A conceptual data model (CDM) uses simple graphical images to describe core concepts and principles of an organization at a high level. A CDM facilitates communication between businesspeople and IT and integration between systems. It needs to capture enough rules and definitions to create database systems while remaining intuitive. Conceptual data models apply to both transactional and dimensional/analytics modeling. While different notations can be used, the most important thing is that a CDM effectively conveys an organization's key concepts.
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The Critical Role of a Conceptual Data Model
1. P A G E 1
Data Modelling
for the Business
The Critical Role of a Conceptual Data Model
C H R I S T O P H E R B R A D L E Y ( C D M P F E L L O W )
chris.bradley@dmadvisors.co.uk
3. P / 3
Christopher Bradley
I N F O R M AT I O N M A N A G E M E N T S T R AT E G I S T
Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
+44 7973 184475 (mobile)
Chris.Bradley@DMAdvisors.co.uk
+44 1225 923000 (office)
4. P / 4
Christopher Bradley
Chris has 36 years of Information Management experience &
is a leading Independent Information Management strategy
advisor.
In the Information Management field, Chris works with
prominent organizations including HSBC, Celgene, GSK,
Pfizer, Icon, Quintiles, Total, Barclays, ANZ, GSK, Shell, BP,
Statoil, Riyad Bank & Aramco. He addresses challenges faced
by large organisations in the areas of Data Governance,
Master Data Management, Information Management
Strategy, Data Quality, Metadata Management and Business
Intelligence.
He is President of DAMA UK, DAMA- I DMBOK 2 author.
In April 2016 he became the inaugural CDMP Fellow, and
received the DAMA lifetime professional
achievement award.
He is an author & examiner for CDMP, a Fellow of the
Chartered Institute of Management Consulting (now IC) a
member of the MPO, and SME Director of the DM Board.
A recognised thought-leader in Information Management
Chris is the author of numerous papers, books, including
sections of DMBoK 2.0, a columnist, a frequent contributor
to industry publications and member of several IM
standards authorities.
He leads an experts channel on the influential
BeyeNETWORK, is a sought after speaker at major
international conferences, and is the co-author of âData
Modelling For The Business â A Handbook for aligning
the business with IT using high-level data modelsâ. He
also blogs frequently on Information Management (and
motorsport).
6. Recent PresentationsDAMA Sydney: August 2016 Sydney; âData Quality, The Impact of Dirty Data?â
DAMA Melbourne: August 2016 Melbourne; âData Quality, Are You Feeling Lucky?â
DAMA Canberra: August 2016 Canberra; âData Quality, Are You Feeling Lucky?â
Australian Computer Society (ACS): August 2016 Canberra; âData Quality Matters For Machines
Tooâ
MDM DG Europe: May 2016 London; âData Governance by Stealthâ
Webinar: May 2016; âData Modelling is not JUST for DBMS designâ
Enterprise Data World: April 2016 San Diego; âDAMA CDMP Workshopâ
DAMA Webinar: April 2016; âData Integration & Interoperabilityâ Disciplines of the DAMA DMBoKâ
DAMA Webinar: March 2016; âDocument & Content Managementâ Disciplines of the DAMA
DMBoKâ
DAMA Webinar: February 2016; âData Operations Managementâ Disciplines of the DAMA
DMBoKâ
Oil & Gas Data Management Conference: February 2016, London; âDeveloping An Information
Strategy To Align With In-Flight Business Programsâ
DAMA Webinar: January 2016; âData Governanceâ Disciplines of the DAMA DMBoKâ
DAMA Webinar: December 2015; âInformation Lifecycle Managementâ Disciplines of the DAMA
DMBoKâ
DAMA Webinar: November 2015; âMetaData Managementâ Disciplines of the DAMA DMBoKâ
Enterprise Data & BI Europe (IRM): November 2015, London; âIs the Data Asset really different?â
& âCDMP Examination Preparationâ & âData Management Room 101â
DAMA Webinar: October 2015; âData Risk & Securityâ Disciplines of the DAMA DMBoKâ
DAMA Webinar: September 2015; âData Warehousing & Business Intelligenceâ Disciplines of the DAMA
DMBoKâ
DAMA Webinar: August 2015; âData Quality Managementâ Disciplines of the DAMA DMBoKâ
BCS & DAMA Seminar: June 2015; âIs the Data Asset really different?â
DAMA Webinar: June 2015; âData Modellingâ Disciplines of the DAMA DMBoKâ
PRISME Pharmaceutical Congress: May 2015, Basel, CH; âBuilding & exploiting a Pharmaceutical
Industry consensus data modelâ
MDM DG Europe (IRM): May 2015, London; âCDMP Examination Preparationâ & âData Governance By
Stealth?, Can you âsellâ Data Governance if the stakeholders donât get it?â
DAMA Webinar: April 2015; âMaster & Reference Data Managementâ Disciplines of the DMBoKâ
Enterprise Data World: April 2015, Washington DC USA; âData Modelling For The Businessâ and
âEvaluating Information Management Toolsâ
DAMA Webinar: February 2015; âAn Introduction to the Information Disciplines of the DMBoKâ
Dataversity Webinar: February 2015; âHow to successfully introduce Master & Reference data managementâ
Petroleum Information Management Summit 2015: February 2015, Berlin DE,
âHow to succeed with MDM and Data Governanceâ
Enterprise Data & Business Intelligence 2014: (IRM), November 2014, London, UK âData Modelling 101â
Enterprise Data World: (DataVersity), May 2014, Austin, Texas, âMDM Architectures & How to identify the right Subject
Area & tooling for your MDM strategyâ
E&P Information Management Dubai: (DMBoard),17-19 March 2014, Dubai, UAE âMaster Data Management
Fundamentals, Architectures & Identify the starting Data Subject Areasâ
DAMA Australia: (DAMA-A),18-21 November 2013, Melbourne, Australia âDAMA DMBoK 2.0â,
âInformation Management Fundamentalsâ 1 day workshopâ
Data Management & Information Quality Europe: (IRM Conferences), 4-6 November 2013, London, UK;
âData Modelling Fundamentalsâ Âœ day workshop: âMyths, Fairy Tales & The Single Viewâ Seminar;
âImaginative Innovation - A Look to the Futureâ DAMA Panel Discussion
IPL / Embarcadero series: June 2013, London, UK, âImplementing Effective Data Governanceâ
Riyadh Information Exchange: May 2013, Riyadh, Saudi Arabia; âBig Data â Whatâs the big fuss?â
Enterprise Data World: (Wilshire Conferences), May 2013, San Diego, USA, âData and Process Blueprinting â
A practical approach for rapidly optimising Information Assetsâ
Data Governance & MDM Europe: (IRM Conferences), April 2013, London, âSelecting the Optimum
Business approach for MDM successâŠ. Case study with Statoilâ
E&P Information Management: (SMI Conference), February 2013, London,
âCase Study, Using Data Virtualisation for Real Time BI & Analyticsâ
E&P Data Governance: (DMBoard / DG Events), January 2013, Marrakech, Morocco, âEstablishing a
successful Data Governance programâ
Big Data 2: (Whitehall), December 2012, London, âThe Pillars of successful knowledge managementâ
Financial Information Management Association (FIMA): (WBR), November 2012, London; âData Strategy
as a Business Enablerâ
Data Modeling Zone: (Technics), November 2012, Baltimore USA; âData Modelling for the businessâ
Data Management & Information Quality Europe: (IRM), November 2012, London; âAll you need to know
to prepare for DAMA CDMP professional certificationâ
ECIM Exploration & Production: September 2012, Haugesund, Norway:
âEnhancing communication through the use of industry standard models; case study in E&P using WITSMLâ
Preparing the Business for MDM success: Threadneedles Executive breakfast briefing series, July 2012,
