Lessons in Data Modeling: Why a Data Model is an Important Part of Your Data Strategy

Why a Data Model is an Important Part
of Your Data Strategy
Donna Burbank & Nigel Turner
Global Data Strategy Ltd.
Lessons in Data Modeling DATAVERSITY Series
July 28th, 2016

Global Data Strategy, Ltd. 2016
Donna Burbank
Donna is a recognized industry expert in
information management with over 20
years of experience in data
management, 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 and is
the President of the DAMA Rocky
Mountain chapter. She was also on the
review committee for the Object
Management Group’s Information
Management Metamodel (IMM) and a
member of the OMG’s Finalization
Taskforce for the Business Process
Modeling Notation (BPMN).
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 CA ERwin Data Modeler r8.
She can be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado,
USA.
2
Follow on Twitter @donnaburbank

Nigel Turner
Nigel has worked in Information Management
(IM) and related areas for over 20 years. This
experience has embraced Data Governance,
Information Strategy, Data Quality, Data
Governance, Master Data Management, &
Business Intelligence.
He spent much of his career in British
Telecommunications Group (BT) where he led
a series of enterprise wide IM initiatives
which brought huge benefits to BT. He also
created and led large IM & CRM consultancy
& delivery practices which served BT Global
Services’ customers.
After leaving BT in 2010 Nigel became VP of
Information Management Strategy at Harte
Hanks Trillium Software, a leading global
provider of Data Quality & Data Governance
tools and consultancy. Here he engaged with
over 150 customer organisations from all
parts of the globe, undertaking extensive
engagements with HSBC, Progress Software,
British Gas, HBOS, EDF Energy, Severn Trent
Water, British Airways, Telefonica O2 and
others.
Nigel is a well known thought leader in
Information Management and has run
tutorials and presented at many international
conferences. He also authored the Data
Quality Strategy module of the Institute of
Data Marketing’s Data Management Award.
Nigel is very active in professional Data
Management organisations and is an elected
Data Management Association (DAMA) UK
Committee member. He was the joint winner
of DAMA International’s 2015 Community
Award for the work he initiated and led in
setting up a mentoring scheme in the UK
where experienced DAMA professionals
coach and support newer data management
professionals.
He can be reached at:
nigel.turner@globaldatastrategy.com
Nigel is based in Cardiff, Wales in the UK.
3
Follow on Twitter @NigelTurner8

Lessons in Data Modeling Series
• July 28th Why a Data Model is an Important Part of your Data Strategy
• August 25th Data Modeling for Big Data
• September 22nd UML for Data Modeling – When Does it Make Sense?
• October 27th Data Modeling & Metadata Management
• December 6th Data Modeling for XML and JSON
4
This Year’s Line Up

Agenda
• Data Strategy & Data Modeling
• Top-Down Business Requirements
• Bottom-Up Technical Landscape
• Data Modeling with other Data Management Disciplines
• Summary & Questions
5
What we’ll cover today

6
Data Strategy & Data
Modeling
Some Basic Definitions

Building an Enterprise Data Strategy
7
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

How can we Transform the Business through Data?
Optimization: Becoming a Data-Driven Company
• Making the Business More Efficient
• Better Marketing Campaigns
• Higher quality customer data, 360 view of customer, competitive info, etc.
• Better Products
• Data-Driven product development, Customer usage monitoring, etc.
• Better Customer Support
• Linking customer data with support logs, network outages, etc.
8
Transformative: Becoming a Data Company
• Changing the Business Model via Data – data becomes the product
• Monetization of Information: examples across multiple industries including:
• Telco: location information, usage & search data, etc.
• Retail: Click-stream data, purchasing patterns
• Social Media: social & family connections, purchasing trends &
recommendations, etc.
• Energy: Sensor data, consumer usage patterns, smart metering, etc.

Basic Definitions
9
Business & Data Strategies
A BUSINESS STRATEGY is a medium to long term business plan which
details the aims & objectives of a business and how it means to
achieve them.
A DATA STRATEGY is a medium to long term plan for the improvement,
management & exploitation of data across a business, and how it is
to be achieved.

Business & Data Strategy – the Interdependency
10
Business Strategy Data Strategy
Sets Requirements for
Informs & Guides
Business Strategy

How a Data Model Fits Within a Data Strategy
11
Data Governance
Focus
Business
Strategy
Business Data
Model
Business Goals &
key data needs
Data Definitions
‘As is’ Data
Baseline
‘To be’ Data
Intention
Data Strategy
Investment (time & resources)Priority

12
Top-Down Business
Requirements
Defining the needs of the
business

Levels of Data Modeling
13
Conceptual
Logical
Physical
Purpose
Communication & Definition of
Business Terms & Rules
Clarification & Detail
of Business Rules &
Data Structures
Technical
Implementation on
a Physical Database
Audience
Business Stakeholders
Data Architecture
Business Analysts
DBAs
Developers
Business Concepts
Data Entities
Physical Tables

