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Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World, 3rd Edition
Let's step back to the year 1978. Sony introduces hip portable music with the Walkman, Illinois Bell Company releases the first mobile phone, Space Invaders kicks off the video game craze, and William Kent writes Data and Reality . We have made amazing progress in the last four decades in terms of portable music, mobile communication, and entertainment, making devices such as the original Sony Walkman and suitcase-sized mobile phones museum pieces today. Yet remarkably, the book Data and Reality is just as relevant to the field of data management today as it was in 1978. Data and Reality gracefully weaves the disciplines of psychology and philosophy with data management to create timeless takeaways on how we perceive and manage information. Although databases and related technology have come a long way since 1978, the process of eliciting business requirements and how we think about information remains constant. This book will provide valuable insights whether you are a 1970s data-processing expert or a modern-day business analyst, data modeler, database administrator, or data architect. This third edition of Data and Reality differs substantially from the first and second editions. Data modeling thought leader Steve Hoberman has updated many of the original examples and references and added his commentary throughout the book, including key points at the end of each chapter. The important takeaways in this book are rich with insight yet presented in a conversational writing style. Here are just a few of the issues this book From Graeme Simsion's
While such fundamental issues remain unrecognized and unanswered, Data and Reality , with its lucid and compelling elucidation of the questions, needs to remain in print. I read the book as a database administrator in 1980, as a researcher in 2002, and just recently as the manuscript for the present edition. On each occasion I found something more, and on each occasion I considered it the most important book I had read on data modeling. It has been on my recommended reading list forever. The first chapter in particular should be mandatory reading for anyone involved in data modeling. In publishing this new edition, Steve Hoberman has not only ensured that one of the key books in the data modeling canon remains in print, but has added his own comments and up-to-date examples, which are likely to be helpful to those who have come to data modeling more recently. Don't do any more data modeling work until you've read it.
While such fundamental issues remain unrecognized and unanswered, Data and Reality , with its lucid and compelling elucidation of the questions, needs to remain in print. I read the book as a database administrator in 1980, as a researcher in 2002, and just recently as the manuscript for the present edition. On each occasion I found something more, and on each occasion I considered it the most important book I had read on data modeling. It has been on my recommended reading list forever. The first chapter in particular should be mandatory reading for anyone involved in data modeling. In publishing this new edition, Steve Hoberman has not only ensured that one of the key books in the data modeling canon remains in print, but has added his own comments and up-to-date examples, which are likely to be helpful to those who have come to data modeling more recently. Don't do any more data modeling work until you've read it.
162 pages, Paperback
First published January 1, 1978
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Displaying 1 - 28 of 28 reviews
September 23, 2013
A classic. I treasure my copy of the 1978 edition. Yeah, it's a database book, and yeah, it's 35 years old. But it overflows with wisdom. An extended quote from the final chapter, entitled "Philosophy":
"This book projects a philosophy that life and reality are at bottom amorphous, disordered, contradictory, inconsistent, non-rational, and non-objective. Science and much of Western philosophy have in the past presented us with the illusion that things are otherwise. Rational views of the universe are idealized models that only approximate reality. The approximations are useful. The models are successful often enough in predicting the behavior of things that they provide a useful foundation for science and technology. But they are ultimately only approximations of reality, and non-unique at that.
This bothers many of us. We don't want to confront the unreality of reality. It frightens, like the shifting ground in an earthquake. We are abruptly left without reference points, without foundations, with nothing to stand on but our imaginations, our ethereal self-awareness. So we shrug it off, shake it away as nonsense, philosophy, fantasy. What good is it? Maybe if we shut our eyes the notion will go away. What do we know about physical entities, about ourselves?"
The 2012 annotations by Steve Hoberman are useful for orienting someone who's not familiar with modern "standard" data modeling practices, primarily relational database ones. They're useful, but I find more compelling the bravery and bewilderment of the original.
"I do not know where we are going, but I do know this—that wherever it is, we shall lose our way." - Praxedes Mateo Sagasta
"This book projects a philosophy that life and reality are at bottom amorphous, disordered, contradictory, inconsistent, non-rational, and non-objective. Science and much of Western philosophy have in the past presented us with the illusion that things are otherwise. Rational views of the universe are idealized models that only approximate reality. The approximations are useful. The models are successful often enough in predicting the behavior of things that they provide a useful foundation for science and technology. But they are ultimately only approximations of reality, and non-unique at that.
