Data Modelling is NOT just for RDBMS's

AN ENTERPRISE ARCHITECTS WHITE PAPER
INFORMATION IS AT THE HEART OF
ALL ARCHITECTURE DISCIPLINES
...AND THERE’S MORE TO DATA MODELLING
THAN YOU THOUGHT
CHRISTOPHER BRADLEY
CHIEF INFORMATION ARCHITECT &
ENTERPRISE SERVICES DIRECTOR

ENTERPRISE ARCHITECTS WHITE PAPER
2 | ENTERPRISE ARCHITECTS ©2014

CONTENTS DATA MODELLING IS A CRITICAL TECHNIQUE AND AT THE HEART
OF ALL ARCHITECTURE DISCIPLINES 4
DATA MODELLING INTRODUCTION 6
BACKGROUND & HISTORY 8
DIFFERENT TYPES OF MODELS FOR DIFFERENT PURPOSES AND
AUDIENCES 10
DATA MODELLING FOR DBMS DEVELOPMENT 12
DATA MODELLING INCORRECTLY TAUGHT AT UNIVERSITY 16
BUT THIS IS WRONG? SO WHAT NEEDS TO CHANGE? 17
MODELLING FOR THE “NEW” TECHNOLOGIES 18
DEMONSTRATING BENEFITS 30
THE GREATEST CHANGE REQUIRED 32
WHAT NEEDS TO STAY THE SAME? 35
CONCLUSION 36
ABOUT THE AUTHOR 38
ABOUT ENTERPRISE ARCHITECTS 39
ENTERPRISE ARCHITECTS ©2014 | 3

DATA MODELLING IS A CRITICAL
TECHNIQUE AND AT THE HEART OF
ALL ARCHITECTURE DISCIPLINES
Many years ago people believed the World was flat and if they sailed over the horizon,
then they would fall off the edge. They also believed that the E arth was at the centre of the
heavens, and that all other planets orbited around it.
But they were wrong.
People who believe Data Modelling is just for DBMS
design are just as misinformed. Data Modelling,
particularly Conceptual Data Modelling is an absolutely
critical technique and is at the heart of all architecture
disciplines. Here’s why:
Since data has to be understood to be managed, it stands
to reason that gaining agreement on the meaning and
definition of concepts will be a key component. That is
precisely what a data model provides.
But just what do I mean when I state that Data
Modelling is at the heart of all architecture
disciplines?
At its heart, the Data Model provides the unifying
language, lingua franca, the common vocabulary
upon which everything else is based. Other modelling
techniques within the complimentary architecture
disciplines will interact with each other, forming a
supportive; cross-checked, integrated and validated set
of techniques. It’s not just (sometime it’s never) about
technical DBMS design.
So to illustrate the case with a few simple
examples, we see in:
The Business Architecture Domain: A Project Charter
documents the rationale, the objectives, the business
scope, and measures the success of the project. It uses
the language of a high level data model to describe the
business concepts.
The Process Architecture Domain: A Workflow Model
describes the sequence of steps carried out by the
actors involved in the process.
The Application & Systems Architecture Domain: A Use
Case describes how an actor completes a step in the
process, by interacting with a system to obtain a service.
A Service Specification describes some form of business
service that is initiated to complete a business event

FIGURE 1: Data Modelling is
at the heart of all architecture
disciplines
The Information Architecture Domain: A Data Model
depicts the critical data items, and the attributes or
facts about them. This is important data that the
organisation wishes to know or store information on,
and is the stuff that the processes and systems act on.
Every type of model references the entities of
significance in the conceptual data model, showing why
conceptual data modelling is such a vital technique.
Getting agreement on the language and definition of
the data concepts always must always occur first; once
established detail about processes can be added:
»» To begin we discover the Nouns: i.e. the items
of interest to the organisation , e.g. “Product”
“Customer” “Location”
»» Next we discover “Verb – Noun” pairs: These are
activities that must be performed, such as process
and sub-process, in order for the organisation to
operate, e.g. “Design Product” “Ship Order”
»» Lastly we discover “Actor – Verb – Noun “
combinations: These form the Use Cases or steps
within a business process, , e.g. “Lead Architect
Designs New Product”.
At this high level, we are seeking to gain an
understanding and agreement on terms and vocabulary
for the data concepts. We do not want to get bogged
down in the level of excruciating detail that a detailed
logical model would take us into.
Thus, high level conceptual models (often called Business
Data Models) are the appropriate vehicle to use here.
It can be loosely argued that they provide some of the
features of an “ontology” i.e. business concepts and their
relationships, although a Conceptual Data Model with its
metadata extensions provides much more.