Conceptual Data Model
• Communication & Definition Of Business Rules

Logical Data Model
• More Detailed, Normalized, Potential Pre-cursor To Physical Design

“Creative” Data Model for Business Audience
• Using a “graphical data model” is an intuitive way to show data entities and their relationships to
a business audience.
• It’s a helpful way to show how data fits into the “big picture” of the organization & helps it run
effectively.
16

The Value of Whiteboarding
It’s often helpful to “whiteboard” data models with sticky notes
Policy
Account
Employee

Identify High-Priority Data Elements
18
Align with Business Drivers
Launch of New Product – Marketing Campaign
requires better customer information
Customer
Product
Region Vendor
Partner
Identify Key
Business Drivers
Filter Data Elements
Aligned with Business
Drivers
Focus Governance &
Improvement Efforts
on Key Data
Targeted Projects to
Show Short-Term
Results

Data Definitions - why bother?
The Tower of Babel
“If as one people
speaking the same
language they have
begun to do this, then
nothing they plan to do
will be impossible for
them. Come, let us go
down and confuse their
language so they will not
understand each other.”
Genesis 11:1-9

The Importance of Definitions
• Definitions are as important as the data elements themselves.
• Many data-related business issues are caused by unclear or ill-defined terms
20
What is in a name?
What do you mean by
“customer”?
We’re calculating “total sales”
differently in each region!
Sales is using a different
“monthly calendar” than
Finance.
How are we defining a
“household”?
What’s an “equity
derivative”?
What’s a “PEG ratio”?
“API” as in “Application Programming Interface?”
or “American Petroleum Institute”? Or a bee?
What’s the difference between an
“ingredient” and a “raw material”?

Data Definitions – The Benefits
• Helps scope the data strategy to focus on the data objects / attributes
that really matter
• Supports the development and enforcement of data standards &
business rules
• Enables selective data quality scrutiny & monitoring
• Prioritizes data improvement activities
• Underpins business and IT impact analysis & change control
• Informs and improves design gateways and approvals
• Better legal & regulatory control, especially if combined with Data
Governance
• If published and communicated, helps to raise general awareness of the
importance of key data
21

22
Bottom-Up Technical
Landscape
Defining the data architecture

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

Metadata Adds Context & Definition
• Metadata stored in data models provides valuable business & technical context.
24

Technical & Business Metadata
• Technical Metadata describes the structure, format, and rules for storing data
• Business Metadata describes the business definitions, rules, and context for data.
• Data represents actual instances (e.g. John Smith)
25
CREATE TABLE EMPLOYEE (
employee_id INTEGER NOT NULL,
department_id INTEGER NOT NULL,
employee_fname VARCHAR(50) NULL,
employee_lname VARCHAR(50) NULL,
employee_ssn CHAR(9) NULL);
CREATE TABLE CUSTOMER (
customer_id INTEGER NOT NULL,
customer_name VARCHAR(50) NULL,
customer_address VARCHAR(150) NULL,
customer_city VARCHAR(50) NULL,
customer_state CHAR(2) NULL,
customer_zip CHAR(9) NULL);
Technical Metadata
John Smith
Business Metadata
Data
Term Definition
Employee
An employee is an individual who currently
works for the organization or who has been
recently employed within the past 6 months.
Customer
A customer is a person or organization who
has purchased from the organization within
the past 2 years and has an active loyalty card
or maintenance contract.

Data Lineage
26
Customer Database
Oracle
Customer Database
SQL Server
Sales Database
DB2
Staging Area Data Warehouse
“Sales Amount”
Transformation Rules
(ETL)
• Data Lineage shows the source to target mapping, or provenance for information.
• For example, to understand how “Sales Amount” in a data warehouse is calculated, it is necessary to
understand where the data came from and how it was manipulated along the way.
• Many data modeling tools track this lineage through integration with ETL tools, or with internal mapping
functionality.

Data Model Design Layer Relationships
• Data model design layer mappings show the relationship between business terms and their
physical implementations on a database platform.
27
Showing Semantic Mapping
Conceptual
Logical
Physical
Business Concepts
Data Entities
Physical Tables
Client
Customer
DB2TeradataOracle
CUST CUSTOMER CTABLE_16
In a Conceptual data model, there may
be a concept called “Client” which is the
term businesspeople use to describe the
people they sell to and work with.
The Logical model might
use the term “Customer”
for that same concept.
Which may be implemented in a number
of physical tables with varying naming
conventions.
Conceptual
Logical
Physical
Business Concepts
Data Entities
Physical Tables

