Data Modeling Best Practices - Business & Technical Approaches

Copyright Global Data Strategy, Ltd. 2019
Data Model Best Practices:
Business and Technical Approaches
Donna Burbank
Global Data Strategy, Ltd.
October 24th, 2019
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Confidential and proprietary. Do not distribute without Couchbase consent. © Couchbase 2019. All rights reserved. 1
Key Locations
• Headquarters: Santa Clara, CA
• Offices: San Francisco, London, Manchester, Bangalore
• Sales Hubs: US (various locations), UK, France,
Germany, Spain, Sweden, Israel, Australia
• Headcount: 350+
What We Do
• The platform that accelerates application innovation
• Combining the best of NoSQL with the power and
familiarity of SQL – all in a single, elegant platform
spanning from any cloud to the edge
• Subscription-based enterprise software company
architected on top of an open source foundation
Created 2011
$150M+ funded
Who We Are
Customers
Best of NoSQL: Cloud-native geo-distributed JSON document-oriented database & key-value store
Couchbase Snapshot

Modern Applications Are Fundamentally Different
NoSQL
Couchbase*
Oracle
1K:1
Consumer
1:1
1M:1 IoT
TransactionsInteractions
Legacy monolithic
infrastructure no
longer an option to
keep up with modern
workloads
Microservices
architecture
supports web,
mobile and IoT
experiences at
scale, with
performance to
match
Transactional Analytical
Confidential and proprietary. Do not distribute without Couchbase consent. © Couchbase 2019. All rights reserved.
Legacy Databases Insufficient
*SQL-compatible

Data Modeling in a post-relational world
● Data Modeling is important for ensuring data correctly maps real world concepts.
● NoSQL, key-value stores and Document databases don’t end modeling. They remove some
concepts and add others.
● What is dead (generally) are attribute tables (Table{id}, AttributeTable{id, single_attribute})
● Person {“name”: “Andrew C. Oliver”, “email”: [ “andrew.oliver couchbase.com”, “acoliver
gmail.com”]}

Data Modeling in a post-relational world
● NoSQL adds new concepts like:
○ Buckets, Collections, Items
and Events
● Some things are different like
Indexes
● There are new dilemmas like
embedded or referenced data
● Some things are getting less
different (RDBMS

Erwin
You can still use Erwin with Couchbase

Global Data Strategy, Ltd. 2019
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 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.
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 - on demand Data Modeling at the Environment Agency of England - Case Study
• April 25 - on demand Data Governance - Combining Data Management with Organizational Change
• May 23 - on demand Master Data Management - Aligning Data, Process, and Governance
• June 27 - on demand Enterprise Architecture vs. Data Architecture
• July 25 - on demand Metadata Management: Technical Architecture & Business Techniques
• August 22 - on demand Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 - on demand Data Catalogues: Architecting for Collaboration & Self-Service
• October 24 Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
3
This Year’s Lineup

Today’s Topic
Data modeling is hotter than ever, according to a number of recent surveys.
Part of the appeal of data models lies in their ability to translate complex data concepts in an
intuitive, visual way to both business and technical stakeholders.
This webinar provides real-world best practices in using data modeling for both business and
technical teams.
4

What is a Data Model?
• A data model is a set of symbols used for communicating concepts and their business rules
• A data model is similar to the architectural diagrams for a house in several ways in that it:
• uses a series of graphical images to convey technical information to a layperson
• consists of several levels from a very-high level to describe scope to the a very detailed description of
technical details.
• shows relationships between key concepts and objects
• is used for communication
5
From Data Modeling for the Business by Hoberman, Burbank, Bradley, Technics Publications, 2009
A model that includes formal data names, comprehensive data
definitions, proper data structures, and precise data integrity rules.
From DAMA Dictionary of Data Management, 2011

Real-World Use Cases for Data Models
6
Examples from practice
Environmental Data
Sampling
How do we align our
scientific terminology?
These are all from real-world examples we’ve implemented. Data Models are everywhere.
Early Childhood
Development
How to we create better
outcomes for children?
eCommerce &
Digital Transformation
What is data is key to our
digital transformation?
University Student
Support
Understanding the Student
Journey with Data
Water Utility
Data Modernization
How do we reflect our
business rules into our new,
digital environment?
Construction
Contracting Efficiencies
How can a data model
highlight inefficiencies in
our business?
Agile Software
Development
What data is involved in
this user story?
Membership Org
Customer Centricity
How do we define our
customer/member?