DATA MODELLING
INTRODUCTION
The problem for many Data Architects is that “Data
Modelling” has, in far too many companies received a
lot of bad press. Have you heard any of these?
»» “It just gets in the way”
»» “It takes too much time”
»» “What’s the point of it”
»» “It’s not relevant in today’s systems landscape”
»» “I don’t need to do modelling, the package has it all”
Yet when Data Modelling first came onto the radar in
the mid 1970’s the potential was enormous: We were
told we would realise benefits of:
»» “a single consistent definition of data”
»» “master data records of reference”
»» “reduced development time”
»» “improved data quality”
»» “impact analysis”
...to name but a few.
Do organisations today want to reap these benefits? You bet, it’s a no-brainer.
So then, why is it that now, here we are, 30+ years on and we see in many organisations that the benefits of Data
Modelling still need to be “sold” and in others the big benefits simply fail to be delivered? What’s happened? What
needs to change?
AS WITH MOST THINGS A LOOK BACK INTO THE PAST IS A GOOD PLACE TO START.

BACKGROUND &
HISTORY
1950’s – 70’s:
Information Technology (at that time often called
Automated Data Processing (ADP)) was starting to
enter the mainstream world of commerce. During this
period we saw the introduction of the first database
management systems such as DL1, IMS, IDMS and
TOTAL. Who can remember a DBMS that could be
implemented entirely on tapes? **
At that time the cost of disc storage was exceptionally
high, and the notion of exchangeable disc packs was just
coming into the data centre. The concept of “database”
operations came into play and the early mentions of
“corporate central databases” appeared.
** It was IMS HISAM if you really want to know.
1970 – 1990:
Data was “discovered”. Early mentions of managing
data “as an asset” were seen and the concepts of
Data Requirements Analysis and Data Modelling were
introduced.
1990 – 2000:
The “Enterprise” became flavour of the decade. We saw
Enterprise Data Management Coordination, Enterprise
Data Integration, Enterprise Data Stewardship and
Enterprise Data Use. An important change began to
happen in this period, there was a dawning realisation
that “technology” alone wasn’t the answer to many
of the information issues, and we started to see Data
Governance being talked about seriously
2000 and beyond:
Data Quality, Data as a Service, Data Security &
Compliance, Data Virtualisation, Services Oriented
Architecture (SOA), governance and alignment with
the business were (and still are) the data management
challenges of this period.
All of this needs to be undertaken in these rapidly
changing times when we have a “new” view of
information: Web 2.0, Blogs, Mash-ups, Data
Virtualisation. It seems anyone can create data! At
the same time we have a greater dependence on
Looking back into the history of data
management; we see a number of key eras.

“packaged” or COTS (Commercial off the shelf)
applications such as the major ERPs. There is also more
and more use of SOA, XML, business intelligence and
less reliance on traditional “bespoke” development.
Notice I sneaked in “mash-ups” (or web application
hybrid) there? See the Wiki article Mashup_(web_
application_hybrid) for more on mash-ups. There are
many powerful facilities available now that enable you
to create your own mash-ups. Make no mistake, these
are now becoming the new “Shadow IT” of this decade.
Remember the home grown departmental Excel macros
of the 90’s and onwards that became “critical” to parts
of the business? Now mash-ups are doing the same
thing. But just who is looking at the data definitions,
the data standards, applicability etc.? Certainly not
the data management group – because frequently
they don’t even know that these functions are being
built in departmental silos, and anyway the “data
team” is pigeon holed as being only involved in DBMS
development.
So that leads us on to examine the belief that many
people still have (too many unfortunately) that Data
Modelling is only for DBMS development.
SO WHY IS THAT?
Firstly we’ll look at Data Modelling for use in DBMS
development.

DIFFERENT TYPES OF MODELS
FOR DIFFERENT PURPOSES AND
AUDIENCES
In its early days Data Modelling was mostly (almost exclusively) what we now call Logical
and/or Physical Data Modelling and it was primarily aimed at DB MS development.
However, there are many different levels of “Data Models” that can be developed, and they each have a different
purpose and audience.
From Figure 2, we see there are many different levels of “Data Models”. The higher up the pyramid we go, the more
“communication” focused the models are. Whereas the further down the pyramid we go the more “implementation
focused the models are. Frequently, a higher level model is created with the sole purpose of improving
communication and understanding.
FIGURE 2: Levels of Data Models

FIGURE 3: Purpose of data
model levels ENTERPRISE DATA MODEL
Documents the very high level business data objects and
definitions. Enterprise wide scope to provide a strategic view
of enterprise data.
CONCEPTUAL DATA MODEL
(SUBJECT AREA)
The business key, attributes and definitions of business data
objects. Also shows the relationship between business data
objects. Broader scope than LDM and may cover a subject
area (also known as subject area model).
LOGICAL DATA MODEL
(APPLICATION)
Documents the business key, attributes and definitions of
business data objects. It also shows the relationship between
business data objects. Frequently is within the scope of a
defined project.
PHYSICAL DATA MODEL
Technical design e.g. tables, columns, keys, foreign keys and
other constraints to be implemented in the data base or XSD.
May be generated from a logical data model.
This model is within the scope of a defined project.

