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Il tradimento dei numeri
Chiunque si occupi di raccolta e analisi dei dati lo sa bene, o almeno dovrebbe: le informazioni che non conosciamo possono essere altrettanto importanti di quelle che abbiamo a disposizione. Ha senso affermare che un prodotto è efficace perché ha solo recensioni favorevoli? Ed è davvero possibile prevedere il buon esito di un trattamento medico sulla base di un campione che, per quanto ampio, non corrisponde alla totalità dei casi? Considerazioni analoghe si possono applicare alla nostra vita di tutti i giorni: perché invidiare una persona per i successi messi in mostra sui social network, quando i suoi fallimenti ci vengono opportunamente nascosti? In un'epoca dominata dai big data, conviene prestare maggiore attenzione al rovescio della medaglia, ovvero ai dati che sappiamo di non avere o - peggio ancora - che crediamo erroneamente di possedere. Simili alla materia oscura che elude le rilevazioni dei cosmologi, i dark data influenzano ogni campo di studio e di lavoro, dalla ricerca medica all'industria, dalle politiche pubbliche e sociali al settore finanziario, e sono molto più numerosi di quanto pensiamo. Sottostimarli è dunque un grave rischio, che può portare a costruire modelli fuorvianti e inesatti, con conseguenze talvolta catastrofiche. Al contrario, smascherare questi numeri «traditori» e imparare a gestire i problemi che essi provocano può portare vantaggi nella nostra sfera professionale e in quella personale. I dark data ci insegnano, infatti, a capovolgere il modo di considerare l'analisi dei dati, facendoci comprendere più a fondo l'universo dell'informazione e ad affrontare l'imprevedibile in maniera più consapevole e ponderata.
377 pages, Kindle Edition
First published February 18, 2020
93 people are currently reading
777 people want to read
About the author
David J. Hand
46 books59 followersDavid J. Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, and Chief Scientific Advisor to Winton Capital Management. He is a Fellow of the British Academy, and a recipient of the Guy Medal of the Royal Statistical Society. He has served (twice) as President of the Royal Statistical Society, and is on the Board of the UK Statistics Authority. He has published 300 scientific papers and 25 books: his next book, The Improbability Principle, is due out in February 2014. He has broad research interests in areas including classification, data mining, anomaly detection, and the foundations of statistics. His applications interests include psychology, physics, and the retail credit industry - he and his research group won the 2012 Credit Collections and Risk Award for Contributions to the Credit Industry. He was made OBE for services to research and innovation in 2013.
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Displaying 1 - 29 of 29 reviews
September 21, 2020
Hands down, one of the most useful layman texts I’ve read all year, perhaps the past 3 years. “Dark Data”, written by British statistician David Hand, discusses one of the least well-understood/talked about subject matters in the data-analysis process flow, dealing with missing data. Why this is the case is not exactly clear, but it probably comes down to two reasons: One, the rapidity of work expected from data scientists, not only in executing their scripts, writing code, implementing algorithms, but overall in gaining the valuable “insights” for the company. Thus, when tasked to quickly build a predictive model that could serve as an engine for some scoring process, the focus is often on the “traditional” machine learning development: the test-train-validation loop, and then the subsequent report or implement into production deliverables. Two, the complexity of the topic of missingness (more on that later).
What is often less focused on either by the team, or the client, in these fast-paced projects, is on the nature of the data itself. That is, how was it produced, what does that production imply about the nature of the data- it’s fidelity, and of course, what information is missing from this data. Classical statistical processes of yesteryear would have been very methodical along these lines, but of course statistical consulting was even more bespoke 2 or 3 decades past than it was today , and thus, afforded a more leisurely pace towards it’s analyst. Not so, in modern data science projects, which are often measured in weeks between significant deliverables.
So the “missingness” of data is often pigeonholed into some process to be dumped into a (often blackbox) algorithm, or simple business logics to quickly sieve out offending columns or rows (if it’s tabular/rectangular) etc. Thus, the art of really taking time to understand your data is quickly becoming lost to the current spate of data science analysts and practitioners, in a field that is itself increasingly becoming more abstract, and is emphasizing the “common sense” human intuitions that underlie the kind of analysis described in this text.