London
Big Data â Whatâs the big fuss?: (Whitehall), Big Data & Analytics, June 2012, London,
Enterprise Data World International: (DAMA / Wilshire), May 2012, Atlanta GA,
âA Model Driven Data Governance Framework For MDM - Statoil Case Studyâ
âWhen Two Worlds Collide â Data and Process Architecture Synergiesâ (rated best workshop in
conference); âPetrochemical Information Management utilising PPDM in an Enterprise Information
Architectureâ
Data Governance & MDM Europe: (DAMA / IRM), April 2012, London,
âA Model Driven Data Governance Framework For MDM - Statoil Case Studyâ
AAPG Exploration & Production Data Management: April 2012, Dead Sea Jordan; âA Process For
Introducing Data Governance into Large Enterprisesâ
PWC & Iron Mountain Corporate Information Management: March 2012, Madrid; âInformation
Management & Regulatory Complianceâ
DAMA Scandinavia: March 2012, Stockholm,
âReducing Complexity in Information Managementâ (rated best presentation in conference)
Ovum IT Governance & Planning: March 2012, London; âData Governance â An Essential Part of IT
Governanceâ
American Express Global Technology Conference: November 2011, UK,
âAll An Enterprise Architect Needs To Know About Information Managementâ
FIMA Europe (Financial Information Management):, November 2011, London; âConfronting The
Complexities Of Financial Regulation With A Customer Centric Approach; Applying a Master Data
Management And Data Governance Process In Clydesdale Bank â
Data Management & Information Quality Europe: (DAMA / IRM), November 2011, London, âAssessing &
Improving Information Management Effectiveness â Cambridge University Press Case Studyâ; âToo Good
To Be True? â The Truth About Open Source BIâ
ECIM Exploration & Production: September 12th 14th 2011, Haugesund, Norway: âThe Role Of Data
Virtualisation In Your EIM Strategyâ
Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL; âHow Do You Want Yours
Served? â The Role Of Data Virtualisation And Open Source BIâ
Data Governance & MDM Europe: (DAMA / IRM), March 2011, London; âClinical Information Data
Governanceâ
7. P / 7
Recent Publications
Book: âData Modelling For The Business â A Handbook for aligning the business with IT using high-level data modelsâ; Technics
Publishing; ISBN 978-0-9771400-7-7; http://www.amazon.com/Data-Modeling-Business-Handbook-High-Level
Book: âDAMA Data Management Body Of Knowledge 2.0â ; Technics Publishing; ISBN TBD
Article: Back to the future for Data management? September 2015
Article: Is the âData Assetâ really different? July 2015
Article: A visit to the vet & a BA flight reminded me about Data Governance; June 2015
White Paper: âInformation is at the heart of ALL Architecture disciplinesâ,; March 2014
White Paper: âAre you ready for Big Data ?â, November 2013
Article: The Bookbinder, the Librarian & a Data Governance story ; July 2013
Article: Data Governance is about Hearts and Minds, not Technology January 2013
White Paper: âThe fundamentals of Information Managementâ, January 2013
White Paper: âKnowledge Management â From justification to deliveryâ, December 2012
Article: âChief INFORMATION Officer? Not reallyâ Article, November 2012
White Paper: âRunning a successful Knowledge Management Practiceâ November 2012
White Paper: âBig Data Projects are not one man showsâ June 2012
Article: âIPL & Statoilâs innovative approach to Master Data Management in Statoilâ, Oil IT Journal, May 2012
White Paper: âData Modelling is NOT just for DBMSâsâ April 2012
Article: âData Governance in the Financial Services Sectorâ FSTech Magazine, April 2012
Article: âData Governance, an essential component of IT Governance" March 2012
Article: âLeveraging a Model Driven approach to Master Data Management in Statoilâ, Oil IT Journal, February 2012
Article: âHow Data Virtualization Helps Data Integration Strategiesâ BeyeNETWORK (December 2011)
Article: âApproaches & Selection Criteria For organizations approaching data integration programmesâ TechTarget (November 2011)
Article: Big Data â Same Problems? BeyeNETWORK and TechTarget. (July 2011)
Article â10 easy steps to evaluate Data Modelling toolsâ Information Management, (March 2010)
Article âHow Do You Want Your Data Served?â Conspectus Magazine (February 2010)
Article âHow do you want yours served (data that is)â (BeyeNETWORK January 2010)
Article âSeven deadly sins of data modellingâ (BeyeNETWORK October 2009)
Article âData Modelling is NOT just for DBMSâsâ Part 1 BeyeNETWORK July 2009 and Part 2 BeyeNETWORK August 2009
Web Channel: BeyeNETWORK âChris Bradley Expert Channelâ Information Asset Management
http://www.b-eye-network.co.uk/channels/1554/
Article: âPreventing a Data Disasterâ February 2009, Database Marketing Magazine
8. Data Modelling
Foundation
(1 day)
Introductory Intermediate Advanced / Deep Dive
Advanced Data Modelling
(3 days)
Integrated Business Process, Data Requirements and
Discovery
(5 days)
DAMA-I CDMP
Exam Cram &
Certification
(3 days)
Level
Information
Management For
The Business
(œ and 1 day)
Multiple Levels of Training to meet your needs
Data Modelling Fundamentals
(3 days)
The âclient Wayâ Information Management Mentoring
Information Management Fundamentals
(4 or 5 days)
Data Quality Management
Implementation & Practice (1 and 2
day)
Data Warehouse & Business Intelligence
Implementation & Practice (1 and 2
day)
Reference & Master Data Management
Implementation & Practice (1 and 2
day)
Data Governance Implementation &
Practice (1 and 2 day)
Data Integration Implementation &
Practice (1 and 2 day)
Introduction to
Information
Management (3
days)
MetaData Management
Practitioner (1 day)
9. P A G E 9
AGENDA
âș What is a Conceptual Data Model
âș Why is a Conceptual Data Model Important?
âș Data Models and Information Management
âș Aligning with Business Goals & Motivations
âș Data Modeling Basics
10. P A G E 1 0
Who Are You? Survey
HOW WOULD YOU DESCRIBE YOUR ROLE?
Businessperson or Business Analyst
Business Intelligence Analyst or Developer
Data Architect, Data Modeler, or Data Analyst
DBA or Technical IT
A combination of the above
Other
11. P A G E 1 1
I am new to data modelling
I know a little about data modelling
I have a lot of experience with data modelling
Poll: How Familiar are you with Data
Modelling?
13. P A G E 1 3
What is a
Conceptual Data Model?
âș A Conceptual Data Model (CDM) uses simple
graphical images to describe core concepts
and principles of an organization and what
they mean
âș The main audience of a CDM is businesspeople
âș A CDM is used to facilitate communication
âș A CDM is used to facilitate integration between
systems, processes, and organizations
âș It needs to be high-level enough to be intuitive,
but still capture the rules and definitions
needed to create database systems
PRECISION
FLEXIBILITY
14. P A G E 1 4
THIS? THIS?
or
We all use Models to Gain Understanding
18. P A G E 1 8
Customer
âA Picture is Worth a
Thousand Wordsâ
EXAMPLES OF CONCEPTUAL DATA MODELS
19. P A G E 1 9
âA Picture is Worth a
Thousand Wordsâ
Product
Customer
Location
Order
Raw
Material
Ingredient
Region
EXAMPLES OF CONCEPTUAL DATA MODELS
20. P A G E 2 0
âA Picture is Worth a
Thousand Wordsâ
EXAMPLES OF CONCEPTUAL DATA MODELS
21. P A G E 2 1
âA Picture is Worth a
Thousand Wordsâ
EXAMPLES OF CONCEPTUAL DATA MODELS
22. P A G E 2 2
Conceptual Data
Models apply to
Dimensional Data
Modelling as well.