DATA MODELLING FOR
DBMS DEVELOPMENT
As outlined previously, in its early year’s Data Modelling was primarily aimed at DBMS development and there were
two main techniques for developing a model; we’ll have a look at these in a moment.
Just to illustrate this we can look at 4 typical roles that may be considered as “customers” of the Data Modelling output:
THE ENTERPRISE DATA CUSTOMER
This might be at Director or CxO level. The accuracy
of data is critical, they are reports users, and the data
“products” that data professionals produce are key to
serving the needs of this type of user.
THE DATA ARCHITECT
This person knows the business and its rules. He/she
manages knowledge about the data and defines the
conceptual direction and requirements for capturing data.
THE DBA
This role is production oriented, manages data storage
and the performance of databases. They also plan and
manage data movement strategies, and play a major part
in data architecture by working with architects to help
optimise and implement their designs in databases.
THE DEVELOPER DBA
This role works closely with the development teams
and is focused on DBMS development. They frequently
move and transform data, often writing scripts and ETL to
accomplish this.
Data Models (more accurately the metadata) were and are seen as the glue, or the lingua franca, for integrating IT roles through
the DBMS development lifecycle. All of the roles listed above depend on metadata from at least one of the other positions.

What then are the steps for developing a DBMS and utilising Data Models? Firstly a word of warning; this could be the subject
of a huge paper in its own right, but I’ll try and summarise it simply here.
There are two “main” approaches to creating DBMS’s from models: One is the “top down” or “to-be” approach and the other is
termed the “bottom-up” or “as-is” approach.
TOP DOWN (TO-BE) APPROACH
STEP 1:
STEP 4:
When speaking with business representatives, discover
Verify the logical data model with the stakeholders.
and document the business requirements, before
Walk a number of major business use cases through
agreeing on a high-level scope. The output is typically
and refine the model. Apply the technical design rules
some form of Business Requirements Document (BRD).
with knowledge of the technical environment that you
This will give an understanding at a high level, of the
are going to implement the solution on, use known
concept where the data is used by business processes,
volumetric and performance criteria and create a first
and vice versa.
cut physical data model. Remember the same logical
model could be implemented in different ways upon
STEP 2:
varying technology platforms.
Create a more detailed business requirement document
STEP 5:
with subscriber data requirements, business process and
business rules.
Generate the Data Definition Language (DDL) from the
physical model. Refine the physical design with DBA
STEP 3:
support and implement the DBMS using the refined
physical model.
Understand and document the business keys, attributes
and definitions from business subject matter experts.
From this create and continually refine a logical data
model. Determine what the master entities are and
what is common to other business areas.
This top down approach has an advantage that the “New” or “ To-Be” business and data requirements are the main
priority. In the early days there were not many “existing systems” to consider, a good job because the approach doesn’t
take into account any of the hidden nuances and rules that may be deep down within the existing systems.

BOTTOM UP (AS-IS) APPROACH
The primary purpose of the Bottom Up (or As-Is) Approach is to create a model of an existing system into which
the new requirements can be added. Frequently, the bottom-up approach is used because a model of the current
system simply doesn’t exist. Often because it has evolved and/or the original design staff have retired, died, or
moved on and the documentation has not been kept up to date.
The main steps in the bottom-up approach are:
STEP 1:
Reverse engineer the database of file schema from the
system that is already implemented. From this you will
have the database catalog, table, column, index names
etc. Of course these will all be in “tech” language without
any business definitions.
STEP 2:
Profile the real data by browsing and analysing the data
from the tables. Scan through the ETLs to find out any
hidden relationships and constraints. Modern data
profiling tools are invaluable here as they will allow you to
gain real insight to the data, way beyond simply trying to
understand from the column names. You did know that
SpareField6 really has the alternative delivery location?
The bottom up approach is great for capturing those hidden “gotchas” that are tucked away inside the current system.
However it doesn’t give any serious attention to new requirements.
STEP 3:
Find out foreign key relationships between tables from
IT subject matter experts, and verify the findings. The
typical output here is a refined physical model.
STEP 4:
Document the meanings of columns and tables from IT
subject matter experts.
STEP 5:
Try to understand the business meaning of probable
attributes and entities that may be candidates for logical
data model. From here the result is a “near logical”
model.
Thus, a third way is a hybrid of these two approaches that is frequently called the “Middle Out” Approach. The
Middle Out Approach employs the best parts of the Top-Down and Bottom-Up Approaches. This is the approach
I favour when designing a new model, which is likely to have a better chance of ultimately being used for a
technology solution.