Besides the expediency, and with respect to the complexity of missigness, there are actually many different ways one’s data can suffer from missingness. In fact, that’s why this book is a great layman intro to the topic, as the author has outlined a nice, compact system to think about the nature of missing data, and provide starting threads of analysis one can engage in to drill down on that missingness to inform the eventual ameliorating solution.
The author’s system takes the form of a simple typology, a list of 15 different kinds of missing data, which collectively form the definition of “Dark Data”. The types range from self-selection, knowing what’s missing, knowing what’s not missing, missing data that may be time-dependent, to purposefully missing (gaming or adversarial behavior), and many others. Each type in the list gets at least one section of commentary, and an illustrative example. The first few on the list (knowing when something is or is not missing or not knowing it), provides the perfunctory commentary on the traditional typology of missingness that one may have encountered in statistical texts, missing at random (MAR), missing not at random (MNAR) etc., and this material can easily be gotten freely on the net if not encountered in class. However, from there, the real value of the text becomes more apparent, with in-depth discussions from a range of historical case-studies from history (polling during FDRs reelection campaign), another may discuss issues with experimentation, specifically the randomized control trial (RCT), like differential dropout rates, and what that may imply for analysis. In a way, a student inherits the “folklore” of motivating examples from statistics, both well-known and little-known.
Overall, I really liked the book. It isn’t a textbook, it is an introduction, and as mentioned previously, a well-needed one. If there was a weak section in the book, I think it would have to be the chapter dealing with fraud and dark data, I just didn't feel it fit as naturally with the other chapter. I think this should be on the reading list for every introductory and intermediate data scientist, and even old-hands would get some use (I think) from a guided retrospection on these topics and ways to deal with them. Highly recommended.
What is often less focused on either by the team, or the client, in these fast-paced projects, is on the nature of the data itself. That is, how was it produced, what does that production imply about the nature of the data- it’s fidelity, and of course, what information is missing from this data. Classical statistical processes of yesteryear would have been very methodical along these lines, but of course statistical consulting was even more bespoke 2 or 3 decades past than it was today , and thus, afforded a more leisurely pace towards it’s analyst. Not so, in modern data science projects, which are often measured in weeks between significant deliverables.
So the “missingness” of data is often pigeonholed into some process to be dumped into a (often blackbox) algorithm, or simple business logics to quickly sieve out offending columns or rows (if it’s tabular/rectangular) etc. Thus, the art of really taking time to understand your data is quickly becoming lost to the current spate of data science analysts and practitioners, in a field that is itself increasingly becoming more abstract, and is emphasizing the “common sense” human intuitions that underlie the kind of analysis described in this text.
Besides the expediency, and with respect to the complexity of missigness, there are actually many different ways one’s data can suffer from missingness. In fact, that’s why this book is a great layman intro to the topic, as the author has outlined a nice, compact system to think about the nature of missing data, and provide starting threads of analysis one can engage in to drill down on that missingness to inform the eventual ameliorating solution.
The author’s system takes the form of a simple typology, a list of 15 different kinds of missing data, which collectively form the definition of “Dark Data”. The types range from self-selection, knowing what’s missing, knowing what’s not missing, missing data that may be time-dependent, to purposefully missing (gaming or adversarial behavior), and many others. Each type in the list gets at least one section of commentary, and an illustrative example. The first few on the list (knowing when something is or is not missing or not knowing it), provides the perfunctory commentary on the traditional typology of missingness that one may have encountered in statistical texts, missing at random (MAR), missing not at random (MNAR) etc., and this material can easily be gotten freely on the net if not encountered in class. However, from there, the real value of the text becomes more apparent, with in-depth discussions from a range of historical case-studies from history (polling during FDRs reelection campaign), another may discuss issues with experimentation, specifically the randomized control trial (RCT), like differential dropout rates, and what that may imply for analysis. In a way, a student inherits the “folklore” of motivating examples from statistics, both well-known and little-known.