This bothers many of us. We don't want to confront the unreality of reality. It frightens, like the shifting ground in an earthquake. We are abruptly left without reference points, without foundations, with nothing to stand on but our imaginations, our ethereal self-awareness. So we shrug it off, shake it away as nonsense, philosophy, fantasy. What good is it? Maybe if we shut our eyes the notion will go away. What do we know about physical entities, about ourselves?"
The 2012 annotations by Steve Hoberman are useful for orienting someone who's not familiar with modern "standard" data modeling practices, primarily relational database ones. They're useful, but I find more compelling the bravery and bewilderment of the original.
"I do not know where we are going, but I do know this—that wherever it is, we shall lose our way." - Praxedes Mateo Sagasta
February 17, 2016
It's long been an axiom that "data" and "information" are not the same thing. Kent shows, at great length and with considerable insight, why "information" and "meaning" are also not necessarily the same thing, and examines the issues involved in ensuring that "meaning" can be unambiguously represented and retrieved.
In this, he is both decades ahead of his time (the book was written in the mid-1970s, yet many of the issues he raises are important for the development of the Semantic Web), and also out of date (many of his concerns in the latter parts of the books are limitations of the IMS database, and other DBMS of the era).
Throughout the book, Kent looks rigorously at many aspects of information which are normally taken for granted, but which actually involve considerable ambiguity - both in the context of data within IT systems and in the real world: for example, in seemingly-obvious concepts like "this" and "the same".
Sometimes, his rigorous view can lead to him tying himself in pedantic knots - the discussion of attributes being a particular example. In addition, his habit of raising issues and not coming to any conclusion (a deliberate choice by the author rather than a lack of clarity) can be wearing: in many places discussions peter out into Kent admitting that he doesn't know how to conclude the argument.
There are some points where this edition's origins as a re-typesetting of typewritten original are a handicap - Kent at times has to jump through linguistic hoops to describe some concept that could be explained far simpler in a diagram.
In the latter third of the book, Kent concerns himself with discussion of a generic conceptual model, which he never really adequately describes the purpose of: it appears to be a meta-model to be used to describe other models, or provide a formal definition of other models. After a couple of chapters of agonising over the limitations of relational & hierarchical models for defining this, he eventually lands on something which is redolent of object modelling (a couple of chapters after a modern reader has long been muttering "surely this can be addressed by object modelling?"). When I belatedly realised what Kent had written and worked on after this book, it's hardly a coincidence.
It's a strange book - on the one hand concerned with perception of reality, on another with how that is represented in models and computer systems, it often veers as much toward philosophy (particularly at the end) as information technology.
All in all, a very worthwhile book, taking an unusual and very perceptive look into the meaning of data.
In this, he is both decades ahead of his time (the book was written in the mid-1970s, yet many of the issues he raises are important for the development of the Semantic Web), and also out of date (many of his concerns in the latter parts of the books are limitations of the IMS database, and other DBMS of the era).
Throughout the book, Kent looks rigorously at many aspects of information which are normally taken for granted, but which actually involve considerable ambiguity - both in the context of data within IT systems and in the real world: for example, in seemingly-obvious concepts like "this" and "the same".
Sometimes, his rigorous view can lead to him tying himself in pedantic knots - the discussion of attributes being a particular example. In addition, his habit of raising issues and not coming to any conclusion (a deliberate choice by the author rather than a lack of clarity) can be wearing: in many places discussions peter out into Kent admitting that he doesn't know how to conclude the argument.
There are some points where this edition's origins as a re-typesetting of typewritten original are a handicap - Kent at times has to jump through linguistic hoops to describe some concept that could be explained far simpler in a diagram.
In the latter third of the book, Kent concerns himself with discussion of a generic conceptual model, which he never really adequately describes the purpose of: it appears to be a meta-model to be used to describe other models, or provide a formal definition of other models. After a couple of chapters of agonising over the limitations of relational & hierarchical models for defining this, he eventually lands on something which is redolent of object modelling (a couple of chapters after a modern reader has long been muttering "surely this can be addressed by object modelling?"). When I belatedly realised what Kent had written and worked on after this book, it's hardly a coincidence.
