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Turning Data into Wisdom: How We Can Collaborate with Data to Change Ourselves, Our Organizations, and Even the World
When you think of data, do you think of complex charts and dashboards, things that are best left up to the experts to decipher? It's time to debunk that myth. In this book, you will discover the Turning Data into How We Can Collaborate with Data to Change Ourselves, Our Organizations, and Even the World presents a 6-phase, 12-step process to help those at all levels of an organization use their knowledge, skills, and experience to make data-informed decisions that can help transform their companies-and sometimes, even the world. The many real-life examples and case studies as well as tools, definitions, and templates will help you feel equipped and empowered to understand, seek out, and discuss data with others, ultimately using this information to make the best decisions for your business and the people it serves.
268 pages, Paperback
Published January 10, 2021
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Displaying 1 - 15 of 15 reviews
December 16, 2023
Read for my MLIS class
June 30, 2021
I'm pleased to welcome you to my first and likely only SPONSORED GOODREADS REVIEW, brought to you by whoever published this book and sent me a copy. Given my usual evisceration of most business publishing not sure why they picked me but too bad for them.
What I can't figure out is who this book is intended for. I've decided it isn't me. I think it might be a quasi textbook - it was large and floppy, bolded important words, and had a chapter that included "The 6-Phase Data-Informed Decision-Making Process" AND "The 12-Step Data-Informed Decision-Making Methodology" AND the "Top-10 [sic, I think they just started hyphenating every single word at this point] Data-Informed Decision-Making Skills." That's a cool 28 hot tips in only Chapter 2! It was decently comprehensive, I think, but pretty dry and weirdly un-opinionated. Which is fine, I guess, when talking about data and analytics, but it seemed like a platonic guide to analytics without any real reflection on what happens or could happen in the real world. Detailed frameworks and definitions can be helpful but I had hoped for more thought leadership here.
Also if I hear about Moneyball one more time I'm gonna scream.
So if this whole world is new to you and you want to arm yourself with some frameworks and foundations, this might do that for you, especially by reading chapters selectively as problems present themselves. Or maybe a reference guide for some guardrails if you feel overwhelmed with a data project. But as a straight-up read, I was kinda bored.
What I can't figure out is who this book is intended for. I've decided it isn't me. I think it might be a quasi textbook - it was large and floppy, bolded important words, and had a chapter that included "The 6-Phase Data-Informed Decision-Making Process" AND "The 12-Step Data-Informed Decision-Making Methodology" AND the "Top-10 [sic, I think they just started hyphenating every single word at this point] Data-Informed Decision-Making Skills." That's a cool 28 hot tips in only Chapter 2! It was decently comprehensive, I think, but pretty dry and weirdly un-opinionated. Which is fine, I guess, when talking about data and analytics, but it seemed like a platonic guide to analytics without any real reflection on what happens or could happen in the real world. Detailed frameworks and definitions can be helpful but I had hoped for more thought leadership here.
Also if I hear about Moneyball one more time I'm gonna scream.
So if this whole world is new to you and you want to arm yourself with some frameworks and foundations, this might do that for you, especially by reading chapters selectively as problems present themselves. Or maybe a reference guide for some guardrails if you feel overwhelmed with a data project. But as a straight-up read, I was kinda bored.
April 20, 2021
Disclaimer: I received this book as a complimentary copy in exchange for an honest review.
About me: I am a Data Scientist at an NYC Fintech, with 4 years of experience applying AI/ML in the financial sector. On a daily basis, I deal with key stakeholders from the rank-and-file ops user, to senior management at major global banks.
Review:
Here's a book that's easy to read but tough to review. It presents the problem of working with and making sense of data in 6 phases:
1. Ask
2. Acquire
3. Analyze
4. Apply
5. Announce
6. Assess
The above is pretty accurate, and an excellent way to think of data-related problems. Each of those are broken down into sections where the author develops his thesis, providing great examples along the way to cement the material.
One can easily tell he is a great educator from the way he handles difficult concepts and makes them accessible without being patronizing. In that, he is also thorough.
My major gripe with the book, and the reason I only gave it 4-stars, is that I am at a loss as to who this book was intended to serve. Depending on the audience, it could be: (perfect!), (too technical), (not technical enough), (too long).
The first 5 chapters are, in all honesty, great! Having been part of implementation teams at large financial institutions, I always found myself going through the steps outlined therein and explaining the same concepts as the author (albeit less brilliantly) to senior management and low-level stakeholders. In that sense, these five initial chapters are invaluable for anyone trying to explain the problem of extracting value from data and how to go about it from a completely methodological standpoint. So, perfect if you need to talk to (busy) managers that only need to get the overall picture and buy into it, as well as the ops users that will be implementing these first steps.