THE MIDDLE OUT APPROACH
Employs the best parts of the Top-Down and Bottom-Up
Approaches. This is the approach I favour when designing
a new model, which is likely to have a better chance of
ultimately being used for a technology solution.
Image: Flickr, Wwarby - Red Arrows

DATA MODELLING INCORRECTLY
TAUGHT AT UNIVERSITY
As part of my DAMA-I education brief (and to be honest as a way of giving something
back to the community) I am frequently asked to speak not just at conferences but with
academic institutions. Over the past 10 years or so I have been taken aback at what I have
observed regarding the way in which Data Modelling is portrayed on courses at many
Universities in the UK and USA (and I suspect in other places too).
Here are a few snippets I have pulled from 5 separate
universities recently regarding data modelling on the
Computer Science Bachelors & Masters courses:
»» “The purpose of a Data model is to design a relational
database system”
»» “An ER Model is used to specify design and document
Database design”
»» “A Data model is a pictorial representation of the
structure of a relational database system”
»» “… it is a description of the objects represented by a
computer system together with their properties and
relationships”
»» “ER Modelling is a Database design method”
At one of these I dug deeper and examined several
of the course assignments. One assignment asked
students to prepare a model to represent an office
environment and in part of the detailed description
within the assignment brief it mentioned the “Rolodex”
and “IBM Selectric” that were on the desks in this office.
Now, I’m not talking here of reading an assignment paper
set for a course in 1975, this was one I saw in 2013!!
Now with all of these uses of Data Models that I have
described so far, the history of Data Modelling, the way
it’s still being taught in some Universities, and judging
from much of the literature from the Data Modelling
tool vendors themselves; it not surprising that many
people are left with the impression that data modelling
is just for DBMS’s.

BUT THIS IS WRONG? SO WHAT
NEEDS TO CHANGE?
The use and benefit of Data modelling is considerably greater than its current “one trick
pony” press would suggest.
To make Data Modelling relevant for today’s systems
landscape we must show that it’s relevant for the “new”
technologies such as:
»» ERP packages.
»» SOA & XML.
»» Business Intelligence.
»» Data Lineage.
»» Data Virtualisation.
Without forgetting that an appropriate level Data Model
is an awesome communication tool so it can for used
for communicating with the business.
See also “Data Modelling For The Business – A Handbook
for aligning the business with IT using high-level Data
Models”; Technics Publishing; ISBN 978-0-9771400-7-7.
We also need to break away from the “you must read
my detailed Data Model” mentality and make the
information available in a format users can readily
understand. For example this means that Data
Architects need to recognise the different motivations of
their users and re-purpose the model for the audience:
Don’t show a business user a Data Model!
Information should be updated instantaneously, and we
must make it easy for users to give feedback, after all
you’ll achieve common definitions quicker that way.
We need to recognise the real world commercial climate
that we’re working in and break away from arcane
academic arguments about notations methodologies
and the like. If we want to have Data Modelling
play a real part in our business then it’s up to us to
demonstrate and communicate the genuine benefits
that can be realised. Remember, Data Modelling isn’t
a belief system, just because you “get it” don’t assume
that the next person does.

MODELLING FOR THE
“NEW” TECHNOLOGIES
I feel I must make a confession here. The technologies are not really all that new!
It’s just that “traditionally” Data Modelling has not been
seen as being relevant to these areas. To break out
of this “modelling is a one trick pony” view we need
to show how and why Data Modelling IS relevant for
today’s varied IT landscape. Therefore we must show
that it’s relevant for the “new(er)” technologies such as:
1. ERP packages.
2. SOA & XML.
3. Business Intelligence.
4. Data Lineage.
5. Data Virtualisation.
6. Communicating with the business.
1. ERP PACKAGES
As Data Architects, when faced with projects that
are embarking upon the introduction of a major ERP
package, have you ever heard the cry:
“We don’t need a Data Model – the package has it all”?
But, does it?
Is data part of your business requirement? Of course
it is. So just how do you know whether the package
meets your overall business data requirements? You did
assess the data component when doing your fitness for
purposes evaluation didn’t you? A Data Model will assist
in both package configuration and fitness for purpose
evaluation.
How can you assess that the ERP package has
compatible data structures, definitions and meanings
as your legacy systems? Again a good Data Model will
assist this.

What about data integration, legacy data take on and
master data integration – how can these readily be
accomplished? You guessed it – a Data Model can help
here too.
The critics say that modelling isn’t needed for ERP
packages. But that’s because they are wedded to the
old-world view that modelling is only used for DBMS
development. It’s not.
In this case, when we are implementing ERP systems, the
model will NOT be required to generate a DBMS from,
however for all of the other aspects described above it
IS invaluable.
So what’s’ the problem? Why can’t we just point our
favourite Data Modelling tool at the underlying DBMS of
the package? Simply put, for the most part the problem
is that Database System Catalog does not hold useful
metadata.
Several well-known ERP systems do not hold any
Primary Key (PK) or Foreign Key (FK) constraints in the
Database itself. It’s only within their application layer
that this knowledge is held. It is within the proprietary
ERP Data Dictionary where anything resembling a
‘Logical View’ of the data and the definitions are held.
FIGURE 4: Part of an ERP reverse
engineered directly from the DBMS