Overall, I really liked the book. It isn’t a textbook, it is an introduction, and as mentioned previously, a well-needed one. If there was a weak section in the book, I think it would have to be the chapter dealing with fraud and dark data, I just didn't feel it fit as naturally with the other chapter. I think this should be on the reading list for every introductory and intermediate data scientist, and even old-hands would get some use (I think) from a guided retrospection on these topics and ways to deal with them. Highly recommended.
November 26, 2021
Pop-stats book about missing data: how to detect it, how to reason about it, how to compensate for it (single and multiple imputation, the EM algorithm, bootstrapping) and ways we use it to our advantage ("blinding" study participants, anonymisation, Bayesian priors). A clear presentation of the material, made accessible to non-statisticians; the book at times suffers from trying to cover too much.
October 19, 2022
I bought this book expecting to read about non-standard sources of data and new ways to use existing data. But it only repeats the classic problems of censoring and sample bias, which can be found in any introductory statistics textbook (often using the same textbook examples), and which anyone with basic data literacy would have had to deal with ad nauseam. I don't see why this book was necessary.
August 20, 2020
A lot of important data is lost, not collected or simply not available. This book tries to shine a light on the fact that a lot of important data is dark - sometimes for very good reasons.
The book is rather sprawling and lacks focus. By trying to shoehorn subjects as disparate as Sokal's hoax and the Piltdown man into the same text as underrepresented social groups it ends up very disjointed.
The book is rather sprawling and lacks focus. By trying to shoehorn subjects as disparate as Sokal's hoax and the Piltdown man into the same text as underrepresented social groups it ends up very disjointed.
March 15, 2023
I was expecting to embark on a fascinating journey to the depths of the dark web or something equally mysterious but unfortunately I was mislead by the title. The author goes through, in detail and with numerous examples, how we live in constant darkness because we never have all the data about any one thing. Dark data is the data that's missing and there are different types.
The first third of the book was fairly interesting with different types of dark data being explained and illustrated with examples. However, the rest was very focused on how to identify and deal with dark data when conducting scientific research. It wasn't quite textbook style but not quite popular science either. It was certainly more than I was interested in knowing though and I struggled through the final third by having it playing in the background.
The book was narrated very well by the author himself but I would stay clear of this one unless research and understanding the pitfalls of data are interesting or relevant to you.
The first third of the book was fairly interesting with different types of dark data being explained and illustrated with examples. However, the rest was very focused on how to identify and deal with dark data when conducting scientific research. It wasn't quite textbook style but not quite popular science either. It was certainly more than I was interested in knowing though and I struggled through the final third by having it playing in the background.
The book was narrated very well by the author himself but I would stay clear of this one unless research and understanding the pitfalls of data are interesting or relevant to you.
February 2, 2022
A bunch of stories and examples about data.
I find it risky and misleading to rename concepts that are already defined in academia.
I find it risky and misleading to rename concepts that are already defined in academia.
December 11, 2024
Dark Data: Why What You Don't Know Matters wasn't really for me. I think that this book would be good for someone that doesn't think too much about statistics, wants to learn more about how studies are conducted, or has never had a college class in statistics and wants to learn more about the topic.
According to the author, Dark Data is similar to dark matter (and is named after it). We don't know either of their origins, but we do know that both affect the data that we are observing.
I found a lot of the information contained in this book to not be fresh. While the main points are true and important, I felt it was nothing that I haven't already learned about during my undergrad.
The main emphasis of the book is to never take the data you observe at face value and always be thoughtful, honest, and careful when collecting data. Dig deeper to see if you can find more reasons why what you are observing is happening.
If more of X leads to less of Y, Z might be in the background causing X to increase and thus actually more of Z leads to less of Y. If we were to only look at X and Y, we would have completely missed the well of information being given to us by Z.