28
Data Modeling with other
Data Management Disciplines
Supporting Enterprise Data
Initiatives

Data Modeling for Data Warehousing & Business Intelligence
• What is the definition of customer?
• Where is the data stored?
• How is it structured?
• Who uses or owns the data?
Data Warehouse BI Report:
Customers by Region
• What are the definitions of key business terms?
• What do I want to report on?
• How do I optimize the database for these reports?
Data Modeling helps answer:
For Data Warehousing For BI Reporting
Data Modeling helps answer:
• Data Modeling is the “Intelligence behind Business Intelligence”
• Understand source and target data systems
• Define business rules
• Optimize data structures to align queries with reports
Show me all
customers by region
Source Systems
Relational Model
Dimensional Model

Data Modeling for Enterprise Architecture
• Enterprise Architecture provides a high-level view of the people, processes, applications, and data
of an organization
• Putting data in business context
• How does data link to the rest of my organization?
• If I change data, what business processes are affected?

Data Modeling for Cloud and SaaS
Cloud Database
DB2
Sybase
SQL Server
Teradata
Oracle
• A Data Model is your “roadmap” for:
 What data to move to the Cloud, and what to keep on-premises
 Defining data structures (physical model) and business requirements (logical model) for Cloud databases
• Off-Premises doesn’t mean Out of your Control
Data Model

Data Modeling for Application Development
• The majority of today’s applications are data-driven
• Data Modeling is a key part of the application development lifecycle
• Reuse of common data objects helps promote
• Increased efficiency – don’t “reinvent the wheel”
• Better collaboration
• Increased quality and consistency

Data Modeling for Master Data Management
• Master Data Management strives to create a “single version of the truth” for key business data:
customer, product, etc.
• Using a central data model helps define:
• Common business definitions
• Common data structures
• Data lineage between defined “version of the truth” and real-world implementations

Data Modeling for Data Governance
• Data, like money, is a corporate asset, and needs to be managed accordingly.
• Like an auditing department for finance, data governance provides the guidelines, accountability
and regulations around data management.
• Data Models can help define:
• What are the standards, domains, and rules for data?
• Who is accountable for data (e.g. Data Steward)?
• Who is using data?
• What is the lineage and traceability of data?
• What is the proper definition of key business information?
• When was the data last updated?

Data Modeling Supports an Enterprise Data Strategy
35
From Top-Down to Bottom-Up
Conceptual Data Model
Physical Data Model
• Data Lineage
• Impact Analysis
• Metadata Management
• Data Standards
• Etc.
Data Modeling Ecosystem

Summary
• Data Strategy & Data Modeling
• Top-Down Business Requirements
• Bottom-Up Technical Landscape
• Data Modeling with other Data Management Disciplines
• Summary & Questions
36
The Importance of Data Modeling to Data Strategy

About Global Data Strategy, Ltd.
• Global Data Strategy is an international information management consulting company specializing 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 technological solution.
• Clear & Relevant: We provide clear explanations using real-world examples, not technical jargon.
• 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, and we attract high-
quality professionals with years of technical expertise in the industry.
37
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy

Contact Info
• Email: donna.burbank@globaldatastrategy.com
nigel.turner@globaldatastategy.com
• Twitter: @GlobalDataStrat
@donnaburbank
@NigelTurner8
• Website: www.globaldatastrategy.com
• Company Linkedin: https://www.linkedin.com/company/global-data-strategy-ltd
• Personal Linkedin: https://www.linkedin.com/in/donnaburbank
https://uk.linkedin.com/in/nigelturnerdataman
38
DAMA Rocky Mountain Chapter
Website: http://www.dama-rockymountainchapter.org/
Twitter: @DAMA_RMC
DAMA UK Chapter
Website: http://www.damauk.org/
Twitter: @DAMAUK

DATAVERSITY Training Center
• Learn the basics of Metadata Management and practical tips on how to apply metadata
management in the real world. This online course hosted by DATAVERSITY provides a series of six
courses including:
• What is Metadata
• The Business Value of Metadata
• Sources of Metadata
• Metamodels and Metadata Standards
• Metadata Architecture, Integration, and Storage
• Metadata Strategy and Implementation
• Purchase all six courses for $399 or individually at $79 each.
Use discount code “GDS” to receive 20% off!
• Register here
• Other courses available on Data Governance & Data Quality
39
Online Training Courses
New Metadata Management Course
Visit: http://training.dataversity.net/lms/

Lessons in Data Modeling Series
• July 28th Why a Data Model is an Important Part of your Data Strategy
• August 25th Data Modeling for Big Data
• September 22nd UML for Data Modeling – When Does it Make Sense?
• October 27th Data Modeling & Metadata Management
• December 6th Data Modeling for XML and JSON
40
Join us next month

Questions?
• Questions on Data Strategy & Data Modeling?
• Questions for next month’s topic on Big Data & Data Modeling?
• Suggestions for next year’s lineup?
41
Thoughts? Ideas?

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Lessons in Data Modeling: Why a Data Model is an Important Part of Your Data Strategy