What we really need is to be able to get the ERP
metadata into a useful format similar to that shown in
Figure 5 below.
How can we do that? Well there isn’t space in this
article to go into the detail, and much of it varies from
ERP to ERP. However with SAP, there is a metadata
extraction facility independently available called SAPHIR.
Additionally, you can also validate a model created from
SAPHIR by examining key screen items such as in the
example illustrated below in Figure 6.
Summary: Why develop Data Models for package
implementation
So why do we need to bother undertaking Data
Modelling when implementing an ERP system?
1. For requirements gathering. If your business data
is part of your requirement, you need to model them.
2. For a fit for purpose evaluation. Surely you must
have evaluated the suitability of the package before
deciding to implement it?
FIGURE 5: Useful model from an
ERP
3. For gap analysis: Even if you are told “it’s a done
deal – we are going with package X”, the Data Model
will give you rich insight to gaps in key areas of
functionality. I have used this many times with clients
when implementing major well known packages
to help spot areas where a work round, or manual
implementation will be required.
4. For configuration. Using models as a communication
vehicle to demonstrate use case is invaluable. From
these the many options in the ERP system can be
examined and then configured with confidence.
5. For legacy data migration and take on.
6. For master data alignment. The ERP may have
its own master data sets. You can use the model
to ensure correct alignment of these with your
corporate master data initiative. Don’t fall into the
trap of letting the tail wag the dog!
7. Fundamentally, this is the key one. It’s all about
ensuring that your ERP data can integrate within your
overall Information Architecture
FIGURE 6: Validating an ERP
model from transaction screens

2. SOA AND XML
Fundamentally, SOA is built upon a message based set
of interactions, i.e. all interaction between components
is through messages. These are generally XML
messages, so it is true to say that XML is at the core of
SOA.
But there is a potential problem.
XML is a hierarchical structure (just like in the good old
days of IMS & DL1), but the real world of data is not.
Let’s illustrate this with a real world example – a book.
Looking at Figure 7, we see that this book is entitled
“Data Modeling for the Business”. When we look at this
example we see data such as Title, Author(s), ISBN, Price,
Publisher, Amazon URL and so on.
I don’t intend to give a detailed exposition on the
subject of SOA; however, it’s worth reminding ourselves
of the fundamental components in the architecture.
The Bus in SOA is a “conceptual” construct, which helps
to get away from point to point thinking. An approach
for integrating applications via “a bus” is by using
Message Oriented Middleware (MOM).
A Message Broker is a dispatcher of messages and
comes in many varieties. The broker operates upon a
queue of messages within the routing table.
Adapters are where the different technology worlds are
translated, e.g. UNIX, Windows, OS/390 and so on.
FIGURE 7: Book example

Looking at the authors, (myself, Steve & Donna) there
is also some information (on the back cover) relating to
each of us.
We can develop a Data Model to represent this “real
world” data and show it in an Entity Relational format.
Typically these ER models can represent real world data
pretty accurately.
Figure 8 shows an example ER model for the “book
authoring” data subject area.
A few of the business assertions that this Data Model
makes are that:
A. A book can be written (authored) by at least one &
possibly several writers (in this case, me, Steve and
Donna).
B. A writer may be the author of many books (e.g. Steve
has also written “Data Modeling Made Simple”).
C. Thus Book <> Writer is a many to many relationship.
However, the intersection entity is a real world
concept; it’s the “Book Authorship” entity and this is
shown in Figure 8.
Now, when we want to use data in this model within
an XML based system we have to remember that XML
messages are hierarchic; that is a child entity can only
have one parent entity; whereas an entity relationship
(ER) model allows a child entity to have several parent
entities. Thus we need to do something to turn
the ER model representation into a hierarchic XML
representation. To accomplish this we need to decide
whether to make “Book” the parent of Book Authorship
or to choose “Writer” to be the parent.
In Figure 9, the resultant XML model has been created
after choosing Book as the parent.
Whilst simplistic (for the sake of the example), the XML
model in Figure 9 now represents the XML schema we’re
going to use. Within our SOA based system, we may
have a transaction which utilises an XML message called
“Book Details”. Figure 10 below shows how an XML
message has been created from the XML schema, and is
utilised (in the message queue) in our SOA solution.
So clearly, Data Modelling IS a key component required
in a SOA implementation.
It’s somewhat ironic that this “new” SOA concept and the representation of data in a hierarchic form (i.e. in XML
messages), draws heavily on the approaches we had to employ when designing a database schema for IM S and DL1
which were hierarchic DBMS’s!

FIGURE 8: Book example ER
model
FIGURE 9: Book XML model
FIGURE 10: Book details XML
model

3. BUSINESS INTELLIGENCE
When looking at Business Intelligence and Data Warehouses, we are trying to ensure that the data utilised by the
business for their queries and reports is reliable.
In order to accomplish this, not only do we need to manage the data that the business utilises, but also the
metadata. We all know by now that much of this metadata is contained within the data models.
So, what are the main reasons for managing this model metadata?
1. Reduce Cost: In addition to all the other points
below, the goal here is to reduce the overall cost of
managing a significant part of the IT infrastructure.
Managing metadata helps automate processes,
reduce costly mistakes of creating redundant/non-conformant
data, and reduce the length of time to
change systems according to business needs.
2. Higher Data Quality: Without proper management,
the same type of data may be managed differently in
the places it is used and degrade its quality/accuracy.
3. Simplified Integration: If data is understood and
standardised, it reduces the need for complex and
expensive coding and scripting to transform and
massage data during integration.
4. Asset Inventory: Managing the knowledge about
where data lives and what you store is critical for
eliminating redundant creation.
5. Reporting: Creating a standard definition of data
types and making it easy for the enterprise to find,
will reduce cost in application development (e.g.
time to research and create new objects) as well as
facilitate a general understanding of the enterprise’s
data.
6. Regulatory Compliance: Without metadata
management, you are not complying with
regulations. Bottom line: An audit trail of data,
starting with its whereabouts, is critical to complying
with government mandates.