This is all fine and good. Good enough for me to recommend to someone wanting to learn more about statistics. The reason I gave this book 3 stars is because it is very long. There are a lot of examples, which is good. However, those examples are very exhaustive and feel like you are going down a rabbit trail. There were many times during this book where the author would wrap up an example, bring it back to the original point, and I would think to myself "oh, we're still on this subject?"
If you can power through the many examples and are interested in learning more about statistics, then I would recommend checking this book out.
According to the author, Dark Data is similar to dark matter (and is named after it). We don't know either of their origins, but we do know that both affect the data that we are observing.
I found a lot of the information contained in this book to not be fresh. While the main points are true and important, I felt it was nothing that I haven't already learned about during my undergrad.
The main emphasis of the book is to never take the data you observe at face value and always be thoughtful, honest, and careful when collecting data. Dig deeper to see if you can find more reasons why what you are observing is happening.
If more of X leads to less of Y, Z might be in the background causing X to increase and thus actually more of Z leads to less of Y. If we were to only look at X and Y, we would have completely missed the well of information being given to us by Z.
This is all fine and good. Good enough for me to recommend to someone wanting to learn more about statistics. The reason I gave this book 3 stars is because it is very long. There are a lot of examples, which is good. However, those examples are very exhaustive and feel like you are going down a rabbit trail. There were many times during this book where the author would wrap up an example, bring it back to the original point, and I would think to myself "oh, we're still on this subject?"
If you can power through the many examples and are interested in learning more about statistics, then I would recommend checking this book out.
February 1, 2021
This book is basically about statistical and logical errors caused by the absence of data. Very reminiscent of Donald Rumsfeld's famous quote about known unknowns and unknown unknowns. Overall, worth reading.
July 19, 2020
Too trivial and very easy to get lost in the not well-structured examples. Now giving up.
January 5, 2026
When I was finished university, it was fashionable for business leaders to say "In God we trust, everyone else bring data", to show that they had advanced data-driven decision making processes. Later in life, a new popular saying was "if you torture the data enough, it will tell you anything". Both these quotes reveal changing attitudes towards reliance on data.
This book uses as its analogy the man searching under a streetlamp for the keys he dropped. And when asked if that was where he dropped the keys he says "no, but this is the only place I can see". And that illustrates quite nicely the problem that if we only look at the data we can see, we might be missing other very important data.
The book is full of examples of different kinds of dark data, and how they lead to wrong conclusions. Some examples that stuck in my head:
- A medical study found that people with asthma were more likely to recover from pneumonia than people who did not have asthma. The dark data here was that people with asthma were typically rushed immediately to the ICU, which is why they received much better medical care.
- Two studies on Medicine A and Medicine B showed a much higher success rate for A. However, it turns out that if you breakdown by age, B is actually much more effective across all age groups. How is that possible? Because the success rate of both medicines is much lower among older people, and the study for Medicine B comprised many more older people.
Another important lesson is that when you get an expected result, don't immediately think you have made a new discovery. It is much more likely that there is something wrong with your data. There are 14 types of Dark Data in total, of course I don't remember all of them. Here are a couple:
- Known Missing Data:This includes instances where it's explicitly known that data is missing, such as missing values in a survey or data points that were not recorded.
- Unknown Missing Data: This category involves situations where it's not known what data is missing.For example, the number of dissatisfied customers who don't bother complaining.
- Data Selection Bias: This occurs when the data collected only represents a subset of the population or phenomena, leading to skewed results.
- Missing Variables: Important characteristics or features of the data might be missing, leading to incomplete analysis.
- Changing Definitions: When the definition of a variable changes over time, it can make comparisons between datasets problematic.
- Summaries of Data: While summaries provide a concise overview, they can hide important details and variations within the data.
- Errors: When someone has entered the data wrongly
- Counterfactuals: This refers to data that would have been collected under different circumstances, but is not available.For example, what would have happened if a different treatment had been used in a clinical trial?
- Unstructured Data: Data in formats like emails, PDFs, or social media posts can be difficult to analyze without proper structuring and tools.
- Feedback and Gaming: This occurs when data is manipulated or influenced by individuals trying to achieve a specific outcome, such as gaming a system to get a desired result.