âA Picture is Worth a
Thousand Wordsâ
EXAMPLES OF CONCEPTUAL DATA MODELS
23. P A G E 2 3
âA Picture is Worth a
Thousand Wordsâ
EXAMPLES OF CONCEPTUAL DATA MODELS
24. P A G E 2 4
âA Picture is Worth a Thousand
Wordsâ
EXAMPLES OF CONCEPTUAL DATA MODELS
25. P A G E 2 5
Is Notation Important?
âș Many Notations can be used to express a high-
level data model
âș The choice of notation depends on purpose
and audience
âș For data-related initiatives, such as Master Data
Management (MDM) and Data Warehousing
(DW):
» ER modeling using IE (Information
Engineering) is our choice of notation
» It is important that your high-level model uses
a tool that can generate DDL, or can
import/export with a tool that can
» A repository-based solution helps with reuse
and standards for enterprise-wide initiatives
26. P A G E 2 6
Multiple Models â Multiple Purposes â
Multiple Audiences
Conceptual
Logical
Physical
Audience
Businessperson
3NF
Data Architect
DBA , Developer
Purpose
Communication &
Definition of Business
Terms & Rules
Clarification & Detail of
Business Rules & Data
Structures
Technical
Implementation on a
Physical Database
Business Concepts
Data Entities
Physical Tables
Repository &
Model-Driven
27. P A G E 2 7
Building Models Top Down vs. Bottom Up
âș Models can be built
» Top-Down
» Bottom-Up
» Using a Hybrid Approach â
Middle Out
Conceptual
Logical
Physical
Repository &
Model-Driven
28. P A G E 2 8
Step 1: Speak with the business representatives. Document the key
business requirements and agree on high-level scope.
BRD
Step 2: Create detailed business requirement specification document
with subscriber data requirement, business process and business rules
BRD
Step 3: Understand and document the business major attributes and
definitions from business subject matter experts. Create logical data
model.
Logical data model
Tables & foreign key
constraints
Step 4: Verify the logical data model with the stakeholders and create
physical data model
Step 5: Implement using the created physical model
Top-down Approach
29. P A G E 2 9
Step 5: Try to understand the business meanings of probable attributes
and entities that may be candidates for logical data model
Step 4: Document the meanings of columns, and tables from IT
subject matter experts
Step 3: Find out foreign key relationships between tables, from IT
subject matter experts & verify findings
A near logical data model
(accuracy unknown)
Tables & foreign key
constraints
Step 2: Profile data by browsing and analyzing the data from tables.
Scan through the ETL to find out hidden relationships & constraints.
Step 1: Reverse engineer the database schema that is already
implemented.
Bottom-up Approach
30. P A G E 3 0
Create Different Models for Different
Audiences
Business -> Conceptual
31. P A G E 3 1
Create Different Models for Different
Audiences
Technical -> Physical
32. P A G E 3 2
Database Script (DDL)
vs. Physical Data Model
product_id INTEGER NOT NULL,
product_name VARCHAR(50) NULL,
product_price NUMBER NULL);
ALTER TABLE PRODUCT
ADD ( PRIMARY KEY (product_id) ) ;
CREATE TABLE DEPARTMENT (
department_id INTEGER NOT NULL,
department_name VARCHAR(50) NULL);
ALTER TABLE DEPARTMENT
ADD ( PRIMARY KEY (department_id) ) ;
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);
ALTER TABLE EMPLOYEE
ADD ( PRIMARY KEY (employee_id) ) ;
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);
ALTER TABLE CUSTOMER
ADD ( PRIMARY KEY (customer_id) ) ;
CREATE TABLE ZORDER (
zorder_id INTEGER NOT NULL,
employee_id INTEGER NOT NULL,
customer_id INTEGER NOT NULL,
zorder_date DATE NULL);
ALTER TABLE ZORDER
ADD ( PRIMARY KEY (zorder_id) ) ;
âA picture is worth a thousand wordsâ
33. P A G E 3 3
CONCEPTUAL LOGICAL PHYSICAL
Defines the scope, audience, context for
information
Defines key business concepts and their
definitions
Represents core business rules and data
relationships at a detailed level
Main purpose is for communication and
agreement of scope and context
Main purpose is for communication and
agreement of definitions and business logic
Provides enough detail for subsequent first
cut physical design
Relationships optional. If shown, represent
hierarchy.
Many-to-Many relationships OK Many-to-Many relationships resolved
Cardinality not shown Cardinality shown Cardinality shown
No attributes shown Attributes are optional. If shown, can be
composite attributes to convey business
meaning.
Attributes required and all attributes are
atomic. Primary and foreign keys defined.
Not normalized (Relational models) Not normalized (Relational models) Fully normalized (Relational models)
Subject names should represent high-level data
subjects or functional areas of the business
Concept names should use business
terminology
Entity names may be more abstract
Subjects link to 1-M HDMs Many concepts are supertypes, although
subtypes may be shown for clarity
Supertypes all broken out to include sub-
types
âOne pagerâ Should be a âone pagerâ May be larger than one page
Business-driven Cross-functional & more senior people
involved in HDM process with fewer IT.
Multiple smaller groups of specialists and IT
folks involved in LDM process.
Informal notation âLooserâ notation required â some format
construct needed, but ultimate goal is to be
understood by a business user
Formal notation required
< 20 objects < 100 objects > 100 objects
Comparing Conceptual, Logical & Physical
Models
34. P A G E 3 4
Roles Culture
3NF
DBAs, Data Architects and Executives are different creatures
DBA
âą Cautious
âą Analytical
âą Structured
âą Doesnât like to talk
âą âJust let me code!â
Data Architect/Data Modeler
âą Analytical
âą Structured
âą Passionate
âą âBig Pictureâ focused
âą Likes to Talk
âą âLet me tell you about my data
model!â
Business Executive
âą Results-Oriented
âą âBig Pictureâ focused
âą Little Time
âą âHow is this going to help me?â
âą âI donât care about your data
model.â
âą âI donât have time.â
35. P A G E 3 5
Data Drives the Business
â Make sure itâs Correct
In todayâs information age, data drives key business
decisions.
Executives ask questions such as:
âș How many customers do I have?
âș What is total revenue by region for last fiscal year?
âș Which products drove the most revenue this quarter?
Behind the answers to those questions lies a data
model:
âș Documenting the source and structure of data
» What database(s) store customer information
» How are these databases structured to store customer
information
âș Defining key business terms
» What is a product? e.g. Finished goods only? Raw
materials?
âș Regulating business rules
» Can a customer have more than one account?
36. P A G E 3 6
Information in Context
Thereâs more to data than meets the eye
Iâd like a
report showing
all of our
customers
SUPPORT
ENGINEER
A personâs not a
customer if they
donât have an
active maintenance
account.
SALES
A customer is
someone who
wants to buy
our product.