It's a strange book - on the one hand concerned with perception of reality, on another with how that is represented in models and computer systems, it often veers as much toward philosophy (particularly at the end) as information technology.
All in all, a very worthwhile book, taking an unusual and very perceptive look into the meaning of data.
July 20, 2014
Fris en interessant boek vol gedachten over het waarnemen en beschrijven van de werkelijkheid. Zeer de moeite waard voor wie zich met data bezighoudt. Ja, je mag het van me lenen maar ik wil het wel terug!
July 10, 2020
If you're dealing with databases, you're most probably in one of the three following categories:
- as a developer of database technology, busy with developing the core database engine, or
- as a business developer, administrator, building a solution by using existing database technologies, or
- as a data modeler, eliciting various requirements from the business domain experts to create meaningful conceptual, logical and physical data models, or reverse engineer an existing database-backed business system to create similar models to help other projects, integrations, etc.
This book is for the experienced people whose activities mostly fall into the third category. But make no mistake, this is not a technical cookbook, it's not a how-to book for data modelers. It goes above and beyond vendor-specific, even programming technology specific aspects of data modeling, and takes the reader on a tour of the 'essence' of data modeling related topics.
If you've spent some time thinking on the essential and even meta aspects of data modeling and information architecture, you'll appreciate the exposition of challenges in this book. Similar to other books that do a deep dive on the essential aspects of a topic, the book in a sense is a philosophical one, almost necessarily so. Therefore, readers with a background or serious interest in philosophy, especially analytical philosophy, will probably appreciate the author's message. People who've spent some time thinking about epistemology, ontology (both in the classical philosophical, as well as in the formal-logic & semantic web related sense), and their relationship to structuring and querying information using relational, NoSQL, graph and document databases will particularly enjoy many chapters and timeless, challenging examples.
I'm thankful to Steve Hoberman not only because he's the reason that this book is still in print, but also because he recommended this to me in his Data Modeling Masterclass in Brussels, back in 2018. Moreover, his personal and modern interpretation of the book is more than enough to enrich the reader's understanding of these topics, so much so that one simply forgets this is a database related book written more than 40 years ago!
- as a developer of database technology, busy with developing the core database engine, or
- as a business developer, administrator, building a solution by using existing database technologies, or
- as a data modeler, eliciting various requirements from the business domain experts to create meaningful conceptual, logical and physical data models, or reverse engineer an existing database-backed business system to create similar models to help other projects, integrations, etc.
This book is for the experienced people whose activities mostly fall into the third category. But make no mistake, this is not a technical cookbook, it's not a how-to book for data modelers. It goes above and beyond vendor-specific, even programming technology specific aspects of data modeling, and takes the reader on a tour of the 'essence' of data modeling related topics.
If you've spent some time thinking on the essential and even meta aspects of data modeling and information architecture, you'll appreciate the exposition of challenges in this book. Similar to other books that do a deep dive on the essential aspects of a topic, the book in a sense is a philosophical one, almost necessarily so. Therefore, readers with a background or serious interest in philosophy, especially analytical philosophy, will probably appreciate the author's message. People who've spent some time thinking about epistemology, ontology (both in the classical philosophical, as well as in the formal-logic & semantic web related sense), and their relationship to structuring and querying information using relational, NoSQL, graph and document databases will particularly enjoy many chapters and timeless, challenging examples.
I'm thankful to Steve Hoberman not only because he's the reason that this book is still in print, but also because he recommended this to me in his Data Modeling Masterclass in Brussels, back in 2018. Moreover, his personal and modern interpretation of the book is more than enough to enrich the reader's understanding of these topics, so much so that one simply forgets this is a database related book written more than 40 years ago!
May 20, 2018
tl; dr: reality is subjective, which means that no data model will be objectively correct (e.g., what is an attribute vs. a relationship, what is an entity vs. an attribute). for every business problem we make choices for the data model based on the scope, and which questions we need answered. when integrating multiple systems meant for multiple business problems, we'll have to reconcile the data models with each other. the idea that there no objective correct data model is freeing, though i was hoping to learn more from this book about how to make choices about data model given the specific business scope at hand.
it's very existential. the last chapter is about how we all perceive reality differently because of our view of science / spirituality, and the sapier-whorf hypothesis lol.
it's very existential. the last chapter is about how we all perceive reality differently because of our view of science / spirituality, and the sapier-whorf hypothesis lol.
