Lessons in Data Modeling: Data Modeling & MDM

Data Modeling &
Master Data Management (MDM)
Donna Burbank
Global Data Strategy Ltd.
Lessons in Data Modeling DATAVERSITY Series
September 28th, 2017

Global Data Strategy, Ltd. 2017
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. She was on the
review committee for the Object
Management Group’s Information
Management Metamodel (IMM) and the
Business Process Modeling Notation
(BPMN). Donna is also an analyst at the
Boulder BI Train Trust (BBBT) where she
provides advices and gains insight on the
latest BI and Analytics software in the
market.
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.
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Follow on Twitter @donnaburbank
Today’s hashtag: #LessonsDM

DATAVERSITY Lessons in Data Modeling Series
• January - on demand How Data Modeling Fits Into an Overall Enterprise Architecture
• February - on demand Data Modeling and Business Intelligence
• March - on demand Conceptual Data Modeling – How to Get the Attention of Business Users
• April - on demand The Evolving Role of the Data Architect – What does it mean for your Career?
• May - on demand Data Modeling & Metadata Management
• June - on demand Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling
• July - on demand Data Modeling & Metadata for Graph Databases
• August - on demand Data Modeling & Data Integration
• September 28 Data Modeling & Master Data Management (MDM)
• October 26 Agile & Data Modeling – How Can They Work Together?
• December 5 Data Modeling, Data Quality & Data Governance
3
This Year’s Line Up

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
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Definition

When?
A Data Model Describes the Entities of the Business
The “Who, What, Where, When, Why” of the Organization – the Nouns
Entity: A classification of the types of objects found in the
real world --persons, places, things, concepts and events – of
interest to the enterprise. 1
1 DAMA Dictionary of Data ManagementWho?
How?
Where?
What?
Product
Salesperson
Invoice
Why?
Order
Period
Location

A Data Model Is a Visual Representation of Core Entities
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A data model is a graphical view of the core entities important to the organization.
Humans tend to think in Pictures.
But… All Entities are not
Master Data Entities

A Data Model Is a Visual Representation of Core Entities
7From Data Modeling for the Business by Hoberman, Burbank, Bradley, Technics Publications, 2009
A data model is a graphical view of the core entities important to the organization.
Humans tend to think in Pictures.
Early Master Data

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
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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
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Master Data:
Customer
Master Data: Product
Master Data: Location
Reference Data:
Country Codes
Reference Data:
State Codes
Transaction
Data

Master Data – the Opportunity
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A 360 Degree View through Data
Stefan Krauss
Age = 31
Occupation = Ski Instructor Purchased €500 in
outdoor gear in 2016
100% of purchases online
Top Finisher in Engadin Ski
Marathon 2010-2015
Member of Loyalty
Program since 2010
Prefers Text Message
Address = Pontresina, Switzerland

Global Data Strategy, Ltd. 2017 11
Stefan Krauss
Age = 62
Master Data – the Opportunity (& Need)
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 2016
Address = Zurich, Switzerland

Master Data Management (MDM)
• There are many architectural approaches to MDM. Two are the following:
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Centralized Virtualized/Registry
MDM
Virtualization Layer
• Core data stored in a
common schema in a
centralized “hub”.
• Used as a common
reference for
operational systems,
DW, etc.
• Data remains in
source systems.
• Referenced through
a common
virtualization layer.
BOTH require a Data Model

MDM Data Models
• In an MDM Data Model, the core
attributes for master data entities
can be identified.
• This is typically the superset of
attributes used by core systems &
stakeholders in the organization.
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Core, Shared
Attributes
Source System A
Source System B
Source System C

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
The MDM data model is a selected
superset of the source system models.
MDM can feed the dimensional model for
the data warehouse (e.g. customer,
location, etc.)
Applications can reference
the “Golden Record” for
lookup.