From here on, I became a bit lost in the scope of the material. Chapters 6 ("The Analyze Phase") through 9 ("The Apply Phase") are a mishmash of things that seem a bit Frankensteinian. For instance, if you read and found the first five chapters useful, you would not expect the author to tell you about means, standard deviations, and the normal curve in chapter 6. It's a bit too basic for that audience (and probably any audience of this book) and probably does not belong here. Chapters 8 and 9 are a bit too psych-101, filled with too many principles and "rules" that are redolent of popular science books. In short, too "bloggy".
Fortunately, the author managed to bring it in for a strong finish in the latter chapters, providing useful tools, frameworks, models, templates and job-aides that will surely benefit his audience. The last chapter in particular ("Example use cases and case studies") is brilliant as it presents the overall picture without any of the fluff. I loved it!
Overall, I think this book would be useful to anyone looking to make informed, data-driven decisions, or are working closely with those that will. Recommended.
About me: I am a Data Scientist at an NYC Fintech, with 4 years of experience applying AI/ML in the financial sector. On a daily basis, I deal with key stakeholders from the rank-and-file ops user, to senior management at major global banks.
Review:
Here's a book that's easy to read but tough to review. It presents the problem of working with and making sense of data in 6 phases:
1. Ask
2. Acquire
3. Analyze
4. Apply
5. Announce
6. Assess
The above is pretty accurate, and an excellent way to think of data-related problems. Each of those are broken down into sections where the author develops his thesis, providing great examples along the way to cement the material.
One can easily tell he is a great educator from the way he handles difficult concepts and makes them accessible without being patronizing. In that, he is also thorough.
My major gripe with the book, and the reason I only gave it 4-stars, is that I am at a loss as to who this book was intended to serve. Depending on the audience, it could be: (perfect!), (too technical), (not technical enough), (too long).
The first 5 chapters are, in all honesty, great! Having been part of implementation teams at large financial institutions, I always found myself going through the steps outlined therein and explaining the same concepts as the author (albeit less brilliantly) to senior management and low-level stakeholders. In that sense, these five initial chapters are invaluable for anyone trying to explain the problem of extracting value from data and how to go about it from a completely methodological standpoint. So, perfect if you need to talk to (busy) managers that only need to get the overall picture and buy into it, as well as the ops users that will be implementing these first steps.
From here on, I became a bit lost in the scope of the material. Chapters 6 ("The Analyze Phase") through 9 ("The Apply Phase") are a mishmash of things that seem a bit Frankensteinian. For instance, if you read and found the first five chapters useful, you would not expect the author to tell you about means, standard deviations, and the normal curve in chapter 6. It's a bit too basic for that audience (and probably any audience of this book) and probably does not belong here. Chapters 8 and 9 are a bit too psych-101, filled with too many principles and "rules" that are redolent of popular science books. In short, too "bloggy".
Fortunately, the author managed to bring it in for a strong finish in the latter chapters, providing useful tools, frameworks, models, templates and job-aides that will surely benefit his audience. The last chapter in particular ("Example use cases and case studies") is brilliant as it presents the overall picture without any of the fluff. I loved it!
Overall, I think this book would be useful to anyone looking to make informed, data-driven decisions, or are working closely with those that will. Recommended.
July 18, 2021
This is a useful book about how data science and analysis can be, or even should be, used. It’s directed at the project management/business side of things, and doesn’t go into depth about analytical techniques at all. Instead it proposes a method for organizing data analysis projects, and most important addresses some of things that data science can’t do on its own, such as overcoming bias or ensuring data validity. An analyst might still take an interest in useful techniques for managing collaboration and conflict resolution. And just in general to book has an excellent bibliography of data science, project management, and business psychology resources.
I received this book from the publisher.
I received this book from the publisher.
July 1, 2021
I received a review copy of this through LibraryThing back in April. The timing for me wasn’t the best as my wife and I had just put our house on the market and were going through multiple rounds of showings, contracts, buyers backing out... well, that and other factors made it hard to focus on this. And this book needs a little more focus. (I started over a couple of weeks ago, reviewing all of my notes, and then was able to carve out the time to focus myself.) The concepts are far from difficult - you've seen them all before, but the reading, and my responding, were more difficult than I thought it should be. Part of it is the presentation; terms defined in a glossary are bolded, and there are some sections that have bold bleeding all over the pages (one paragraph has eight bold phrases!) And, a lot of italics. I get the idea, but it’s distracting from that focus. The overall impression is clinical, academic, cluttered, visually distracting. There is a lot of reinforcement, which will be repetitive to someone grasping the ideas quickly.