October 14, 2020
This is less about data works technicalities, and more about philosophies, especially about reality and our undertanding of it. Actually there are more questions raised than questions answered.
Things that Kent concerned more than 40 years ago are still asked by practitioners today: How to define entities, how to name things, is my definition of "user" the same as your definition of "user"...
The things that I will remember most from the book:
- A data model is not reality. It is not the reality of a business, or the actual structure of information in real world. It is merely a tool to comprehend reality.
- However, our tool defines our perception of reality. Avoid thinking "What I can do with this tool" - think broadly about the problems and how you want to solve it.
- A data model is a version of reality. Different people will have their own different reality. While it's impossible to produce an exact representation of reality, it is useful to produce something "close enough" that can be used to communicate with lots of people.
Things that Kent concerned more than 40 years ago are still asked by practitioners today: How to define entities, how to name things, is my definition of "user" the same as your definition of "user"...
The things that I will remember most from the book:
- A data model is not reality. It is not the reality of a business, or the actual structure of information in real world. It is merely a tool to comprehend reality.
- However, our tool defines our perception of reality. Avoid thinking "What I can do with this tool" - think broadly about the problems and how you want to solve it.
- A data model is a version of reality. Different people will have their own different reality. While it's impossible to produce an exact representation of reality, it is useful to produce something "close enough" that can be used to communicate with lots of people.
November 21, 2021
This book is older than I am (though not entirely as the edition I’ve read is from 2012), but still feels like a breathe of fresh air to me, after over a decade in the tech industry and having looked at an endless parade of proposed solutions.
Rather than being about solutions, this text is about making sure you know about problems and where to find them. If the world of data were a forest, this book would tell you what to look out for to survive, rather than selling you a survival kit and sending you off.
It shows its age in some parts (a conclusion that leans a bit heavily on a strong form of the Sapir-Whorf hypothesis), and the newer edition’s comments try to couch and re-contextualize some of the text in a more recent framing with specific examples.
However, the approach it takes is overall great and I’m happy to have found it.
Rather than being about solutions, this text is about making sure you know about problems and where to find them. If the world of data were a forest, this book would tell you what to look out for to survive, rather than selling you a survival kit and sending you off.
It shows its age in some parts (a conclusion that leans a bit heavily on a strong form of the Sapir-Whorf hypothesis), and the newer edition’s comments try to couch and re-contextualize some of the text in a more recent framing with specific examples.
However, the approach it takes is overall great and I’m happy to have found it.
May 18, 2020
Written in 1978, still amazingly relevant (although the worries about record naming and the level of Sapir-Whorf references date it). Confirms my interest in experimenting more with graph databases. Basically: reality's a complicated mess that we automatically parse fuzzily, so explicit and deterministic data modeling can only succeed in limited domains where users have shared perspectives and goals.
Dave, I bet you'd like this one.
Dave, I bet you'd like this one.
November 20, 2019
This is quite unique book. The main promise is the reality is complex (it is full of ambiguity, intermingled concepts, fuzzy boundaries, loosely defined phenomenons, and everything is in flux all the time), so how can we use simple models to capture that? And given that modeling process is itself is an artifact of reality, how can we be so strict and decisive in the way we do modeling.
My main objection to this book is that it uses a very blunt way to illustrate the complexity of reality: list and enumerate every issue you can think of. Which is very fun at first encounter, but becomes torturous boring very quick.
This book is a scavenger hunt for brilliant ideas in a forest of boredom. In the end, I have no clue how much did I miss.
And yes this is a philosophy book, meaning the purpose of it is to provoke thinking, and I'm yet to understand if there any action items to get out of it. Be ready to read through a lot of "water", cause exploring dark waters is the whole purpose of this enterprise.
My main objection to this book is that it uses a very blunt way to illustrate the complexity of reality: list and enumerate every issue you can think of. Which is very fun at first encounter, but becomes torturous boring very quick.
This book is a scavenger hunt for brilliant ideas in a forest of boredom. In the end, I have no clue how much did I miss.