SYBASE
DB2
ORACLE
SQL
SERVER
MS
SQL
AZURE
INFORM
IX TERADA
TA
SAP
DBA
Which customer
database do you
want me to pull this
from? We have 25.
BUSINESS
EXECUTIVE
DATA
ARCHITECT
And, by the way, the
databases all store
customer information
in a different format.
âCUST_NMâ on DB2,
âcust_last_nmâ on
Oracle, etc. Itâs a mess.
ACCOUNTING
A customer is
someone who
owns our product.
HUMAN
RESOURCES
My customers
are internal
employees.
37. P A G E 3 7
The Challenge
âș Youâve been tasked to assist in the creation of a
Data Warehouse
âș Trying to obtain a single view of âcustomerâ
âș Technical and political challenges exist
» Numerous systems have been built
alreadyâdifferent platforms and
databases
» Parties cannot agree on a single definition
of what a âcustomerâ is
Solution:
Need to build a Conceptual Data Model
38. P A G E 3 8
Building a Conceptual Data Model
âș Our challenge is to achieve a âsingle version of the
truthâ for Customer information
âș We have 6 different systems with customer
information in them:
» 2 on Oracle
» 1 on DB2
» 1 using legacy IDMS
» 1 SAP system
» 1 using MS SQL Server
DB2
Oracle
Oracle
IDMS
SQL
Server
SAP
39. P A G E 3 9
Building a Conceptual Data Model
âș We start with a very simple CDM, with
just one object on it, called
âCustomerâ.
âș We use an data model and show
business definitions
Too Simple??
Customer
A person or organization that has
purchased at least one of our products
and has an active account.
40. P A G E 4 0
Too simple?
Our team thought so, so went ahead and focused on the
technical integration, including:
âș Reverse engineering a physical model from each system
âș Profiling the data to ensure data type consistency
âș Creating ETL scripts
âș Migrating the data into the data warehouse
âș Building a reporting system off of the data
41. P A G E 4 1
Focusing on the Business
âș This implementation went âperfectlyâ, with no errors
in the scripts, no data type inconsistencies, no
delays in schedule, etc.
âș We built a complex BI reporting system to show our
upper management the results.
âș We even sent out a welcome email to all of our
customers, giving them a 50% off coupon, and
thanking them for their support.
42. P A G E 4 2
Focusing on the Business
Until we showed the report to the business sponsor:
âș We canât have 2000 customers in this region! I know
we only have around 400!
âș Why is Jonesâ Tire on this list? They are still evaluating
our product! Sales was negotiating a 10% discount
with them, and you just sent them a 50% coupon!?!?
âș You just spent all of that money in IT to build this
report with bad data???
43. P A G E 4 3
Back to the Drawing Board
After doing an extensive review of the six source systems, and talking with
the system owners we discovered that:
âș The DB2 system was actually used by Sales to track their prospective âcustomersâ
âș These âcustomersâ didnât match our definitionâthey didnât own a product of ours!!
Customer
A person or organization who does not
currently own any of your products and
who is potentially interested in purchasing
one or more of our products.
Customer
A person or organization that has
purchased at least one of our products
and has an active account.
44. P A G E 4 4
Oops!
We were mixing current customers, with prospects (non-customers).
âș We just sent a discount coupon to 1600 of the wrong people!
âș We gave upper management a report showing the wrong figure for our total
number of customers!
âș We are now significantly over budget to have to go back and fix this!!
We started over, this time with a Conceptual Data Model
45. P A G E 4 5
Achieving Consensus
We created a report of the various
definitions of customerâŠ
And verified with the various stakeholders
that:
âș There were 2 (and only 2 definitions) of
customer
âș Sales was OK with calling their âcustomerâ
a âprospectâ
Customer
A person or organization who does not
currently own any of your products and
who is potentially interested in purchasing
one or more of our products.
Customer
A person or organization that has
purchased at least one of our products
and has an active account.
46. P A G E 4 6
Resolving Differences
Our new high-level data model
looked like this:
47. P A G E 4 7
Identify Model Stakeholders
Make sure ALL relevant
parties are involved in
the design process â
Get buy-in!
48. P A G E 4 8
Identify Model Purpose
âș Key to success of any project is finding the right pain-point and solving it.
âș Make sure your model focuses on a particular pain point, i.e. migrating an
application or understanding an area of the business
Existing Proposed
Business
âToday, we have a poor
customer list.â
âBy next quarter, we want to
identify a quality list of potential
new customer prospects in the
Northeast region.â
Application
âThe legacy Account
Management system is
managed in DB2.â
âWe need to migrate the legacy
Account Management system to
SQL Server, for finance reasons.â
49. P A G E 4 9
A CDM Facilitates Communication
Focus on your (business) audience
âș Intuitive display
âș Capture the business rules and definitions in your model
Simplicity does not mean lack of importance
âș A simple model can express important concepts
âș Ignoring the key business definitions can have negative affects
A model or tool is only part of the solution
âș Communication is key
âș Process and Best Practices are critical to achieve consensus and buy-in
A Conceptual Data Model Facilitates
Communication between Business and IT
50. P A G E 5 0
Communication is the Main Goal
of a Conceptual Data Model
âș Wouldnât it be helpful if we did this in daily life, too?
âș i.e. âLetâs go on a family holiday!â
PERSON CONCEPT DEFINITION
Father Vacation
An opportunity to take the time to achieve
new goals
Mother Vacation Time to relax and read a book
Jane Vacation A chance to get outside and exercise
Bobby Vacation Time to be with friends
Donna Vacation More time to build data models
51. P A G E 5 1
Tactics for
Improving Communication
Start small
âș Donât try & boil the ocean
âș Gain consensus â then drill into more detail & widen scope
Re purpose the models and data
âș Mask detail
âș Avoid Data Modeling geek language
Donât become a methodology obsessive!
Go out of your way to engage with stakeholders
âș Lunch & learn
âș Webinars
52. P A G E 5 2
The basics of a good
workshop / interview
BEFORE
âș Choose attendees carefully for a broad representation
âș Reserve interviews for senior/time-limited stakeholders
âș Do your homework! Research. Prepare questions
DURING
âș Use the attendeesâ own language, avoid jargon
âș Use open questions, listen, and playback understanding
AFTERWARDS
âș Photograph whiteboard / flipchart output
âș Write up your notes ASAP, omit nothing yet
TOP DOWN FACT FINDING
53. P A G E 5 3
Collect: Gather the Nouns
Students enroll for a course by
submitting an application via our
web portal, providing their name,
date of birth, email, selected
course and card details.
TopTrainingCorp arranges for
distribution of the necessary
payment to the relevant
examination centre and
certification body. All our courses
are approved by the relevant
certification body.
Instructors deliver our courses over
3 days after which the student sits
2 examinations consisting of 40
multiple-choice questions.
What the business says⊠What the analyst hearsâŠ
Student blah blah course blah
blah application blah blah
name, email, selected course,
card details.
Blah blah payment blah blah
examination centre blah blah
blah certification body.
blah blah blah course blah blah
blah certification body. Instructor
blah blah blah course blah blah
blah student blah blah blah
examination blah blah question.
54. P A G E 5 4
Workshop Activity
INSURECo Description
INSURECo assists Customers manage their risk. In exchange for a
constant stream of Premiums, INSURECo pays Customers a sum of
money upon the occurrence of a predetermined event, such as a
natural catastrophe, a car crash, or a doctor's visit.