The top 5 benefits
from managing this
model metadata for
reporting are:
#5 DATA STRUCTURE QUALITY.
Models ensure that the business design of data
architecture is appropriately mapped to the logical
design, providing comprehensive documentation on
both sides.
#4 DATA CONSISTENCY.
By having standardised nomenclature for all data –
including domains, sizing, and documentation formats
– the risk of data redundancy or misalignment is greatly
reduced.
#3 DATA ADVOCACY.
Models help to emphasise the critical nature of data within
the organisation, indicating direction of data strategy and
tying data architecture to overall enterprise architecture
plans, and ultimately to the business’s objectives.
#2 DATA REUSE.
Models, and encapsulation of the metadata
underpinning data structures, ensure that data is
easily identified and is leveraged correctly in the first
place, speeding incremental tasks through reuse
and minimising the accidental building of redundant
structures to manage the same content.
#1 DATA KNOWLEDGE.
Models, combined with an efficient modelling practice,
enable the effective communication of metadata
throughout an organisation, and ensure all stakeholders
are in agreement on the most fundamental requirement:
the data.

ER Models vs. Dimensional Models for Reporting
A lot has been written previously about the appropriateness of ER vs Dimensional Models for BI and Data
Warehousing. To dispel any myths it’s worth looking at the key features of each type of model:
FEATURES OF A DIMENSIONAL MODEL
»» “Star Schema” (or snowflake or even star flake)
»» Optimised for reporting
»» Business entities are de-normalised
»» More data redundancy to support faster query
performance
»» Relationships between business entities are implicit
(it’s evident that a product has a brand and
manufacturer, but the nature of the relationship
between these entities is not immediately obvious
»» Loosely coupled to the business model – changes to
the business model can often be accommodated via
graceful changes without invalidating existing data or
applications.
FEATURES OF AN ER MODEL
»» Optimised for transactional processing (arrival of new
data)
»» Normalised – typically in 3rd (or 5th normal form)
»» Designed for low redundancy of data
»» Relationships between business entities are
explicit (e.g. Product determines brand determines
manufacturer)
»» Tightly coupled to current business model

Don’t forget Data Lineage – it’s applicable to many aspects, and now with regulatory compliance requirements in
many sectors this is also a statutory need.
In BI and DW applications, mappings and transformations determine how each field in the Dimensional Model is
derived. The derivations could actually drive the ETL process. In lineage, like BI the metadata is vital!
Big Data Trap: Using the metadata to help understand and document Data Lineage (as well as to help with business
data understanding, data glossaries and so on) is one of the areas which companies rush into. This “me too”
attitude towards big data can be damaging if companies don’t tread incredibly carefully. After all, if you haven’t got
your “little & medium” data strategy correct, how can you hope to succeed in the big data space?
4. DATA LINEAGE
The knowledge of how data is transformed is itself
valuable intellectual property that should be retained
within a business, and very importantly it is absolutely
necessary for compliance with the Basel II Accord and
Sarbanes-Oxley Act (SOX): SOX requires that lineage &
transformation of financial data is recorded as it flows
through business systems.
There are two key aspects of Data Lineage
TRANSFORMATIONS:
»» What has been done to the data?
BUSINESS PROCESSES:
»» Which business processes can be applied to the
data?
»» What type of actions do those processes perform
(Create, Read, Update, Delete)?
»» Audit Trail – who has supplied, accessed, updated,
approved and deleted the data and when?
»» Which processes have acted on the data?
So where do I need Data Lineage?
For the design of ETL processes, the creation of
Dimensional Models, the transforming data to XML
(typically from ER) and for workflow design.
What is the problem that lineage helps to address?
Fundamentally we need to be able to help business
users to answer questions or concerns raised such as:
»» That figure doesn’t look right! Where does it come
from?
»» How can we prove to the auditor that financial data
has been handled correctly?
Not only do we need to help our primary customers
(the business folks), but we also need to be able to help
IT staff to answer questions such as:
»» I need to integrate the data supplied from your system
with the data in my system.
»» How can I understand where your data has come
from and what it means?
And finally, we need to be able to help systems to
answer questions such as:
»» When a piece of source data is updated, which items
in the Data Warehouse will need to be recalculated?
So why does Data Lineage matter?
We aim to have an increased understanding of where
data comes from and how it is used, which will lead to
increased confidence in the accuracy of data