And yes this is a philosophy book, meaning the purpose of it is to provoke thinking, and I'm yet to understand if there any action items to get out of it. Be ready to read through a lot of "water", cause exploring dark waters is the whole purpose of this enterprise.
December 8, 2019
Philosophical. About challenges and limits of modeling.
I picked this up expecting to find ideas applicable to DDD/object-oriented domain modeling. I did.
This book is more than 40 years old now, but because of its philosophical nature, it is still very relevant today, at least in any field where models of real-world are created and processed.
In the end, reality is subjective and amorphous. Any rational model will never represent reality perfectly, and there will always be cases at the edges, where the model does not quite fit. The best we can hope for is to make our models useful (and not perfect) approximations of the real-world.
I picked this up expecting to find ideas applicable to DDD/object-oriented domain modeling. I did.
This book is more than 40 years old now, but because of its philosophical nature, it is still very relevant today, at least in any field where models of real-world are created and processed.
In the end, reality is subjective and amorphous. Any rational model will never represent reality perfectly, and there will always be cases at the edges, where the model does not quite fit. The best we can hope for is to make our models useful (and not perfect) approximations of the real-world.
January 21, 2019
Unique and informative. The book is almost 40 years old, yet still relevant to some aspects of today's data challenges. Although written by a professional DB academic (there is such a thing) and professional, the book does not read like an IT or computer science text. Instead, it reads like Bertrand Russell, or maybe Kant in analytic logic.
The being written scarcely 20 years after the invention of the relational database management system is primarily concerned about the "philosophy" of representing things in reality as content within such a system, basically producing a data model and a logical model. In this capacity, much time is spent on set-analysis, i.e. what does it mean for something to be a member of such-and-such at which level does it make sense to map your entities in, how many/which attributes should one record. When are certain data models appropriate for which use-cases (e.g. like a record model)?
Though, again, the book is written as if the author was a philosopher and not a DBA. Given the increasing relevance of data, this book is still surprisingly relevant, even if a lot of the DBA like work has become commodified. As the author notes, just establishing such a rational information system will take many decades to accomplish within the whole of US enterprise. He was right, the task has still not been completed.
Given the books introspective topic, which amounts to epistemology, it makes a great hiking companion. I'd actually listen to it again. Recommend (for nerds)
The being written scarcely 20 years after the invention of the relational database management system is primarily concerned about the "philosophy" of representing things in reality as content within such a system, basically producing a data model and a logical model. In this capacity, much time is spent on set-analysis, i.e. what does it mean for something to be a member of such-and-such at which level does it make sense to map your entities in, how many/which attributes should one record. When are certain data models appropriate for which use-cases (e.g. like a record model)?
Though, again, the book is written as if the author was a philosopher and not a DBA. Given the increasing relevance of data, this book is still surprisingly relevant, even if a lot of the DBA like work has become commodified. As the author notes, just establishing such a rational information system will take many decades to accomplish within the whole of US enterprise. He was right, the task has still not been completed.
Given the books introspective topic, which amounts to epistemology, it makes a great hiking companion. I'd actually listen to it again. Recommend (for nerds)
May 20, 2020
Useful critique
The original book from the 70s is a solid and useful intro to data modelling and still applicable today. The focus is on the limitations so you probably need to have some basic knowledge of data modelling first. At least at the level of say databases 101 and SQL and/or some practical experiences.
The author emphasises the inherent imprecision of any model due to the complexity and ambiguity of ‘reality’. All good points. And clearly arising from deep practical experience.
The final chapter could be seen as a bit pretentious, though making some good points. I did think though that we’d all heard more than enough about Sapir and Whorf and the multitudinous Eskimo words for snow or the different Hopi conceptualisations of time - and the downward slide into insipid mushy ‘relativism’. Brings back memories. Ah the 70s!!
The original book from the 70s is a solid and useful intro to data modelling and still applicable today. The focus is on the limitations so you probably need to have some basic knowledge of data modelling first. At least at the level of say databases 101 and SQL and/or some practical experiences.
The author emphasises the inherent imprecision of any model due to the complexity and ambiguity of ‘reality’. All good points. And clearly arising from deep practical experience.
The final chapter could be seen as a bit pretentious, though making some good points. I did think though that we’d all heard more than enough about Sapir and Whorf and the multitudinous Eskimo words for snow or the different Hopi conceptualisations of time - and the downward slide into insipid mushy ‘relativism’. Brings back memories. Ah the 70s!!