A little data modeling up-front
… prevents headaches down the road
• It’s often tempting to skip data
modeling documentation because it’s
“faster”
• But…long-term, it’s ultimately longer as
errors and inconsistencies need to be
fixed as a result.
“If you don’t have time to do it right, do
you have time to do it again?”

Levels of Data Models
8
Conceptual
Logical
Physical
Purpose
Communication & Definition of
Business Concepts & Rules
Clarification & Detail
of Business Rules &
Data Structures
Technical
Implementation on
a Physical Database
Audience
Business Stakeholders
Data Architects
Data Architects
Business Analysts
DBAs
Developers
Business Concepts
Data Entities
Physical Tables
Business Stakeholders
Data Architects
Enterprise
Subject Areas
Organization & Scoping of main
business domain areas

Where to Start: “Top Down” or “Bottom Up?”
• Models can be built
• Top-Down: Identifying business concepts,
definitions & relationships
• Bottom-Up: Creating an inventory of existing data
assets.
• Using a Hybrid Approach – Middle Out
• An Iterative, Hybrid Approach is often the most
practical.
Conceptual
Logical
Physical
Iterative

Business Data Model (Conceptual)
• Communication & definition of core data concepts & their definitions
• A business data model
provides core definitions
of key data objects.
• It also shows key
relationships between
data objects.
• Even a simple diagram as
the one on the right can
tell a powerful “story”
…. And uncover key
business issues and
opportunities.
• How do we define a
“customer” vs. a “client”
• Is our employee
relationship different for
each?

Innovation & Collaboration
• An Enterprise Data Model provides a “catalogue” of an organization’s data asset.
• Staff are able to see all of the data available across the organization – spurring innovation & collaboration.
11
Sharing the catalogue of enterprise data assets
I didn’t realize that the Insurance
Dept was tracking Weather
Events. I could use that to link
Weather to Product Sales for
Trend Analysis!! Cool!

Use the Language of Your Audience
• When communicating with business stakeholders, it’s important to display data models in a way
that’s intuitive to them
• PowerPoint-style Conceptual Data Models
• Use Business Terminology
• Avoid Excess Detail
12
Gaining Buy-In

Tell a Story
• Humans have evolved over time as storytellers
• We can’t even sleep without dreaming in stories.
• No one cares about your data model…
• … but they do care about the RESULTS of your data model
• … relate the model to a real world impact or scenario..i.e.
“story”
13
What impact does the data model have on the business?
Humans are Storytellers.

Real-World Quotations from Business Users
14
The Good

UK Environment Agency
• The UK Environment agency worked with Global Data Strategy to develop Data Models & Data Standards
in order to support Open Data publication of key environmental measures.
15
Governing Open Data Publication through Data Models
• Land boundaries
• Air & Water Quality
• Fish & Wildlife populations
• Etc.
• Common Data Models & Standards helped create a
common lingua franca across the organization:
• Saving time & money
• Supporting Regulation
• Enhancing public reputation
• Improving data quality & consistency
• Increasing collaboration between teams
“Establishing a standard is a really important step in bringing our
information together so we can be better joined up, better integrated and
work together more efficiently. In short, if you’ve got even the
slightest interest in how we plan and deliver outcomes on the ground, you
should be taking an interest in this!”
- National River Basin Operations Manager, Environment Agency