5. DATA VIRTUALISATION
But what is going to be presented to the applications?
We’ve got all sorts of different data formats, rules,
characteristics and so on in the source data. So what
are we going to show in our nice new uniform view of
the data that is presented to the applications?
It’s the Data Model that is absolutely the language, the
key which unlocks the potential of Data Virtualisation.
The Data Model informs the federation layer of the DV
toolset, and it is against the definitions & structures of
the Data Model that the consuming applications access
the data.
You can almost imagine Data Virtualisation as being
“views on steroids”.
One of the great new technologies to emerge recently
is Data Virtualisation. Most of us will be familiar with
Storage Virtualisation and even Server Virtualisation.
The purpose of virtualisation in the IT world is to mask
complexity, and present a virtual representation of the
thing as if it were a real instance itself.
So with Data Virtualisation, data can be federated from
a very wide variety of heterogeneous environments and
data storage systems, but presented to an application as
if it were a real SQL table, XML message, Web service,
SOAP call etc.
Figure 11 illustrates a typical data virtualisation
architecture.
FIGURE 11: Typical Data
Virtualisation Architecture

6. COMMUNICATING WITH THE BUSINESS
In a Conceptual Data Model, the business key, attributes
and definitions of major business data objects are
developed. It also shows the relationship between
major business data objects. It is used to communicate
with the business, to give an overview of the main
entities, super types, attributes, and relationships. It will
contain lots of ‘Many to Many’ and multiple meaning
relationships. All of this is addressed in the more
detailed logical data model, after there is agreement on
scope and definitions from these high level models.
Fundamentally, these high level models have
different perspectives and levels of detail for
different uses.
Finally, Data Modelling can play a very useful role in
helping to communicate with the business.
As described earlier in this paper, Data Models can be
produced at different levels (Enterprise, Conceptual,
Logical, Physical) and are for different audiences. At
the higher levels a model is a phenomenal tool for
getting across ideas, concepts and gaining a good
understanding of the language and meaning of the
major data concepts in the business.
At the highest level, an Enterprise Data Model
documents the very high level business data objects
and definitions. Its scope is Enterprise wide and is there
to provide a strategic view of Enterprise data. The
Enterprise Data Model is there to get across big picture,
high level concepts.

DEMONSTRATING BENEFITS
As I mentioned earlier, we constantly need to demonstrate the benefits accruing from data
modelling. Nobody owes us a living, and no matter how impor tant WE believe the place
of modelling to be, it is beholdant upon us to demonstrate (and sell) the benefits within our
organisations.
So just how can you gain traction, budget and Executive buy-in? Here are a few tips:
1. Be visible about the program:
»» Identify key decision-makers in your organisation
and update them on your project and its value to
the organisation.
»» Focus on the data that is crucial to the business
first! Publish that and get buy in before moving on
(e.g. start small with a core set of data).
2. Monitor the progress of your project and show its
value.
3. Define deliverables, goals and key performance
indicators (KPIs).
4. Start small. Focus on core data that is highly visible
in the organisation. Don’t try to “boil the ocean”
initially.
5. Track and Promote progress that is made.
6. Measure metrics where possible:
»» “Hard data” is easy (for example # data elements,
#end users, money saved, etc.)
»» “Softer data” is important as well (data quality,
improved decision-making, etc.). Anecdotal
examples help with business/executive users e.g.
“Did you realise we were using the wrong calculation
for Total Revenue?” (based on data definitions)
Remember, soft skills are becoming critically important for Information Professionals, and whilst you
might not like it, the hard facts are that part of YOUR job nowadays IS marketing.

BE VISIBLE ABOUT THE PROGRAM.
MONITOR THE PROGRESS OF YOUR PROJECT AND
Image: Flickr, Sebastiaan ter Berg
SHOW ITS VALUE.
DEFINE DELIVERABLES, GOALS AND KEY
PERFORMANCE INDICATORS (KPIS).
START SMALL.
TRACK AND PROMOTE PROGRESS THAT IS MADE.
MEASURE METRICS WHERE POSSIBLE.

THE GREATEST CHANGE REQUIRED
As Information Professionals, we need to break away from the view “you must read my
detailed Data Model” mentality and make the appropriate model information available in a
format users can readily understand.
This for example means that Data Architects need to recognise the different motivations of their users, and re-purpose
the information they present to be suitable for the audience: Don’t show a business user a Data Model!
Information should be updated instantaneously, and we must make it easy for users to give feedback, after all you
will achieve common definitions much quicker that way.
We need to recognise the real world commercial climate that we’re working in and break away from arcane
academic arguments about notations methodologies and the like. If we want to have Data Modelling play a real
part in our business then it’s up to us to demonstrate and communicate the benefits that are realised.
Remember, Data Modelling isn’t a belief system, just because you “get it” don’t assume that the next
person does.