January 25, 2025
9 chapters on topics of computer science like entities, the nature of an information system, naming, relationships, attributes, types and categories and sets, models, the record model, and philosophy. Without the philosophy chapter, I would have given it only 2 stars as this topic is too dry for me. It lacks practical parts and examples to remember and understand better what the author means by that. It could have have a software exemplification part, but unfortunately it does not. However, the author is really knowledgeable about languages in general as exemplified by the last chapter in which he quotes extensively from Whorf, one of the gods of language science.
August 10, 2020
A hollistic approach on how we perceive reality and our imperfect ways (i shall not say futile) in trying to embed it in our information systems.
The book is more like a memorandum of pitfalls that humans encounter when trying to design data systems.The book does not advocate of a specific approach (e.g tabular vs graph vs network representation) but it does provide solid facts to help us get to the right mindset when designing our data systems.
The book is more like a memorandum of pitfalls that humans encounter when trying to design data systems.The book does not advocate of a specific approach (e.g tabular vs graph vs network representation) but it does provide solid facts to help us get to the right mindset when designing our data systems.
July 3, 2019
I realize that it was written for databases and information systems, but I read it in hope that it could give me some insights into systems of systems architecture. Enjoyed it, but given that I enjoy “general semantics”, Korbyski, etc. that’s no surprise. Yes, it has a philosophical tone, but that’s expected if you’re diving into Ontology.
July 14, 2020
Nice philosophical view on what we're really doing when we're modeling data; a friendly reminder from a playful pedant that you can never really pin "reality" down. I quite enjoyed it... except for the inline comments from Steve Hoberman: he reiterates what William Kent says but waters it down and misses the joy, and reminds us you can find out more by reading his latest book. Thanks, Steve, but no thanks.
November 5, 2017
Imperative introduction to information science with pleasantly simplified instruction.
January 2, 2020
"If you're confused, it just proves you've been paying attention."
Read
April 27, 2020Great book about data modeling.
December 8, 2020
A technical book that’s almost half a century old and still relevant
December 13, 2020
If you're doing any days modeling, this is an absolute must read. Gives a great perspective how any days modeling will ultimately have flaws
March 3, 2023
Increíble lectura sobre la naturaleza de la información, las dificultades de modelarla y los sistemas de representación con los que contamos en las computadoras.
Esta lectura en cada página te romperá la cabeza si eres un desarrollador de software, modelador de base de datos, analista de datos o cualquier otra profesión que tenga que ver con el tratamiento de la información y la representación del mundo real.
El tema o la cuestión que deja más clara y que se repite a través de todo el escrito es que el modelado de datos y la decisión de cómo representar algo es completamente subjetiva y dependerá muchas cosas, entre ellas, el objetivo del modelado. Es por eso que diferentes modelados o abstracciones de la misma cosa del mundo real variarán dependiendo de las personas que lo hagan.
El libro en realidad se va mucho más profundo filosófica y epistemológicamente y te hace cuestionarte cosas como si de verdad las cosas están tan bien definidas como creemos en el mundo real. Esto te hace razonar que si las cosas están tan difusas en el mundo real, con mucha más razón lo estarán en las representaciones que creamos.
Algunos de los puntos sobre la representación de información que toca son:
1. Identidad
2. Igualdad
3. Transformaciones (cambios en el tiempo)
4. Atributos (Aquí me rompió la cabeza una pregunta: ¿Es lo mismo un atributo que una relación?)
5. Entidades
6. Relaciones
7. Nombres y Aliases
8. Registros y mecanismos de almacenaje
Además, la tercera edición que fue publicada en 2012 tien comentarios de Steve Hoberman, que a mi parecer también son bastante valiosos por la experiencia que tiene en modelado de información.
Esta lectura te hace cuestionarte muchas cosas de lo que sabes sobre computación en general, bases de datos, sistemas de representación y de la REALIDAD MISMA. Te hace pensar que hay muchas cosas que damos por sentado pero que en realidad si piensas un poco puedes ver que están tan definidas como lo penssbas.
