-net worth customer?
How do we define Regions, Markets,
Locations, Catchments, Sites, etc.?

What is Master Data? What is Reference Data?
8
How do we define Regions, Markets,
Locations, Catchments, Sites, etc.?
One person’s Master Data is
another person’s Reference Data… vs.
Address Line 1
Address Line 2
City
State
AL
AK
AR
AZ
CA
CO
..etc.
Master Data Reference Data

… but Don’t make this overly Pedantic …
9

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 2019
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
MDM
“Golden Record”
Data
Warehouse BI & Reporting
Data Model
Lookup
End User Applications
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe
Data Stewardship
Validation

ETL
15
CRM In-Store
Sales
Sales
Supply
Chain
Each system has its own unique
functionality and associated data model.
MDM
“Golden Record”
Data
Data Model
Lookup
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe
Data Stewardship
Validation
First Name
Family Name
Address Line 1
Address Line 2
City
State
Phone
Email
Spouse
First Name
Postal Code
Customer ID
First Name
Family Name
Account #
Credit Balance
First Name
Middle Name
Family Name
Email
Twitter ID
Gender
First Name
Family Name
Address Line 1
Address Line 2
City
State
Credit Card #
Phone
First Name
Family Name
Address Line 1
Address Line 2
City
State
Country
Phone

ETL
16
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/subset of the source system models.
Data Stewardship
Validation
Customer ID
First Name
Family Name
Address Line 1
Address Line 2
City
State
Country
Phone
Email
Country Codes
State Codes
First Name
Family Name
Address Line 1
Address Line 2
City
State
Phone
Email
Spouse
First Name
Postal Code
Customer ID
First Name
Family Name
Account #
Credit Balance
First Name
Middle Name
Family Name
Email
Twitter ID
Gender
First Name
Family Name
Address Line 1
Address Line 2
City
State
Credit Card #
Phone
First Name
Family Name
Address Line 1
Address Line 2
City
State
Country
Phone

ETL
17
CRM In-Store
Sales
Sales
Supply
Chain
MDM
“Golden Record”
Data
Data Model
Lookup
Reference
Data Sets
Publish & Subscribe
Data Stewardship
Validation
First Name = John
Family Name = Smith
Address Line 1 = 101 Main ST
Address Line 2
City = Anywhere
State = Texas
ZIP = 10101
Phone = 555 927 1212
Email = johns@gmail.com
Spouse = Mary
First Name = Jack
Postal Code = 10101
Customer ID = 123
First Name = John
Family Name = Smith
Account #
Credit Balance
First Name = J.
Middle Name
Family Name = Smith
Email = goaway@me.net
Twitter ID = @johns
Gender = Male
First Name = Johnn
Family Name
Address Line 1 = 101 Main Street
Address Line 2 = Apt 2
City = Plano
State = TX
Credit Card #
Phone = +1 555 927 1212
First Name = John
Family Name = Smith
Address Line 1 = 101 Main St
Address Line 2
City = Anywhere
State = TX
Country = USA
Phone = x1212
Customer ID = 123
First Name = John
Family Name = Smith
Address Line 1 = 101 Main ST
Address Line 2 = Apt 2
City = Anywhere
State = TX
Postal Code = 10101
Country = USA
Phone = +1 555 927 1212
Email = johns@gmail.com
Matching Rules help create a
“Golden Record”
Data Quality
& Matching

Data Model / Database Keys for Matching Rules
• Candidate attribute combinations for matching are often aligned with the primary
and alternate keys from the logical data model.
18
Ideally, if all systems use the same unique identifier, matching is easier.
But this isn’t often realistic in “real world” systems.
• First, match on Date of Birth + SSN
• Then, match on SSN + Last Name
• Etc.
Matching on Primary Key
Matching on Alternate Keys
Customer
Customer ID

Fuzzy Matching
• Fuzzy matching logic can also be used, which is particularly helpful in matching string fields such as names
and addresses, where human error or different entry standards between systems can cause slight
variations in similar values, e.g.
• “101 Main St” vs. “101 Main Street”
• “John Smith” vs. “J Smith”
• In addition synonyms can be created to assist with matching, for example
• “St”, “St.”, “Street”, etc. for addresses
• “Tim”, “Timothy” for names and nicknames.
• When using fuzzing matching, data quality thresholds can be defined for auto approval.
• Match scores are created for each fuzzy match, for example .9 would indicate a strong match and .2 a weak one.
• Using these scores as a guide, thresholds can be defined for which matches can be auto-approved, which can be
auto-rejected, and which need human review from a data steward.
19

ETL
20
CRM In-Store
Sales
Sales
Supply
Chain
MDM
“Golden Record”
Data
Data Model
Lookup
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe
“Human in the Loop” – Data
Stewards can validate match
candidates.
Data Stewardship
Validation

ETL
21
CRM In-Store
Sales
Sales
Supply
Chain
MDM
“Golden Record”
Data
Data Model
Lookup
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe
Applications can reference
the “Golden Record” for
lookup.
Data Stewardship
Validation

ETL
22
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.)
Data Stewardship
Validation

MDM is not Reporting or Analytics
-- It can be a Source
23
MDM
“Golden Record”
Data
e.g. Customer by Region
Reference
Data Sets
Graph Database
Social Network
Analysis
Data Lake
Social Media
Sentiment Analysis

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 :
24
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.?
26
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.
27
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

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 Alignment & 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 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

Summary
30
• 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 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
31
Join us next month

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.
32
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

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33
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DAS Slides: Master Data Management – Aligning Data, Process, and Governance

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DAS Slides: Master Data Management – Aligning Data, Process, and Governance