5. Be aware of the differences in behaviour & motivations of
different types of users, for example a DBA is typically:
»»Cautious.
»»Analytical.
»» Structured.
»»Doesn’t like to talk.
»» “Just let me code!”
However a Data Architect is:
»»Analytical.
»» Structured.
»» Passionate.
»» “Big Picture” focused
»» Likes to Talk.
»» “Let me tell you about my data model!”
And a Business Executive is:
»» Results-Oriented.
»» “Big Picture” focused.
»»Has little time.
»» “How is this going to help me?”
»» “I don’t care about your data model.”
»» “I don’t have time.”
SO WHAT CAN WE DO?
1. Provide information to users in their “Language”:
»» Repurpose information into various tools: BI, ETL,
DDL, etc.
»» Publish to the web.
»» Exploit collaboration tools / SharePoint / Wiki
and so on. What about a Company Information
Management Twitter channel?
»» Business users like Excel, Word, Web tools, so make
the relevant data available to them in these formats.
2. Document Metadata:
»»Data in context (by Organisation, Project, etc.)
»»Data with definitions.
3. Provide the Right Amount of Information:
»» Don’t overwhelm with too much information. For
business users, terms and definitions might be enough.
»» Cater to your audience. Don’t show DDL to a business
user or business definitions to a DBA.
4. Market, Market, Market!
»» Provide visibility to your project.
»» Talk to teams in the organisation that are looking
for assistance.
»» Provide short-term results with a subset of
information, and then move on.
As Information professionals we’ve got to get these softer skills baked into ourselves and our colleagues.
Some of the key things as a profession we can do are to:
»» Remember, nobody owes us a living, so we
must constantly demonstrate benefits. As data
professionals we constantly need to fight for their
existence.
»» Examine professional certification (CDMP / BCS etc.).
This shows we are serious about our profession.
»» Develop interpersonal skills.
»» Avoid methodology wars & notation bigots. Please
don’t air discussions about Barker vs IE vs UML
class diagrams in front of business users. Yes, sadly
enough I have seen this done!

Image: Flickr, Mark Sebastian

WHAT NEEDS TO STAY THE SAME?
Having highlighted the areas that need to change in order to make modelling more
relevant to our business colleagues, and the information environments of today, are there
any things that should stay the same?
Yes indeed.
We must keep the disciplines and best practices that have existed in the modelling community for many years.
These can be categorised into 3 major areas as follows:
1. MODELLING RIGOUR
Development of Conceptual, Logical and Physical Data
models with good lineage and object re-use.
Structures created in the most appropriate normal form
(typically 3rd normal form); good and consistent data
definitions, for all components of the data model.
2. STANDARDS & GOVERNANCE
These cover standards for both development and usage
of information models, including aspects of data quality.
Data Governance including ownership, stewardship and
operational control of the data.
3. OBJECT REUSE VIA A COMMON REPOSITORY
Not only used for data modelling, the metadata that
is captured whilst developing Conceptual, Logical
and Physical Data models is of immense use for
many aspects of the business. Interestingly, several
organisations are now beginning to use this metadata as
the basis of their Business Data Dictionaries.
The key here is holding the metadata in a common,
repository and reusing the objects where appropriate.

CONCLUSION
Throughout this paper we have illustrated that
data is at the heart of all architecture disciplines.
We have seen that Data Models can be produced at different levels and for different purposes and audiences.
We have examined many aspects of Data Modelling, starting with its history, its use in DBMS development, the way
it is taught in some Universities and firmly refuting the criticism that it is only appropriate for DBMS development.
However as data professionals, it’s up to us to make the biggest change necessary to make it appropriate to the
technologies and business environments of today.
We need to grasp the nettle and engage and communicate effectively within our businesses.

SO AS CAPTAIN PICARD SAID:
MR DATA - MAKE IT SO!

ABOUT THE AUTHOR
Chris is the Global Chief Information Architect and UK Enterprise Services Director of
Enterprise Architects, an International strategy & architecture professional services firm,
providing strategic architecture delivery and support services globally.
CHRIS BRADLEY
FOR MORE
DETAILS
CONTACT
With 32 years experience in the Information Management field, Chris works with
leading organisations including Total, Barclays, RBS, GSK, Disney, BP, Statoil, Riyad
Bank & Aramco in Data Governance, Information Management Strategy, Data Quality
& Master Data Management, Metadata Management and Business Intelligence.
He is a Director of DAMA- I, holds the CDMP Master certification, a Fellow of the
Chartered Institute of Management Consulting (now IC) member of the MPO, and
SME Director of the DM Board.
As a columnist and frequent contributor to industry publications, Chris is a recognised
thought-leader in Information Management. He leads an experts channel on the
influential BeyeNETWORK, is a regular speaker at major international conferences,
and is the co-author of “Data Modelling For The Business – A Handbook for aligning
the business with IT using high-level data models”. He also blogs frequently on
Information Management (and motorsport).
CHRIS BRADLEY
Chief Information Architect & Enterprise Services Director
Chris.Bradley@enterprisearchitects.com

ABOUT ENTERPRISE ARCHITECTS
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Our strength is more than just world-class practice in
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