Critical Writing of Quality Database Design

Mr. Traitet Thepbandansuk Student ID: 20043132 Module: Logical Database Design

Assessment 2: Individual Critical Writing

Module: Logical Database Design

Assessment: Assessment 2

Title: Individual Critical Writing

Course: Enterprise Systems Professional

Author: Mr. Traitet Thepbandansuk

Student number: 20043132

Total pages: 10 pages (Including cover page)

Word count: 2,218 (Excluding references)

Submission deadline: Monday 23rd January 2012

Course: Enterprise Systems Professional Page 1 of 10 Assessment 2


1. Introduction

A database design is a process of transformation a high-level or conceptual data model in business into
a logical data model that will used toimplement the physical database and application (Kendall 2011). A
quality database designenables software development able to be implemented, maintained and
modified in a cost-effective way (Hoxmeier 1997). This paper includes the critique of the processes
undertaken in the module,the first assignment, and my experience, whichis divided into three parts.

1. The meaning of a “Quality Database Design” and proposed criteria for measuring
2. The contributionof Use Case, ER, and EERmodels to a “Quality Database Design”
3. Contrast the concepts of “correct” and “quality” in terms of a database design

2. The meaning of a “Quality Database Design” and proposed criteria for measuring

There are several aspects of a quality database design based on a perspective of each evaluator. Data
modelling with a quality process doesn’t exactly lead to a usable database in production (Redman
1995). Therefore, a quality database design should approach not only a modelling process,semantic
quality, and data quality e.g. data redundancy, consistency and integrity, but should also be
concernedabout customer satisfaction in terms of completion requirements(Hoxmeier 1997). For this
reason, this papercombines the criteria from the Framework for Assessing Database Quality stated by
Hoxmeier (1997), and TQM (Total Quality Management) principle,which is widely used to improve
product quality. The objectives are to clarify the meaning of a quality design, and to suggest four criteria
for measuringa quality database design as follows; Database Process Quality, Database Data Quality,
Database Semantic Quality,and Customer Satisfaction.

2.1 Database Process Quality

There are several steps involved in the process of turning an initial problem domain into a database
design. The quality design process addressed the quality-related issues in each phase enables to
deliver a database with high quality (Blakeslee and Rumble 2003). For example, a good result of
data analysing requirements is to clarify assumptions with clients in order to capture all business
requirements (D'Orazio and Happel 1996). The use case diagram is a UML tool, which allows a
development team to use it as a discussion tool to capture comprehensive views of an overall
system. Furthermore, the data modelling stage is generally represented by an Entity-Relationship
(ER) diagram. A good ER model providing a list of data items and relationships should emphasize
rules of interrelationships between data items to improve data accuracy and consistency (Teorey
2009).

In short, a good quality process should provide three main steps as follows; capturing business
requirements as a conceptual data model, translating it into a logical data model focusing on entity-
relationship modelling tools, such as ER and Class diagram, and reducing data redundancies by
using normalization methodology (Houldcroft 2011).



2.2 Database Data Quality

Poor data quality can damage the efficiency of organizations, and businesses (Batini and
Scannapieco 2006), and inaccurate data also make users lose confidence in the database
(Redman 1995). Therefore, a quality database model should provide data accuracy as the first
priority. To be correct, data value must be the right value, and represented in a consistent and
unambiguous form. However, there are several issues related to data accuracy that database
designers should emphasize. Firstly, a good database design should define null rules to prevent the
problems of missing value. For example, the BIRTH_DATE attribute cannot be NULL because
every person has a date of birth. Furthermore, two-value problem, such as “St Louis” and “Saint
Louis”, can be inaccurate data because both are the same city recorded with different values.
Therefore, these data can create an opportunity for inaccurate usage(Olson 2003).

In summary, a database design, which can improve data accuracy, is one of the necessary
indicators to measure database quality. The following figure illustrates the further examples of
improving data quality reflected in the assignment 1.

Figure 2.2:More examples of improving data quality



2.3 DatabaseSemantic Quality

As can be seen in the figure 2.3, a semantic database design should provide information that
represents a high proportionate match between problem areas and the solution domain, and should
reduce the ambiguous dimensions of a conceptual database design(Hoxmeier 1997). Using
quantitative techniques, e.g.use case, ER and EER diagrams allow the results described in a visual
format, and measured in a meaningful way (Hoxmeier1997). Regarding with my experiences, we
started by using use case diagrams to scope the problem areas, and used the ER and EER
diagrams to communicate with development teams about information flowing and relationships
between entities. As a result, we could design databases matching with all requirements, and
develop applications in short time.

To sum up, semantic quality database design is data modelling that designs structure for a
database fitting as good as possible some relevant world (Bakker 2006).

Figure 2.3: Mapping the solution to the problem domain (Hoxmeier 1997).