- Data from the Unobservable: Data from events that are not directly observable, such as events that occur in the future or in places that are difficult to access.
- Deliberate Darkening: This refers to situations where data is intentionally hidden or suppressed, often for malicious purposes.
While most of the book explains the different types of Dark Data, there is a second part that discusses (in rather boring statistical ways) how you can compensate for dark data, and also how you can actually use dark data to your advantage.
Overall, this book should be of interest to anyone who deals with data or has to make decisions, and is a must-read for anyone involved in processing data.
This book uses as its analogy the man searching under a streetlamp for the keys he dropped. And when asked if that was where he dropped the keys he says "no, but this is the only place I can see". And that illustrates quite nicely the problem that if we only look at the data we can see, we might be missing other very important data.
The book is full of examples of different kinds of dark data, and how they lead to wrong conclusions. Some examples that stuck in my head:
- A medical study found that people with asthma were more likely to recover from pneumonia than people who did not have asthma. The dark data here was that people with asthma were typically rushed immediately to the ICU, which is why they received much better medical care.
- Two studies on Medicine A and Medicine B showed a much higher success rate for A. However, it turns out that if you breakdown by age, B is actually much more effective across all age groups. How is that possible? Because the success rate of both medicines is much lower among older people, and the study for Medicine B comprised many more older people.
Another important lesson is that when you get an expected result, don't immediately think you have made a new discovery. It is much more likely that there is something wrong with your data. There are 14 types of Dark Data in total, of course I don't remember all of them. Here are a couple:
- Known Missing Data:This includes instances where it's explicitly known that data is missing, such as missing values in a survey or data points that were not recorded.
- Unknown Missing Data: This category involves situations where it's not known what data is missing.For example, the number of dissatisfied customers who don't bother complaining.
- Data Selection Bias: This occurs when the data collected only represents a subset of the population or phenomena, leading to skewed results.
- Missing Variables: Important characteristics or features of the data might be missing, leading to incomplete analysis.
- Changing Definitions: When the definition of a variable changes over time, it can make comparisons between datasets problematic.
- Summaries of Data: While summaries provide a concise overview, they can hide important details and variations within the data.
- Errors: When someone has entered the data wrongly
- Counterfactuals: This refers to data that would have been collected under different circumstances, but is not available.For example, what would have happened if a different treatment had been used in a clinical trial?
- Unstructured Data: Data in formats like emails, PDFs, or social media posts can be difficult to analyze without proper structuring and tools.
- Feedback and Gaming: This occurs when data is manipulated or influenced by individuals trying to achieve a specific outcome, such as gaming a system to get a desired result.
- Data from the Unobservable: Data from events that are not directly observable, such as events that occur in the future or in places that are difficult to access.
- Deliberate Darkening: This refers to situations where data is intentionally hidden or suppressed, often for malicious purposes.
While most of the book explains the different types of Dark Data, there is a second part that discusses (in rather boring statistical ways) how you can compensate for dark data, and also how you can actually use dark data to your advantage.
Overall, this book should be of interest to anyone who deals with data or has to make decisions, and is a must-read for anyone involved in processing data.
August 15, 2020
This book is written for a non-technical audience, and this review is written from this perspective.
The first 200 pages constitutes a useful, if slightly repetitive, survey of different ways that data can be "dark" - i.e., misleading. It might be wrong, it might be missing, it might be an unrepresentative sample, it might be the wrong data for the question to hand, the underlying reality might have changed since the data was collected and so forth. To be reminded in so much depth that we need to be mindful of what data is missing and how it might be wrong is useful.
The last 100 pages is the real meat, and it's unfortunate it is so brief. There is a good discussion of how data can be missing (viz: the missing data is dependent on present data, the missing data is not dependent on any other data or the missing data is dependent on other data which is also missing). The discussion of how to mitigate this issue is a bit brief. There is also a good if brief discussion of how deliberately darkening the data can be used to drive better analytical outcomes.