Creo sinceramente que esta lectura debería ser obligatoria para todos los estudiantes de computación o carreras similares y también para personas relacionadas con el almacenado de datos en cualquiera de sus formas, sobre todo aquellas responsables de definir cómo se guardará la información.
Finalmente, me sorprende cómo desde 1974, año en que se escribió la primera edición, poco han cambiado las cosas y parece que los problemas fundamentales de la representación de información son irresolubles. Agradezco mucho haberme topado con este libro.
Esta lectura en cada página te romperá la cabeza si eres un desarrollador de software, modelador de base de datos, analista de datos o cualquier otra profesión que tenga que ver con el tratamiento de la información y la representación del mundo real.
El tema o la cuestión que deja más clara y que se repite a través de todo el escrito es que el modelado de datos y la decisión de cómo representar algo es completamente subjetiva y dependerá muchas cosas, entre ellas, el objetivo del modelado. Es por eso que diferentes modelados o abstracciones de la misma cosa del mundo real variarán dependiendo de las personas que lo hagan.
El libro en realidad se va mucho más profundo filosófica y epistemológicamente y te hace cuestionarte cosas como si de verdad las cosas están tan bien definidas como creemos en el mundo real. Esto te hace razonar que si las cosas están tan difusas en el mundo real, con mucha más razón lo estarán en las representaciones que creamos.
Algunos de los puntos sobre la representación de información que toca son:
1. Identidad
2. Igualdad
3. Transformaciones (cambios en el tiempo)
4. Atributos (Aquí me rompió la cabeza una pregunta: ¿Es lo mismo un atributo que una relación?)
5. Entidades
6. Relaciones
7. Nombres y Aliases
8. Registros y mecanismos de almacenaje
Además, la tercera edición que fue publicada en 2012 tien comentarios de Steve Hoberman, que a mi parecer también son bastante valiosos por la experiencia que tiene en modelado de información.
Esta lectura te hace cuestionarte muchas cosas de lo que sabes sobre computación en general, bases de datos, sistemas de representación y de la REALIDAD MISMA. Te hace pensar que hay muchas cosas que damos por sentado pero que en realidad si piensas un poco puedes ver que están tan definidas como lo penssbas.
Creo sinceramente que esta lectura debería ser obligatoria para todos los estudiantes de computación o carreras similares y también para personas relacionadas con el almacenado de datos en cualquiera de sus formas, sobre todo aquellas responsables de definir cómo se guardará la información.
Finalmente, me sorprende cómo desde 1974, año en que se escribió la primera edición, poco han cambiado las cosas y parece que los problemas fundamentales de la representación de información son irresolubles. Agradezco mucho haberme topado con este libro.
January 2, 2020
I timeless piece on reflecting about data and how to store and represent it in a way that it's amenable to be handled by a computer system.
The author reaches into fundamental questions to be made about the way we think about data and make models in our minds and how to translate that for a machine representation without losing the meaning of what we're trying to represent.
The questions are still relevant after so many years have passed from the original edition and the inception of those questions in the author's thoughts.
The author reaches into fundamental questions to be made about the way we think about data and make models in our minds and how to translate that for a machine representation without losing the meaning of what we're trying to represent.
The questions are still relevant after so many years have passed from the original edition and the inception of those questions in the author's thoughts.
September 1, 2008
Written some decades ago, this book almost reads as if it came out yesterday. The author pulls apart the simplifications in data modelling that are often swept under the carpet, but are a fundamental part of the realities we are trying to model.
The book loses momentum a little towards the end, and the suggested solution to some of the issues is only partially fleshed out. However, modern Semantic Web researchers will find much that is familiar.
The book loses momentum a little towards the end, and the suggested solution to some of the issues is only partially fleshed out. However, modern Semantic Web researchers will find much that is familiar.
August 23, 2015
Fantastic book about the challenge of modeling the world with computers. It was written decades ago but it might have been written yesterday. The questions it poses have for the most part not been answered to any satisfaction. I also think it manages to foreshadow some of the more recent research about the role of metaphor in language and thought. Highly recommended.
July 3, 2014
A philosophy book that asks many important questions, but answers few of them. I liked it! Everyone should be aware of these points of conflict between data and reality.
December 15, 2015
Amazingly prescient for its time.
September 19, 2016
Do try this book if you're into information modeling one way or the other. For me personally, 2 or 3 chapters had too much details.
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