Business Rules & Matching
• Once master attributes have been assigned, and their populating source systems
identified, a next step in MDM is to clarify how records are identified as equivalent, in a
process known as matching.
• Matching rules provide the criteria used to match records from disparate systems as
candidates for a golden record.
• Matching strategies are based on identifying attributes, and multiple match strategies can
be defined, for example:
• Match Strategy 1: Match on Date of Birth + Social Security Number
• Match Strategy 2: Match on Social Security Number + Last Name
• Match strategies can be executed in sequential order. For example, if no match is found
using Strategy 1, a match will be searched for using Strategy 2, and so on through the list of
match strategies.
15

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.
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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

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.
17

Matching Approval – Key Stewardship Role
• A key responsibility of Data Stewards for MDM is the manual review and approval of potential matches
which cannot be auto-approved and which require human review.
• In these cases, the match score is below the defined threshold, and requires a data steward to review the
proposed matches and MDM golden record. Each steward would review the items for their given area
only.
18
Match Group ID Name Match Status Match Score Record Source
000007 John R Smith Proposed .7843 System A
000007 Jack Smith Proposed .6532 System B
000007 John Smith Proposed .6894 System C
000007 John R Smith Proposed etc. System D

Survivorship – Attribute Groups
• Once matches have been approved, a golden record can be assigned from a match group through a
process of Survivorship. (Note: These rules are distinct from Matching Rules)
• In order to create a mastered record from the various source systems, a series of attribute groups are
defined, with specific survivorship rules for each of those attributes.
• For example, address sets could be defined for the following scenarios
• Name fields (e.g. containing First Name, Last Name, Maiden Name, etc.). Rules could be defined that these
attributes are populated from System A.
• Demographic fields (e.g. containing Race, Ethnicity, Gender, etc.). Rules could be defined that these fields are
populated from System B.
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System A
System B
MDM
“Golden Record”

Harmonization
• Harmonization: The process of harmonization pushes the mastered record back to
source systems.
• While this helps keep the MDM and source systems in synch, and works to improve overall data
quality …
• … it should be handled carefully, with close coordination with the owners and stewards of the source
systems.
20

Governance & Business Process for MDM
• Successful MDM is critical on collaboration between the owners and stewards of
various systems, and between business and IT stakeholders.
• In fact, the top two reasons for failure of MDM systems cited by the Gartner
analyst group1 are :
• Failure of IT to Align With Business Process Improvements and Document Business Value
• Delaying or Mismanaging Information Governance Implementation
• 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.
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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

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.?
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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.
23
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

Case Study: Linking Data with Process for MDM
• 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 Models to identify the flow of information
• CRUD Matrixes to understand usage, stewardship & ownership
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Managing the Data that Runs the Business
Product Creation &
Testing
Menu Display &
Marketing
Supply Chain Point of Sale &
Restaurant Operations

Summary
• Master Data focuses on the core entities of the business (e.g. Customer, Product,
Supplier, etc.)
• Data models are a critical part of any MDM initiative – defining & managing these
core entities
• Master Data Management can provide significant business opportunity, as long as
governance, process, data quality, survivorship rules, etc. are managed correctly.
• Data Governance is critical to any MDM initiative
• Business Process Models and CRUD matrices are important tools in aligning MDM
to business success

DATAVERSITY Lessons in Data Modeling Series
• January - on demand How Data Modeling Fits Into an Overall Enterprise Architecture
• February - on demand Data Modeling and Business Intelligence
• March - on demand Conceptual Data Modeling – How to Get the Attention of Business Users
• April - on demand The Evolving Role of the Data Architect – What does it mean for your Career?
• May - on demand Data Modeling & Metadata Management
• June - on demand Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling
• July - on demand Data Modeling & Metadata for Graph Databases
• August - on demand Data Modeling & Data Integration
• September 28 Data Modeling & Master Data Management (MDM)
• October 26 Agile & Data Modeling – How Can They Work Together?
• December 5 Data Modeling, Data Quality & Data Governance
26
This Year’s Line Up

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

Contact Info
• Email: donna.burbank@globaldatastrategy.com
• Twitter: @donnaburbank
@GlobalDataStrat
• Website: www.globaldatastrategy.com
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Questions?
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Thoughts? Ideas?

Lessons in Data Modeling: Data Modeling & MDM

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