When I read books like this, I try to figure out who the target audience is. That was a challenge here. The chapter for his Analyze phase had some good information... mired with way-too-in-the-weeds details on statistical analysis, like showing how to manually calculate standard deviations?? I found it particularly interesting that the two sentences at the top of the back cover were “When you think of data do you think of complex charts and dashboards, things that are best left up to the experts to decipher? It’s time to debunk that myth.” and yet the author spent more than 50 pages on the minutiae of statistics. And I thought the presentation of causal loop diagrams less intuitive than Ackoff’s actual concepts.
The decision-making process Phases Hanegan offers -Ask, Acquire, Analyze, Apply, Announce, Assess - are alliterative, but they don’t have to be. Yes, it sells better though to have a handle. Also, I'm not fond of using definite articles, example:The 12-Step Methodology, is more authoritative than “A 12-Step”. That’s normal for these types of books, I know, but I always key on those word choices, like “Figure 3 shows the top-ten skills required within an organization to follow the data-informed decision-making process.” the top ten? what’s the source for that pronouncement? No source, it’s opinion and we’ve all read other books with different lists also making similar claims.
Pages 60-62 in the paperback copy I received had some of the best takeaways... “Ensuring that data can be trusted: Having a Data Strategy” and descriptions of characteristics of quality data are a good cheat sheet to hang on to. Later, on measurement and choosing the right key performance indicators, the author rightly notes that choosing the wrong ones can not only be useless, it could be harmful.
“Most strategic decisions within an organization are ripe for groupthink,...” Really? I disagree. General decisions maybe, but truly strategic decisions are either one person after assessing the options, or a team by a thought out process. Not groupthink.
Note to the author and the publisher: careful with quotes. Two epigraphs that I checked were sourced to sites that aren't that reliable when it comes to quotes. One, from Goodreads, is certainly not in itself questionable, but the quotes there are user curated and are misattributed many, many times. I found that particular quote from B.J. Neblett on his own blogspot page. Another, the Edison one about finding 10,000 ways to fail (I will often try to track down colloquial quotes from well-known persons...I don't always find the original source, and then I might say "attributed to"), was sourced from BrainyQuote. BrainyQuote is even less reliable that Goodreads, which is more disturbing as it purports to be a quote resource. Edison never actually said that, though he did say something close as recounted in a 1910 biography, and again, something similar in a 1926 interview.
So... the target? Student? Maybe. Mid-level manager, someone new to management? Also maybe. Certainly not a high level manager or executive who doesn’t need to do the heavy lifting herself. And if you are one of those higher on the higher levels, it might help to understand what is being asked (and maybe reconsider the asking). Still, most of the concepts once distilled and filtered are good to add to the toolbox.
When I read books like this, I try to figure out who the target audience is. That was a challenge here. The chapter for his Analyze phase had some good information... mired with way-too-in-the-weeds details on statistical analysis, like showing how to manually calculate standard deviations?? I found it particularly interesting that the two sentences at the top of the back cover were “When you think of data do you think of complex charts and dashboards, things that are best left up to the experts to decipher? It’s time to debunk that myth.” and yet the author spent more than 50 pages on the minutiae of statistics. And I thought the presentation of causal loop diagrams less intuitive than Ackoff’s actual concepts.
The decision-making process Phases Hanegan offers -Ask, Acquire, Analyze, Apply, Announce, Assess - are alliterative, but they don’t have to be. Yes, it sells better though to have a handle. Also, I'm not fond of using definite articles, example:The 12-Step Methodology, is more authoritative than “A 12-Step”. That’s normal for these types of books, I know, but I always key on those word choices, like “Figure 3 shows the top-ten skills required within an organization to follow the data-informed decision-making process.” the top ten? what’s the source for that pronouncement? No source, it’s opinion and we’ve all read other books with different lists also making similar claims.
Pages 60-62 in the paperback copy I received had some of the best takeaways... “Ensuring that data can be trusted: Having a Data Strategy” and descriptions of characteristics of quality data are a good cheat sheet to hang on to. Later, on measurement and choosing the right key performance indicators, the author rightly notes that choosing the wrong ones can not only be useless, it could be harmful.
“Most strategic decisions within an organization are ripe for groupthink,...” Really? I disagree. General decisions maybe, but truly strategic decisions are either one person after assessing the options, or a team by a thought out process. Not groupthink.