All in all, for me, this book hovers between 2 stars and 3. 3, because the final section touches on important issues. 2, because it only touches on them and doesn't do more than superficially brush the sand off a rich vein of ore. Perhaps that's a limitation of a popular science book written for a non-technical audience, and it's only so obvious when you are really in the technical audience. Overall 3 stars.
The first 200 pages constitutes a useful, if slightly repetitive, survey of different ways that data can be "dark" - i.e., misleading. It might be wrong, it might be missing, it might be an unrepresentative sample, it might be the wrong data for the question to hand, the underlying reality might have changed since the data was collected and so forth. To be reminded in so much depth that we need to be mindful of what data is missing and how it might be wrong is useful.
The last 100 pages is the real meat, and it's unfortunate it is so brief. There is a good discussion of how data can be missing (viz: the missing data is dependent on present data, the missing data is not dependent on any other data or the missing data is dependent on other data which is also missing). The discussion of how to mitigate this issue is a bit brief. There is also a good if brief discussion of how deliberately darkening the data can be used to drive better analytical outcomes.
All in all, for me, this book hovers between 2 stars and 3. 3, because the final section touches on important issues. 2, because it only touches on them and doesn't do more than superficially brush the sand off a rich vein of ore. Perhaps that's a limitation of a popular science book written for a non-technical audience, and it's only so obvious when you are really in the technical audience. Overall 3 stars.
October 25, 2022
“All this means, first, is that it’s necessary to be very clear about what question you are asking, and, second, that whether data are dark or not will depend on that question. Trite though it may sound, the data you need to collect, the analysis you will undertake, and the answer you will get depend on what you want to know.”
What is invisible to us when we, the non-statisticians, read about statistics? What do we lack in our own personal critical thinking? What questions are mathematicians asking? Or not asking? How can we read data and understand what went into it? These are the questions that Hand asks and answers. This is considered "Dark Data", which Hand lists, including time-dependent, purposefully obscured data, unrepresentative, and more.
As an introductory text to this way of thinking, it hits the mark. Through examples including our everyday statistics to more militaristic, financial, and demographic, Hand shows us just why it's so important to acknowledge what isn't acknowledged.
What is invisible to us when we, the non-statisticians, read about statistics? What do we lack in our own personal critical thinking? What questions are mathematicians asking? Or not asking? How can we read data and understand what went into it? These are the questions that Hand asks and answers. This is considered "Dark Data", which Hand lists, including time-dependent, purposefully obscured data, unrepresentative, and more.
As an introductory text to this way of thinking, it hits the mark. Through examples including our everyday statistics to more militaristic, financial, and demographic, Hand shows us just why it's so important to acknowledge what isn't acknowledged.
February 6, 2025
Two-thirds a quite bad popular science book; one-third a seemingly good or very good practical guide for beginner-level data practitioners. Why two-thirds bad? The first part is a greatest hits meander through some basic statistics, scientific practice, industry tales etc that I think will likely bore and quite possibly annoy anyone with expertise or past reading history in those areas. It's not concise or focused, and some parts read like outright filler. Do I want a two-page summary of nuclear fusion in a book about data? I do not. The second part, on how to correct for missing data in statistical treatments, seems far more useful and better edited, and might be a great 80-odd pages for a professional researcher who isn't an expert statistician, but this part certainly isn't very interesting for a general reader, and the package as a whole doesn't really work.
June 9, 2022
The book wasn’t bad but it wasn’t great either. I would have to say the first half was very solid and I loved reading it. I felt like I was learning tons of useful and fun information but then a lot of the second half felt repetitive; as other users have mentions, as if he was just providing more and more examples for things he’d already explained. I was struggling to read more than 15-20 pages at a time with interest and without falling asleep towards the end. I want to emphasize that this wasn’t because it was written badly, just not as exciting as the first half of the book.
Overall it’s a good viewpoint on data and I don’t regret reading it, as I know it will come in handy.
Overall it’s a good viewpoint on data and I don’t regret reading it, as I know it will come in handy.