2.4 Customer Satisfaction

Total Quality Management (TQM) approaches to improve quality standards and customer
satisfaction (Carroll 1995).A good database design can be also measured by customer satisfaction.
Regarding the concept of TQM, a customer doesn’t mean only an external customer, but it also
means internal customers who are the next processes after a database is designed,especially
DBAs and developers. They have to use a database design to create a physical database, and
develop an application. Regarding with my experience, there are several ways to improve their
satisfaction. Firstly, a database design should be completed before developing because source
code depends on a database. It might take much time, if a database is redesigned. Secondly, a
quality database design should be flexible. For example, if a multi-valued attribute, such as
telephone number, is fixed only for three columns, it may not be flexible once a customer wants to
record more than three numbers.

As a result, database designers should be concerned about future possibility of data, and design a
completed database before deliver to the next process in order to obtain satisfaction from both
internal and external customers.The following diagram illustrates the concepts of TQM and internal
customer reflected in my experience.

Figure 2.4: Applying TQM principles



3. The contribution of Use Case, ER, and EER models to a “Quality Database Design”

3.1 Use Case Model

Although a use case diagramis not a database design tool, and cannot directly contribute to a
quality database, we found that use case modeling helped to capture business requirements in
order to design a good database in several ways. Firstly, the use case diagram is an initial tool that
describes what a system does, and reflects the view of what users want (Kendal 2011). Therefore,
we could use the use cases to determine candidate entities, objects, classes, and relationships for
designing ER and EER diagrams. For instance, in the figure 3.1, Agency Staff, Customer, Property,
Appointment and Promotionwill beentities and classes used in ER and EER diagrams. Secondly,
system analysts can use a use case diagram anduse case description to communicate with
database designers and developersduring explaining scope of work. Similarly, analysts can use
themto discuss initially with clients to confirm requirements.

To sum up, the use case diagram is a powerful UML tool to help a development team understand
how a system work in general. It indirectly contributes to a quality database design, such as
supporting ER and EER diagrams, and helping development team fulfill requirementsin order to
delivera project with high satisfaction. However, theuse case diagram is just only a tool in the
analysis stage. Development team needs to use more tools and methodologies to design a quality
database in the design stage.

Figure 3.1: Use case diagram



3.2 ER Model

Data modeling by using ER diagram is the first step in the database design that links between real-
world objects and the database modeling details (Rob, Coronel, and Crockett 2008). After we used
the ER diagram to design database for the first assignment, we found some advantagesthatcan
contribute to a database design. Firstly, the ER diagram holds information about attributes and
constraints on entities. As can be seen in figure 3.2, theER diagram defines primary and foreign
key constraints for all entities in order to enhance database data quality. These constraints enforce
users to input data following referential integrity rules. For example, room details cannot be
inserted into the ROOM entity, if theirPROPERTY_NO is not found in the PROPERTY entity.
Secondly, in terms of database semantic quality, ER modeling can reduce ambiguity of
communication (Connolly and Begg 2010) because theER diagram is a visual format and
measured in a meaningful way (Hoxmeier1997). For instance, cardinalities, such as 0:*, 1:1, onour
ER diagram helped everyone understand about relationships between entities in the same
direction. Moreover, the ER diagram has entities, attributes, relationships, cardinalities to hold
necessary requirements matching to customer’s needs.Thus it can enhance both data and
semantic quality.

However, only an ER diagram cannot providea high quality data modelbecause it is often
insufficient to represent requirements of complex applications, especially development based on
the object-oriented concept. To enhance design quality, data modeling needs more methodology
to support additional semantic concepts, such as specialization/generalization (class and
subclass), aggregation and composition provided by an EER diagram (Connolly and Begg2010).

Figure 3.2: ER Diagram



3.3 EER (Enhanced Entity-Relationship) Model

A high-quality database design should be flexible so that it can be adapted to meet the demands
of changing data and information requirements (Rob, Coronel, and Crockett 2008). As theEER
diagram combines modelling concepts between anER diagram and UML class diagram, there are
several benefits for creatinga flexible data model over using only an ER diagram. Firstly, as can be
seen in figure 3.3, the EER diagram includes details of classes and theirsubclasses e.g. Student
and Tourist classesare subclasses’ Customer class. As a result, using an EER diagram is more
effectivein creating relational database with constraints than using only an ER diagram. Moreover,
the constraints, such as {Optional,and}, also enhance data quality of database design. Secondly,
aggregations and compositions improve a database design in terms of data integrity quality. For
instance, composition with the black diamond symbol means that when a parent entity instance is
deleted, all child entity instances are automatically deleted.Finally, EER diagram can enhance
quality of an overall system because it designs a data model for object-oriented conceptwhich
aims to support four software engineering goals namely, abstraction, cohesion, low coupling and
modularity (Polovina 2011).

Figure 3.3: EER Diagram



4. Contrast the concepts of “correct” and “quality” in terms of a Database Design

These days, a concept of correct and quality in terms of database design is a controversial issue
leading to much debate. There is a widespread perception that a good database design should follow a
data modelling process in order to support all business requirements; however, other takes a different
stance. The discussion here will attempt to contrast both sides.

There are three main basic principles of a correct database design as follows; to minimize redundancy
because it wastes resources, to develop set of relationships that capture all the relevant data efficiently
and effectively, and to capture rules to ensure that correct data are stored -maintain data integrity
(Riordan 2005).