INSURECo create value by pooling and redistributing various types
of risk. It does this by collecting liabilities (i.e. premiums) from
everyone that it insures and then paying them out to the few that
actually need them. INSURECo can then effectively redistribute
those liabilities to entities faced with some sort of event-driven crisis,
where they will ostensibly need more cash than they currently have
on hand. As not everyone within the pool will actually suffer an
event requiring the total use of all of their premiums, this pooling
and redistribution function lowers the total cost of risk management
for everyone in the pool.
INSURECo can generate profits in two ways:
âą By charging enough premiums to cover the expected payouts
that they will have to cover over the life of the policy
âą By earning investment returns ("the float") using the collected
premiums
INSURECoâs earnings ratio leans heavily towards investment returns.
A large percentage of premiums are paid out which assists in
attracting larger customer volumes and liabilities.
⊠KEY DATA THINGS?
Jeff has been an INSURECo customer since starting his
landscaping business in 2001. Insurance is extremely
important Jeff.
Jeff requires Commercial Liability coverage to protect
himself in case of damage done to a client's property.
Jeff also requires to have his equipment/tools insured.
This includes a number of vehicles that will be driven
for business on a commercial auto policy.
As Jeff is responsible for a number of staff it was
important for his commercial policy to include options
for covering specific items such as worker's
compensation.
Jeff â Business Owner / Contract Landscaper
I N S U R E C O C U S T O M E R P R O F I L E
55. P A G E 5 5
Workshop Activity
Organising the Terms
Break into small groups.
Using the INSURECo company description,
identify the ânounsâ or âconceptsâ or
âthingsâ that are important to the business.
Tip: Itâs helpful to circle the key nouns in
the text.
Create a Post It Note âData Thingâ for
each of the key terms that are important
for the business.
Time: 20 minutes
THINGS (CONCEPTS)
Customer
Concept
#2
Concept
#3 Etc.
56. P A G E 5 6
Workshop Activity
INSURECo DescriptionâŠ
INSURECo assists Customers manage their risk. In exchange for a constant stream of Premiums,
INSURECo pays Customers a sum of money upon the occurrence of a predetermined event, such as a
natural catastrophe, a car crash, or a doctor's visit.
INSURECo create value by pooling and redistributing various types of risk. It does this by collecting liabilities
(i.e. premiums) from everyone that it insures and then paying them out to the few that actually need
them. INSURECo can then effectively redistribute those liabilities to entities faced with some sort of event-
driven crisis, where they will ostensibly need more cash than they currently have on hand. As not everyone
within the pool will actually suffer an event requiring the total use of all of their premiums, this pooling and
redistribution function lowers the total cost of risk management for everyone in the pool.
INSURECo can generate profits in two ways:
âą By charging enough premiums to cover the expected payouts that they will have to cover over the
life of the policy
âą By earning investment returns ("the float") using the collected premiums
INSURECoâs earnings ratio leans heavily towards investment returns. A large percentage of premiums
are paid out which assists in attracting larger customer volumes and liabilities.
⊠KEY DATA THINGS?
57. P A G E 5 7
Break
LETâS TAKE 15 MINUTES FOR A BREAK!
58. P A G E 5 8
Data Modelling & Information
Management
59. P A G E 5 9
DAMA DMBOK
Framework
DATA
ARCHITECTURE
MANAGEMENT
DATA
DEVELOPMENT
DATABASE
OPERATIONS
MANAGEMENT
DATA
SECURITY
MANAGEMENT
REFERENCE &
MASTER DATA
MANAGEMENT
DATA
QUALITY
MANAGEMENT
META DATA
MANAGEMENT
DOCUMENT &
CONTENT
MANAGEMENT
DATA
WAREHOUSE
& BUSINESS
INTELLIGENCE
MANAGEMENT
DATA
GOVERNANCE
Knowledge Areas
60. P A G E 6 0
DATA
ARCHITECTURE
MANAGEMENT
DATA
DEVELOPMENT
DATABASE
OPERATIONS
MANAGEMENT
DATA SECURITY
MANAGEMENT
REFERENCE &
MASTER DATA
MANAGEMENT
DATA QUALITY
MANAGEMENT
META DATA
MANAGEMENT
DOCUMENT & CONTENT
MANAGEMENT
DATA
WAREHOUSE
& BUSINESS
INTELLIGENCE
MANAGEMENT
DATA
GOVERNANCE
âș Enterprise Data Modelling
âș Value Chain Analysis
âș Related Data Architecture
âș External Codes
âș Internal Codes
âș Customer Data
âș Product Data
âș Dimension Management
âș Acquisition
âș Recovery
âș Tuning
âș Retention
âș Purging
âș Standards
âș Classifications
âș Administration
âș Authentication
âș Auditing
âș Analysis
âș Data modelling
âș Database Design
âș Implementation
âș Strategy
âș Organisation & Roles
âș Policies & Standards
âș Issues
âș Valuation
âș Architecture
âș Implementation
âș Training & Support
âș Monitoring & Tuning
âș Acquisition & Storage
âș Backup & Recovery
âș Content
Management
âș Retrieval
âș Retention
âș Architecture
âș Integration
âș Control
âș Delivery
âș Specification
âș Analysis
âș Measurement
âș Improvement
Where does
Data Modelling
fit?
61. P A G E 6 1
Data Modelling for Data Warehousing (DW)
and Business Intelligence (BI)
DATA WAREHOUSE BI REPORT:
CUSTOMERS BY REGION
DATA WAREHOUSING BI REPORTING
âș What is the definition of customer?
âș Where is the data stored?
âș How is it structured?
âș Who uses or owns the data?
Show me all
customers by
region
âș What do I want to report on?
âș How do I optimize the database
for these reports?
A Data Model is the
âIntelligence behind
Business Intelligenceâ
â Understand source and target data systems
â Define business rules
â Optimize data structures to align queries with reports
Data
Warehouse
62. P A G E 6 2
Scenario:
Who is our âCustomerâ?
Has this ever happened to you?
âș Youâve had a particular credit card for 10 years,
and you regularly pay your full balance every
month.
âș In addition to your monthly bill, you also receive a
separate letter asking you to âSign up for our
credit card! Great Interest Rate!â
Why does this happen? Donât they know that you
already own a card?
How could a data model help?
63. P A G E 6 3
Who is our âCustomerâ?
Look familiar?
64. P A G E 6 4
Data Modelling for
Application Development
âș The majority of todayâs applications are
data-driven
âș A data model is a key part of the
application development lifecycle
âș Reuse of common data objects helps
promote
âș Increased efficiency â donât âreinvent the
wheelâ
âș Better collaboration
âș Increased quality and consistency
65. P A G E 6 5
Scenario:
Expense Tracking Application âBugâ
Acme corporation has an Expense Billing application for its
employees.
âș For each business trip, employees were asked to specify
which department should be billed.
âș This caused a problem for employees whose job role spanned
several departments.
A product manager, for example, needed to bill Development for
business trips to manage product releases, but needed to bill Sales for
business trips to support customers.
âș With the existing database structure, this was impossible.
âș When management asked for an estimate of what it would
cost to correct this, the expense was in the thousands of
euros.
How could a data model help?
66. P A G E 6 6
Expense Tracking Application âBugâ
âș When the original database system was built, the
developer assumed that each employee works
for a single department.
âș He structured the system in such a way that only
a single department name could be associated
with each employee.
âș This could have been fixed with a simple data
model design.
