A Critical Analysis of Data Model Quality and Associated Patterns

This report provides a comprehensive review of data model quality, focusing on the book 'Data Model Patterns' by David Hay. The report begins by defining data modeling and its significance in database development and management. It then delves into Hay's work, discussing his contributions to data modeling, including his development of basic patterns and his emphasis on abstraction. The report highlights the book's structure, which covers various aspects of corporate data, procedures, and activities, and examines its approach to creating pure data models that address practical user demands. The report also touches upon the book's key concepts, such as the unit party and staff as anchors for data models, and its applicability across different industries. Overall, the report emphasizes the intellectual accomplishment of Hay's work in simplifying complex areas and capturing their essence, providing a valuable resource for students interested in data modeling.

Running head: DATA MODEL QUALITY 1
Data model Quality
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A post on my view on “Data Model Patterns”
Data model is currently applied in almost every industry due to the ease of using it in
databases. It is construed as the process that enables one to make sense of his data by defining
and classifying it, developing standard definition or descriptors so that the data can be used by all
information system in an association (Patel, & Patel, 2012). As such it has become a critical
aspect in database development and management. For the purpose of this assignment, I picked a
book on “Data Model Patterns” by Hay David from online library that is one of my favorite
books. I got motivated to share my view on the book in this particular article.
Before we dive into the details of the book, we will first have a glance at the author’s
brief bio. Hay David has pioneered in processing and use of data models while supporting
strategic planning and system design from 1970. He has also developed basic patterns for data
modeling. Moreover, the author is a data management, modeling and semantics speaker that is
recognized internationally. He has also authored “Requirement analysis” from a business
perspective to architectural perspective.
David Hay has done an exceptional job by extracting the essence of each thing so as to
deal with it as an abstraction. Following this great job, a much simpler and powerful data model
that are independent on cosmetic variation has been achieved. According to David, learning the
data modeling techniques is not the same as learning how to use it and apply it (Hay, 2013). The
book provides a foundational work on data modeling, it is structured into twelve chapters.
Each of the book’s chapter draws high level data model from the following perspectives:
the corporate and its world, the corporate’s things, procedures and activities, accounting,

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contract, the laboratory, the process manufacturing material requirement low level convention as
well as documents (Hay, 2013).
The book is, however, biased in creating the purest models with an emphasis of
describing things using abstract terms in order to cover a wide ranging circumstances.
Nevertheless, David Hay has revealed the purest model that considers the practical issues for
addressing the demands of the prospective users of a system.
According to the book, the first anchor for data model refers to the unit party. The second
anchor for any data model, on the other hand refers to the staff that the organizations make and
use (Hay, 2013). One of the most importance of the book is that it has the set of models which
can be readily used which are typically compatible with applications in many industries.
To condense, this article has presented my view on David’s one of the most reputable
works “Data Model Patterns” and it can be noted that the book is more of an intellectual
accomplishment. It has boiled down various areas into their basics and it has capture the basics.

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References
Hay, D. C. (2013). Data model patterns: conventions of thought. Pearson Education. Retrieved
on 30th april 2019 from
<https://pdfs.semanticscholar.org/54e4/9cf87dd9982b4de05b799f2ed180e0d1127a.pdf>
Patel, A., & Patel, J. (2012). Data modeling techniques for data warehouse. International
Journal of Multidisciplinary Research, 2(2), 240-246.