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 TurnerGlobal 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 [email protected] is based in Boulder, Colorado, USA.

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Follow on Twitter @donnaburbank

mailto:[email protected]


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: [email protected]

Nigel is based in Cardiff, Wales in the UK.

3Follow on Twitter @NigelTurner8

mailto:[email protected]


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

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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

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What we’ll cover today

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Data Strategy & Data ModelingSome Basic Definitions


Building an Enterprise Data Strategy

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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.

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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

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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

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Business Strategy Data Strategy

Sets Requirements for

Informs & Guides

Business Strategy


How a Data Model Fits Within a Data Strategy

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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

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Top-Down Business RequirementsDefining the needs of the business


Levels of Data Modeling

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Conceptual

Logical

Physical

Purpose

Communication & Definition of Business Terms & Rules

Clarification & Detail of Business Rules &

Data Structures

TechnicalImplementation on a Physical Database

Audience

Business Stakeholders

Data ArchitectureBusiness Analysts

DBAsDevelopers

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.

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The Value of WhiteboardingIt’s often helpful to “whiteboard” data models with sticky notes

Policy

Account

Employee


Identify High-Priority Data Elements

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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

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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

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Bottom-Up Technical LandscapeDefining 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

MySQLOracle

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.

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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)

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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

EmployeeAn 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 cardor maintenance contract.


Data Lineage

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Customer DatabaseOracle

Customer DatabaseSQL Server

Sales DatabaseDB2

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.

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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

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Data Modeling with other Data Management DisciplinesSupporting 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 ModelDimensional 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

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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

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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.

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Data-Driven Business Transformation

Business StrategyAligned With

Data Strategy


Contact Info• Email: [email protected]

[email protected]

• 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

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DAMA Rocky Mountain Chapter

Website: http://www.dama-rockymountainchapter.org/

Twitter: @DAMA_RMC

DAMA UK Chapter

Website: http://www.damauk.org/

Twitter: @DAMAUK

http://www.globaldatastrategy.com/

https://www.linkedin.com/company/global-data-strategy-ltd

https://www.linkedin.com/in/donnaburbank

https://uk.linkedin.com/in/nigelturnerdataman

http://www.dama-rockymountainchapter.org/

http://www.damauk.org/


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

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Online Training Courses

New Metadata Management Course

Visit: http://training.dataversity.net/lms/

http://training.dataversity.net/lms/index.php?r=coursepath/details&id_path=3

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

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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?

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Thoughts? Ideas?