April 16, 2024
Did not finish… read to page 48. Found the topic interesting but the author’s tone a bit judgmental and opinionated. The topic is statistics and the way missing or dark data can impact the results of surveys and studies. I would finish if this field had more to do with my day to day occupation as the systematic breakdown of the information is informative. My TBR stack is too big to spend more time on this topic, however.
May 28, 2020
A good and important read, but takes a bit of scientific background to fully comprehend. Problem is that people with a scientific backgrounds are mostly aware of dark data. Still, a good problem overview and cool categorization. Wish it was a boog for everyone though since this is a very important issue.
September 3, 2022
This book is not quite a text book, not quite popular science. I think it would be better if it shifted towards one or the other. It wasn’t the most thrilling read - I wasn’t really expecting it to be - but it did remind me of some aspects of working with data and made me aware of some others. A good foundation for my career change from maths to data science.
June 13, 2023
Very important concepts and ideas, this is why I rate the book highly- too for its earnest and sweet approach, gentle and poetic anecdotes. Very very slow read however, many many words and not for much reason.
December 19, 2024
This reads like a sequel to the old-but-gold How to Lie with Statistics, with some more up-to-date anecdotes. The “DD taxonomy” is a taxonomy only in Borges’s ironic sense; nevertheless, I think it’s worth keeping at hand and revisiting from time to time.
December 20, 2024
It's a good book, despite the fancy name.
It's important that data professionals develop their skills on Statistics. But most people wouldn't read it if "Statistics" appeared in the title.
All in all, I liked the book and recommend it!
It's important that data professionals develop their skills on Statistics. But most people wouldn't read it if "Statistics" appeared in the title.
All in all, I liked the book and recommend it!
December 2, 2023
Giving it 3.6 rounded up to 4. 90% of contents is trivial for someone with any degree of econometrics training. Zeroes could avail themselves well.
December 11, 2024
Wow that was boring - read like a text book, so it literally might be. Not at all what I expected so doesn’t feel fair to really rate … did not retain a thing
January 6, 2025
A must-read for any data professional
August 23, 2024
One of the best books I've read this year. Moving from a focus on data to absent, hidden, concealed, etc. data is powerful.
Insightful, funny, very clear due in large part to the writing style and abundant use of examples.
Useful for everyone with even the slightest interest in, say, understanding the news, spicy big data claims, or policy plans better. Or those with simply a knack for data and research.
Insightful, funny, very clear due in large part to the writing style and abundant use of examples.
Useful for everyone with even the slightest interest in, say, understanding the news, spicy big data claims, or policy plans better. Or those with simply a knack for data and research.
December 22, 2022
It took me generous amount of time to finish this book. I work in the field of big data and interested in data science. I also like philosophical questions around data and what we can/cannot know with data. What I found most enlightening with this book is the typologies of dark data, which encompasses missing data among many others. Typologies are super helpful for professional like myself, who constantly encounter these problems but don’t always have a framework for making distinctions between problems. I think the framework and the more or less pedagogical tone of the book was helpful to understand the framework. For example, at my work we have dark data related to known unknowns, unknown unknowns and self selected unknowns, but at times we talk about all these different types of dark data as the same thing in one breath. While ethical concerns are at the front of our mind when dealing with dark data, we may need to approach decision making differently depending on the type of dark data. I really hope to be able to use the DD framework introduced in this book more often when talking/discussing quality of data.
November 2, 2020
This made for a great read. Although I was already familiar with most of stuff discussed in book - the term "Dark Data" was new to me. The was dark data is dealt in this book made for a very interesting read. The book is well written and explains the subject matter quite nicely. The simple way in which this book is written makes it - in my opinion - a good read from beginners to experts. While the book covers generic examples in data sciences to cover common cases, the way the author explains the subject matter makes it quite easy for one to extend the concepts to their own area of research. The book is definitely recommended to all especially people working in data sciences in any capacity.
December 31, 2020
Interessante. Non per tutti.
May 31, 2021
I enjoyed the first half of the book a lot, the second part is quite technical but it’s fine
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