Logical Data Model
• A logical data model
• Defines detailed business rules
• Includes attributes, data types,
nullability, etc.
• Defines data structures, but not
physical tables (e.g. hierarchies)
…. And uncovers key
business issues and opportunities.
• Can a customer have more than
one address?
• Is Fax number still a required field?
• What is a fax number???....
Place
Appear on
Contain
Belong to
Customer
customer identifier
first name
middle initial
last name
description
Product
product identifier
product name
description
Order
customer identifier (FK)
product identifier (FK)
order date
Product Part Combination
product identifier (FK)
part identifier (FK)
Raw Material
material_identifier
Finished Good
finished good identifier
Subassembly
subassembly identifier
Part
part identifier
part name
description

Avoid “Death by Data Modeling”
17
• “We’re just going to sit in this room for a few days
until we scope out the entire enterprise data model
plastered across these three walls.
• Just about 1000 entities or so…
• First off, what is the data type for account code? …”

Break the Large Modeling Efforts into Manageable Chunks
18
Instead of creating large models all at once Break them into smaller “chunks” / sprints

Data Modeling Creates an “Active Inventory” of Data Assets
• Know what data you have: Create a visual inventory of database systems
• Know what your data means: Communicate key business requirements between business and IT stakeholders
• Support data consistency: Build consistent database structures & support data governance initiatives
Sybase
MySQL
Oracle
Data Models
Teradata
Sybase
SQL
Server
DB2
Teradata
SQL
Server DB2
MySQLSQL
Azure
SQL
Azure
Oracle
Forward Engineering Reverse Engineering

Physical Data Model
20
• A physical data model
• Defines data structures to store
data on a physical platform (e.g.
RDMBS, Document data store,
etc.)
• Optimizes for performance,
query, etc.
… And ensures that data is stored
in a fit for purpose manner. e.g.
How can I:
• Store data to reduce redundancy
and increase data quality?
• Optimize data storage to “slice
and dice” for self-service
analytics?
• Optimize data storage for speed
of query?

Different Physical Models for Different Use Cases
21
Relational – Normal Form
• Reduce redundancy for
operational data
• Increase data quality
• Ensure consistency (ACID
transactions)
Dimensional– Star Schema
• Ease of reporting for summarized
and historical data
• Ability to easily “slice and dice” for
self-service reporting
• Performance and flexibility
NoSQL
No modeling technique is inherently “better” than another. Data use cases & purpose drives what “good” looks like.
…Rant over…
• Speed of retrieval, low
latency
• High data volumes
• Flexibility for change
…And More!
• There are numerous
ways to model and store
data.
• Hierarchical/XML
• Time series
• COBOL Copybook!
• S3 “buckets”
• Data Vault
• Etc…

Summary
• Data models provide a visual, intuitive way to design data for both business and technical
needs, providing a common “lingua franca” between business and technical stakeholders.
• Business data models should focus on business terminology and rules, and target business
impact and ROI.
• Technical data models provide a graphical way to design new technical data platforms and
create a visual inventory of existing data platforms.
• Data modeling continues to be a popular way to manage data structures and business rules,
even with the diverse use cases and technologies available on the market today.

DATAVERSITY Data Architecture Strategies
• January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 - on demand Data Modeling at the Environment Agency of England - Case Study
• April 25 - on demand Data Governance - Combining Data Management with Organizational Change
• May 23 - on demand Master Data Management - Aligning Data, Process, and Governance
• June 27 - on demand Enterprise Architecture vs. Data Architecture
• July 25 - on demand Metadata Management: Technical Architecture & Business Techniques
• August 22 - on demand Data Quality Best Practices (w/ guest Nigel Turner)
• September 26 - on demand Data Catalogues: Architecting for Collaboration & Self-Service
• Oct 24 – soon on demand Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
23
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.
24
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

Questions?
25
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Data Modeling Best Practices - Business & Technical Approaches

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