67. P A G E 6 7
Expense Tracking Application âBugâ
Each employee can bill
expenses to multiple
departments
(Better implemented as: )
68. P A G E 6 8
Data Modelling for Metadata Management
Metadata is âInformation in Contextâ
A Data Model helps Provide this Context
Customer Name Company City Year Purchased
Joe Smith Komputers R Us New York 1970
Mary Jones Big Bank Co London 1999
Proful Bishwal Little Bank Inc Mumbai 1998
Ming Lee My Favorite Store Beijing 2001
METADATA
DATA
Who is using the
data?
Who owns the
data?
Where is the
data stored?
When was it last
updated?
What are the business
definitions?
Why are we
tracking the data?
What is the structure and
format of the data?
METADATA
69. P A G E 6 9
Enterprise Architecture provides a high-level view of the people, processes,
applications, and data of an organization
Putting data in business context:
âș How does data link to the rest of my organization?
âș If I change data, what business processes are affected?
Data Modelling for Enterprise Architecture
70. P A G E 7 0
Data Modelling for
Database Management
âș Know what data you have: Create a visual inventory of database systems
âș Know what your data means: Communicate key business requirements between
business and IT stakeholders
âș Support data consistency: Build consistent database structures with common
definitions
SYBASEORACLE
DB2
TERA-
DATA
SQL
SERVER
MySQL
SQL
SERVER
MySQL
SYBASE
DB2
ORACLE
TERA-
DATA
71. P A G E 7 1
Data Modelling for
Master Data Management
Master Data Management strives to create a âsingle version of the truthâ
for key business data: customer, product, etc.
Using a central data model helps define:
âș Common business definitions
âș Common data structures
âș Data lineage between defined âversion of the truthâ and real-world
implementations
72. P A G E 7 2
MDM:
Plan Big â Implement Small
Business Initiative / Project 1
Some of MD
area A
needed here
Some of MD
area B
needed here
Business Initiative / Project 2
More of MD
area A
needed here
Some of MD
area C
needed here
More of MD
area B
needed here
Business Initiative / Project 3
More of MD
area C
needed here
More of MD
area B
needed here
More of MD
area A
needed here
Business Initiative / Project 4
Lots of MD
area D
needed here
More of MD
area B
needed here
More of MD
area A
needed here
Project4 later
includes MD for
area D
MDM program cannot deliver Data Subject
Area D at this time for Project 4.
Project 4 gains exemption to add this MD later
IN THE CONTEXT OF THE BIG PICTURE
VIA THE CONCEPTUAL DATA MODEL
IMPLEMENT MDM IN ALIGNMENT WITH BUSINESS INITIATIVES
73. P A G E 7 3
Data Modelling for Data Governance
âș Data, like money, is a corporate asset, and
needs to be managed accordingly.
âș Like an auditing department for finance,
data governance provides the guidelines,
accountability and regulations around data
management.
âș A Data Model can help define:
» 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?
74. P A G E 7 4
Data Modelling for
Data Lineage: e.g. SOX
Financial
reporting &
auditing
requirements
Near real
time reporting
Accuracy of
Financial
statements
Effectiveness
of internal
controls
INFORMATION
MANAGEMENT ESSENTIAL
DATA LINEAGE
IMPLICATIONS
DATA GOVERNANCE
IMPLICATIONS
DATA QUALITY &
DEFINITIONS IMPLICATIONS
75. P A G E 7 5
Data Modelling for
Package / ERP systems
Data Requirements For
Configuration & Fit For
Purpose Evaluation
Data Integration &
Governance
Legacy Data Take On Master Data Integration
DATA MODEL
76. P A G E 7 6
Data Modelling For Packages / ERP Systems
Data lineage (particularly important with
Data Lineage & SOX compliance issues)
Master Data alignment
For Data migration / take on
Identifying gaps
For requirements gathering ... But what if
weâve got to use package X?
CUSTOMER
ORDER
CUSTOMER
ORDER
VS
77. P A G E 7 7
Data Modelling for
Data Virtualisation
Virtual Operational
Data Stores
Shareable Data
Services
D A T A MOD E L
S QL
W E B
S E R VIC ES
S T A R
Virtual
Data Marts
Relational
Views
LEGACY
MAINFRAMES
FILES RDBMS
WEB
SERVICES
PACKAGES
BI, MI AND
REPORTING
CUSTOM APPS
PORTALS &
DASHBOARDS
ENTERPRISE
SEARCH
78. P A G E 7 8
Data Modelling Spans
All Disciplines
Data is at the heart
of ALL architecture
disciplines
Data has to be
understood to be
managed
Different levels of
models for different
purposes
Itâs NOT just for
DBMS design
Data models are not
(just) art
Professional
development:
certification &
training
All of the Architecture disciplines use the language
(and rules) of the data model
80. P A G E 8 0
Validate and Refine
Business Goals
âș A Goal is a statement about a state or condition
of the enterprise to be brought about or
sustained through appropriate Means.
âș A Goal amplifies a Vision â that is, it indicates
what must be satisfied on a continuing basis to
effectively attain the Vision.
âș A Goal should be narrow â focused enough that
it can be quantified by Objectives.
âș A Vision, in contrast, is too broad or grand for it to
be specifically measured directly by Objectives.
However, determining whether a statement is a
Vision or a Goal is often impossible without in depth
knowledge of the context and intent of the business
planners.
In light of the mission and vision and the influencer
pressures, validate and refine the goals of the
organisation
81. P A G E 8 1
Look for Levers
Derive a set of measurable levers of business value
and growth by cascading down the drivers of
income in your business.
âș The levers are intended to be durable even as
business strategy shifts.
Value levers indicate which business dimensions
need to be analysed for change projects.
âș Business consultants use the matrix to understand
which business architecture dimensions have the
greatest impact on each lever, focusing attention on
those dimensions most relevant to the levers in focus.
Look for levers that can help you address
the goals
82. P A G E 8 2
Improvement
Levers Example
Increase price
Increase volume
Improve mix
Improve process
Reduce cost of inputs
Improve warehouse
utilisation
Increase productivity
Decrease staffing
Optimize scheduling
Optimize physical network
Decrease staffing
Use alternative distribution
Lower Customer Service &
Order Management Costs
Lower I/S costs
Lower Finance /
Accounting costs
Lower HR costs
Improve capital planning/
investment process
Reduce inventories
Reduce A/R increase A/P
o Profit-driven marketing
efforts:
âą Target âbestâ customers
âą Offer âbestâ product mix
âą Improve pricing
management
âą Proactive production
planning for inventory
management
âą Most profitable capacity
allocation/utilization
o Reduced sales
management layers
o Focus on high-profit
accounts
o Improved inventory flow
visibility
âą Lower transportation costs
âą Higher facilities utilization
âą Less âfire fightingâ
o Better carrier
evaluation/mgmt.