Although a correct database design can support all requirements, eliminate data redundancy, and
improve data integrity, it is insufficient in the real world. There are some issues that should be brought
up. First of all, a correct design may be forced to fit into new business requirements, whereas a quality
database design is flexible to accept changes or new requirements in the future (Olson 2003). Secondly,
the concepts of Total Quality Management (TQM) are maintaining quality and increasing customer
satisfaction. For example, a customer needs to enhance the speed during retrieving data from a
database. A quality database design may be an incorrect design becauseit needs to perform
denormalization instead of normalization. Although normalization is a design technique to remove
redundancies from tables, and avoid costly, denormalization into a single table can dramatically
improve the speed, and many data warehousing and data mining systems also use this technique
(Burleson 2010).

To summarize, it is true that the concept of correct in terms of a database design brings some
advantages, but the quality outweighs the correct concept. Therefore, a development team should
design a database to be flexible, and be concerned about customer satisfaction.

5. Conclusion

In conclusion, the “Correct Database Design” is processes of producing a data model that can support
customer requirements, improve data accuracy, and reduce duplicate data, whereas the “Quality
Database Design” is more concerned about database flexibility and customer satisfaction.

Although a “Quality Database Design” cannot exactly be evaluated so that software is developed and
done iterative process to fulfil customer requirements, that design can initially be measured by four
suggested criteria; Database Process Quality, Database Data Quality, Database Semantic Quality and
Customer Satisfaction.

Furthermore, the use case model contributes indirectly to a quality design in terms of capturing
business requirements, and theER model can improve database process quality, data quality, and
semantic quality. However, the ER modelis insufficient to represent of complex applications, and needs
the EER model including both the ER and object-oriented conceptsto enhance semantic quality.



REFERENCES

1. BAKKER, J.A. (2006). Semantic Data Modelling. [online]. Last accessed 21 January 2012 at:
http://www.jhterbekke.net/SemanticDataModeling.html.
2. BATINI, Carlo and SCANNAPIECO, Monica (2006). Data Quality: Concepts, methodologies and
techniques.[online]. Springer Berlin Heidelberg, New York. Book from Springer last accessed 19 January
2012 at:http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-
540-33172-8.
3. BLAKESLEE, Dorothy M. and RUMBLE, John Jr. (2003). The essentials of a database quality process.
[online]. Data Science Journal, 2, 35-46. Article from J-STAGE last accessed 19 January 2011 at:
http://www.jstage.jst.go.jp/article/dsj/2/0/35/_pdf.
4. BURLESON CONSULTING (2010). Oracle Data structure denormalization. [online]. Last accessed 20
January 2012 at: http://www.dba-oracle.com/art_9i_denormal.htm.
5. CARROLL, Jennie (1995). The application of total quality management to software development. [online].
Information Technology & People, 8(4), 35 – 47. Article from Emerald last accessed 20 January 2012 at:
http://www.emeraldinsight.com/journals.htm?articleid=883451&show=pdf.
6. CONNOLLY, Thomas M. and BEGG, Carolyn E. (2010). Database systems: A practical approach to design,
implementation, and management. 5th ed., United States of America, Addison Wesley.
7. D'ORAZIO, Renzo and HAPPEL, Gunter (1996). Practical data modelling for database design: Database
and data modelling. Australia, John Wiley & Sons.
8. HOULDCROFT, Alan (2011). Logical database development notes and exercises. Sheffield, Sheffield
Hallam University.
9. HOXMEIER, John A. (1997). A framework for assessing database quality. [online]. Last accessed 18
January 2012 at: http://osm7.cs.byu.edu/ER97/workshop4/jh.html.
10. KENDAL, Kenneth E. and KENDALL, Julie E. (2011). System analysis and design. 8th ed., United Kingdom,
Pearson Education.
11. OLSON, Jack E. (2003). Data Quality: The accuracy dimension.[online]. United States of America, Morgan
Kaufmann. Book from Google eBook last accessed 18 January 2012 at:
http://books.google.co.uk/books?id=x8ahL57VOtcC&printsec=frontcover.
12. POLOVINA, Simon (2011). Object oriented concepts lecture note: Software engineering goals.Sheffield,
Sheffield Hallam University.
13. REDMAN, T.C. (1995). Improve data quality for competitive advantage. [online]. Sloan Management
Review, 36(2), 99-107. Article from HEC Montreal last accessed 18 January 2012 at:
http://zonecours.hec.ca/documents/E2007-1-1161870.redman_1995.pdf.
14. RIORDAN, Rebecca M. (2005). Designing effective database systems. Massachusetts, Pearson Education.
15. ROB, Peter, CORONEL and Carlos, CROCKETT (2008). Database systems: design, implementation &
management. United Kingdom, Tom Rennie.
16. TEOREY, Toby J. (2009). Database design: Know it all. Burlington, Morgan Kaufmann Publishers.


Critical Writing of Quality Database Design

More Related Content

What's hot (20)

Similar to Critical Writing of Quality Database Design (20)

More from Traitet Thepbandansuk (20)

Recently uploaded (20)

Critical Writing of Quality Database Design