o Higher quality Customer
Service
o Improved Supply Chain
visibility
âą Improved order fill rates
âą Significantly lower cost
âą More consistent service
âą Faster problem resolution
o Improved capital
stewardship
âą Increased capital
productivity
âą Reduced inventory
investment
âą Reduced receivables
investment
o Automated PO
requisitions
o Improved information for
evaluating vendors
o Automation of some
scheduling functions
o Single point of entry
eliminates data re-entry
and improves accuracy
o Faster data reconciliation
o Automated billing
processes
o Automated payroll
processes
o Moderately lower safety
stock inventory
o Moderately improved
A/R and A/P
management
Increase
revenues
Decrease
costs
Reduce
selling costs
Reduce
distribution
costs
Reduce
administrative
costs
Increase
gross profit
Decrease
operating
expenses
Capital
deployment
Cost
of capital
Increase net
operating
profit after tax
(NOPAT) (I/S)
Improve
capital
allocation
(B/S)
Enterprise
Value
Map
VALUE LEVERS
TRANSFORMATION
BENEFIT (Outcome)
AUTOMATION
BENEFIT
83. P A G E 8 3
Goals, drivers and
levers are tightly integrated
The value map will help
identify enterprise aligned
business unit drivers and
leverage points
Examples
âą Revenue enhancement
âą Margin enhancement
âą Operating efficiencies
âą Working capital
management
âą Investment capital
productivity
âą Capital structure
optimization
CORPORATE
LEVEL-SPECIFIC
Examples
âą Pricing strategy
âą Product assortment
âą Departmental emphasis
âą Product cost
âą Store operating costs
âą Corporate administrative
costs
âą Operating cash reserves
âą Inventory management
âą Accounts payable
management
âą Store base
âą Leases
âą Intangible assets
âą Distribution assets
BUSINESS
UNIT-SPECIFIC
Examples
âą Purchase frequency
âą Household penetration
âą Transaction size
âą Gross margin
âą Store operating expense
%
âą SG&A expense %
âą Distribution cost per case
âą Days cash on hand
âą Inventory turns-store and
DC
âą Account payable cycle
âą Store-level profitability
âą Debt/Equity ratio
âą Annual capital
investment
OPERATING
VALUE DRIVERS
Earning loyalty & trust
with customers &
community
Make our
Processes simpler
and faster
Empower the frontline to
Deliver Integrated
Financial Solutions
Deliver new
and relevant
products
Create a
performance
culture
REVENUE
COSTS
WORKING
CAPITAL
FIXED
CAPITAL
NOPLAT
INVESTED
CAPITAL
EVM
Goals and Drivers help define value drivers and improvement levers
84. P A G E 8 4
âș Your team has been brought on
board to help address some of the
information related stakeholder
concerns
âș INSURECoâs CIO intuitively understands
some of these concerns are due to
poor management of data and
information across the organisation
âș The CIO has engaged your team to
assist him to understand how he can
best get control of the situation and,
as a result, elevate a number of
stakeholder concerns
âș The CIO is specifically interested in
understanding what information
management practices need to put
in place to avoid finding himself in this
position again in the future
Workshop Activity
Problem Briefing
85. P A G E 8 5
Example
Decomposition
What are our corporate goals?
What are the priorities and battlegrounds given our corporate goals?
What IT assets and data do we need to support these capabilities?
How will our business model change over the next three to five years?
What are the key capabilities that will maximize value creation in the business?
How do we optimize our IT operating model to deliver the required business capabilities?
Earn all
Our customersâ
business
Drive a strong
Customer culture
Enhanced branch
Capability and
Power in frontline
Transform
Service delivery
And processes
Customer
centricity
Front office
empowerment
Channel and
Product operational
excellence
Customer
Profile
management
Relationship
management
Customer
analytics
Offer
design
Product
management
Integrated
Data store
Enterprise
Service bus
Channel
platforms
Product
platforms
Security
platforms
End-user
computing
Customer
analytics engine
Universal
customer master
Integrated sales &
Service front end
Internet platform
transformation
Core banking
transformation
Vision
Strategic agenda
Business objectives
Business capabilities
IT capabilities
IT investments
86. P A G E 8 6
Problem Briefing âWorkflow is
inadequate for
Product
Development,
Underwriting and
Legal &
Complianceâ
âFirst contact
resolution is an
important goal for
Enquiries and
Complaintsâ
âRolling out new
products to
customers is
expensive and takes
a long time. Product
Releases should be a
BAU activity â
âNo correlation
between claims
and reinsurance
systemsâ
âManagement
Reporting is complex
and uses a lot of
disparate systems
and spreadsheets to
produceâ
âWe have key
person
dependencies
in a number of
teamsâ
âErrors are
only picked
up at policy
anniversaryâ
âKnowledge,
Document and
Content
Management is
inadequateâ
â50% of errors are
associated with either
missing a process
step or sending
paperwork to the
wrong placeâ
Are the strategic
programs aligned,
or for that matter,
are they the right
strategic
programs?
Are we investing
in the right areas
across the
enterprise?
Is my investment
portfolio
balanced across
my strategic and
tactical issues
Where can we
take advantage
of synergies
across the major
strategic
programs?
There is a lot of
activity going on
out there, how do
I know we are
doing the right
things?
INSURECo Stakeholder Concerns
87. P A G E 8 7
Workshop Activity
Describe the problem space using
Text-based & Visual models
âș Think about âa day in the lifeâ of INSURECo with specific focus
on the environment and problem space. List these as text
âș Flip over the card and now draw a picture of the problem
âș Post the picture on the wall and explain it to the class. Take
note of common elements
âș Time: 20 minutes
âș After descriptions, the group must reflect on the
similarities and differences and work towards a shared
understanding of the problem space
âș Document this shared understanding on the flip chart
and post on the wall
INDEX CARD â FRONT AND BACK
89. P A G E 8 9
Data Modelling does not
have to be Complicated!
If you can write a sentence, you can build a data model.
If you understand how your business works, you can build a
data model.
Businesspeople should be involved in the development of data
models, because only they understand the business needs
and rules.
Understanding data modelling basics will help the Business
better communicate with IT
90. P A G E 9 0
What is an Entity?
Entity: A classification of the types of objects found in the real world --
persons, places, things, concepts and events â of interest to the
enterprise. 1
1 DAMA Dictionary of Data Management
WHO? WHERE?
WHEN? HOW?WHY?
WHAT?
The âWho, What, Where, When, Whyâ of the Organization
91. P A G E 9 1
Entities are the âNounsâ
of the Organization
âș Who? Employee, Customer, Student, Vendor
âș What? Product, Service, Raw Material, Course
âș Where? Location, Address, Country
âș When? Fiscal Period, Year, Time, Semester
âș Why? Transaction, Inquiry, Order, Claim, Credit, Debit
âș How? Invoice, Contract, Agreement, Document
92. P A G E 9 2
Sample Entities
Product
Customer
Location
Order
Raw
Material
Ingredient
Region
93. P A G E 9 3
Baker Cakes, Inc.
âș Baker Cakes is a family-run business whose
main stakeholder is Bob Baker, the
owner/operator of Baker Cakes. Bob is in
charge of making most decisions, from
database design to icing color selection.
âș In our example, weâre building a new
application for Baker Cakes, who is looking
to build a new application to manage their
data.
94. P A G E 9 4
What Entities are needed
for this Cake Company?
Exercise: List some entities that would be needed for Baker
Cakes, Inc. to manage their corporate data
â Customer
â Product
â Invoice
â Ingredient
â Raw Material
â Flavor
95. P A G E 9 5
Attributes An Attribute is a piece of information
about or a characteristic of an Entity.
Attributes
Entity
Employee âą Employee Identifier
âą Employee Last Name
âą Employee First Name
âą Employee Hire Date
âą Employee Signed Employment Contract
âą Employee Drivers License Photo
Entity
Attributes
96. P A G E 9 6
Relationships are the âVerbsâ
of the Organization
Relationships define the data-centric Business Rules of an organization
âș An employee can work for more than one department.
âș A customer can have more than one account.
âș Sales are reported monthly.
âș A department can contain more than one employee.
â Relationships are the âverbsâ in a sentence
âą A department can contain more than one employee.
Defining Business Rules
â Relationships are the âlinesâ on a data model
Contain
Department Employee
97. P A G E 9 7
Contain
Department Employee
Cardinality = âHow Many?â
âCardinalityâ is the term used to describe the numeric relationships
between the entities on either end of the relationship line.
A department can contain more than one employee.
98. P A G E 9 8
Deciphering Cardinality
Think of how a child might answer the question âHow many?â
âș One = 1 finger
âș More than one = several fingers
Contain
Department Employee
99. P A G E 9 9
Optionality = Required
âOptionalityâ describes what may or must happen in a relationship.
A department may contain more than one employee.
i.e. âzero, one, or manyâ
A department must contain more than one employee.
i.e. âone, or manyâ
i.e. âMustâ or âMayâ
Contain
Department Employee
Contain
Department Employee
100. P A G E 1 0 0
Data Modeling is Similar to Diagramming a
Sentence
1. Place boxes around the ânounsâ, or entities.
2. Underline the verb
3. Circle the âhow manyâ qualifier.
4. Look for optionality words (e.g. âmay/mustâ)
A department may contain more than one employee.
Contain
Department Employee
101. P A G E 1 0 1
Exercise:
Practice âReadingâ the following
Relationships/Business Rules
âș Each Employee may process one or many Transactions.
âș Each Transaction must be processed by one Employee.
âș Each Customer may place one or many Orders.
âș Each Order must be placed by one Customer.
Process
Employee Transaction
Place
Customer Order
102. P A G E 1 0 2
Exercise:
Create a Data Model
from a Business Rule
Create a Data Model for the following business rule:
A customer may own more than one account.
Thought for this exercise:
Can an account have more than one customer associated with it?
If so, how would you show this?
103. P A G E 1 0 3
Exercise:
Create a Data Model
from a Business Rule
Create a Data Model for the following business rule:
A customer may own more than one account.
Own
Customer Account
104. P A G E 1 0 4
What are Keys?
A Key is an attribute or group of attributes that uniquely identifies an Entity.
For example,
â A Customer is identified by a Customer ID
â A Student is identified by Last Name, First Name, and Date of Birth
105. P A G E 1 0 5
Candidate Keys
âș What attributes might uniquely identify an entity? Letâs use Customer as an example.
âș What might uniquely identify an individual customer?
Is Last Name + First Name enough?
â Could there be 2 customers named John Smith? ï Probably
Is Last Name + First Name + Date of Birth enough?
â Could there be 2 customers named John Smith born on 1 June, 1963? ï Less
Likely, but Possible
Is Last Name + First Name + Date of Birth + Address enough?
â Could there be 2 customers named John Smith born on 1 June, 1963 living at 1
Earlâs Court, London, UK? ï Even Less Likely. Possible, but how many attributes
do we want to use?
106. P A G E 1 0 6
Candidate Keys:
Natural vs. Surrogate
âș The keys we just identified would be
classified as natural keys.
âș Natural keys are based on business rules
and logic that determine how an individual
instance can be uniquely identified.
âș As weâve seen, natural keys can become
unwieldy, requiring a number of attributes,
which makes queries difficult.
â Surrogate keys are often used instead,
which are system-generated unique
identifiers. e.g. Customer ID, Product ID, etc.
â While surrogate keys are more efficient,
important business rules are lost when they
are used. Itâs a balancing act.
107. P A G E 1 0 7
Alternate Keys
Alternate Keys can be defined to keep the business
rules of a natural key, while still achieving the
efficiency of a surrogate key.
In the example below:
âș Student Number is the primary key (surrogate key)
âș Student Last Name + Student First Name + Student
Date Of Birth is the natural key
Alternate keys can be used to retrieve
information more quickly in a database (e.g.
index by last name + first name + date of birth).
Student
Student Number
Student First Name (AK1.2)
Student Last Name (AK1.1)
Student Date of Birth (AK1.3)
108. P A G E 1 0 8
Primary Keys
A Primary Key is an attribute or group of attributes that uniquely identifies an Entity.
A primary key is shown âabove the lineâ in a data model.
Primary Key
Customer
Customer ID
Customer First Name
Customer Last Name
Customer Date of Birth
109. P A G E 1 0 9
Supertypes and Subtypes
Supertypes and Subtypes are a
mechanism for grouping objects
with similar (but not identical)
characteristics.
For
example:
Individual
Customer ID (FK)
Customer First Name
Customer Last Name
Customer Gender
Customer Date of Birth
Corporation
Customer ID (FK)
Company Name
Corporate Logo
Customer
Customer ID
First Date of Purchase
Common
Attributes
Supertype
Subtype
Unique
Attributes Unique
Attributes
Subtype
111. P A G E 1 1 1
Dimensional Data Modelling
âș Relational data modeling describes
business rules around transactional
systems.
âș Dimensional data modeling describes
structures for aggregation and
reporting, typically for business
intelligence.
112. P A G E 1 1 2
Data Warehousing and BI
âș Typically a Data Warehouse is built for a Business Intelligence (BI) project
âș Need to transform and summarize the relational tables from operational systems, to a
single warehouse summarized for reporting.
âș âETLâ is the process of Extracting, Transforming, and Loading the Warehouse.
Data
Warehouse
ETL
RELATIONAL DIMENSIONAL
ERP System
Sales DB
Sales DB (2) Sales DB (3)
Accounting
DB
113. P A G E 1 1 3
Dimensional Data Modeling
For BI, an easy way to think of a dimensional model is to ask yourself:
âWhat do I want to report by?â (Apologies to grammarians!), e.g.
âș by month
âș by region
âș by quarter
âș by product
The lines on a dimensional data model represent navigation paths,
not business rules.
114. P A G E 1 1 4
Sample Dimensional
Data Model âStar Schemaâ
In this case, we want to report
on Sales by Product, by
Month, by Sales Rep, and by
Region.
FACT TABLE
DIMENSION
DIMENSION
DIMENSION
DIMENSION
115. P A G E 1 1 5
Dimensional Data Modeling
FACTS
Contain the actual values to be
reported upon. e.g. Sales Figures
âș Few attributes (with links/keys to
the dimensions)
âș Many values
DIMENSIONS
Contain the details that describe
the central fact.
âș Many attributes
âș Few values
116. P A G E 1 1 6
Workshop:
Employee Salary Reporting
âș You work in Human Resources and your company is
implementing a new employee salary and benefit reporting
system.
âș IT asks you for your requirements. You can impress them by
sending them a well-formed dimensional model.
âș Your report needs to show your companyâs total salary
spending by region. You also want to know which managers
are paying their employees the most. And youâll want to see
which roles in the organization make the most money.
âș Create a conceptual data model for this reporting system.
Donât worry about entering definitions for the concepts at this
pointâjust show the concepts that you need to report on and
report by. (Hint: this will be a three-sided star).
117. P A G E 1 1 7
Example:
Employee Salary Reporting
Your conceptual data model for
the new reporting system should
look something like this:
Salary
Manager
Region
Role
118. P A G E 1 1 8
SUMMARY
âș A data model can help increase
Communication with the business
owners and IT staff to and achieve
better results
âș Starting with a robust data model helps
alleviate many of the data quality and
data-related errors commonly seen in
business today.
âș Aligning with Business Motivation and
Drivers is key to success of any
initiative
âș Data Modeling does not have to be
complicated and can be understood
by most businesspeople
âș Data Modeling is at the core of most
Information Management challenges