Note to the author and the publisher: careful with quotes. Two epigraphs that I checked were sourced to sites that aren't that reliable when it comes to quotes. One, from Goodreads, is certainly not in itself questionable, but the quotes there are user curated and are misattributed many, many times. I found that particular quote from B.J. Neblett on his own blogspot page. Another, the Edison one about finding 10,000 ways to fail (I will often try to track down colloquial quotes from well-known persons...I don't always find the original source, and then I might say "attributed to"), was sourced from BrainyQuote. BrainyQuote is even less reliable that Goodreads, which is more disturbing as it purports to be a quote resource. Edison never actually said that, though he did say something close as recounted in a 1910 biography, and again, something similar in a 1926 interview.
So... the target? Student? Maybe. Mid-level manager, someone new to management? Also maybe. Certainly not a high level manager or executive who doesn’t need to do the heavy lifting herself. And if you are one of those higher on the higher levels, it might help to understand what is being asked (and maybe reconsider the asking). Still, most of the concepts once distilled and filtered are good to add to the toolbox.
July 21, 2021
Disclaimer: I received a free review print copy of this book from the publisher in exchange for an honest review.
I am most definitely not the target audience for this. Superficially, it looked like I might be, since my career is built on data management and optimization. However, this is 100% a business book, and I have a bit of a history with business-speak.
This book did not break that mold, being made of approximately 85% buzzwords, 10% pointless "figures" (the one that stands out most is on page 122: a see saw with two circles on it labeled "Experience and Intuition" and "Data", because there is no way I would have understood the idea of balancing two concepts without a 1/4 page MS Paint diagram...), and about 5% actual useful information.
Being a scientist first, it's borderline offensive that the only useful sections, about statistics and, you know....actual data science? Those get yadda-yadda'd at best, a brief one sentence mention that the idea exists at worst.
Most of this is redundant filler, and it reads a lot like a conference talk or seminar for business folk got padded out to book length.
There is value in challenging your assumptions and knowing how to use data appropriately. Speaking from experience, most business folks practice cargo cult data science: they imitate what they see others doing with no real understanding of what they're trying to do.
The useful sections are almost entirely about mitigation of bias and approaching problems in such a way that accomplish this end. There was also a section about data integrity and assuring that you can trust your data. That is a battle I get to fight often as a developer, so it was nice to see.
Otherwise, this reads like every other business-minded text I've read before. The concepts which are incredibly simple get over-explained with comically pointless visual aids, while the actual hard, useful, and important stuff (e.g. statistics) get hand-waved with just a brief mention. I could imagine some business folks where I work coming away from this thinking they understand data science and making my life that much more difficult (as this book mentions, un-learning is not easy).
I can see this having utility for someone in business coming completely new and unspoiled to the topic needing a general overview. If you actually work with data and know about the topic already, this won't have much you can take away.
One parting note I just remembered: On page 150 is the funniest figure of all, "Categorizing items into groups using color." This, like the rest of the book, is printed in grayscale and the dots are indistinguishable when they are clearly intended to be color-coded in some way.
I am most definitely not the target audience for this. Superficially, it looked like I might be, since my career is built on data management and optimization. However, this is 100% a business book, and I have a bit of a history with business-speak.
This book did not break that mold, being made of approximately 85% buzzwords, 10% pointless "figures" (the one that stands out most is on page 122: a see saw with two circles on it labeled "Experience and Intuition" and "Data", because there is no way I would have understood the idea of balancing two concepts without a 1/4 page MS Paint diagram...), and about 5% actual useful information.
Being a scientist first, it's borderline offensive that the only useful sections, about statistics and, you know....actual data science? Those get yadda-yadda'd at best, a brief one sentence mention that the idea exists at worst.
Most of this is redundant filler, and it reads a lot like a conference talk or seminar for business folk got padded out to book length.
There is value in challenging your assumptions and knowing how to use data appropriately. Speaking from experience, most business folks practice cargo cult data science: they imitate what they see others doing with no real understanding of what they're trying to do.
The useful sections are almost entirely about mitigation of bias and approaching problems in such a way that accomplish this end. There was also a section about data integrity and assuring that you can trust your data. That is a battle I get to fight often as a developer, so it was nice to see.
Otherwise, this reads like every other business-minded text I've read before. The concepts which are incredibly simple get over-explained with comically pointless visual aids, while the actual hard, useful, and important stuff (e.g. statistics) get hand-waved with just a brief mention. I could imagine some business folks where I work coming away from this thinking they understand data science and making my life that much more difficult (as this book mentions, un-learning is not easy).
I can see this having utility for someone in business coming completely new and unspoiled to the topic needing a general overview. If you actually work with data and know about the topic already, this won't have much you can take away.
One parting note I just remembered: On page 150 is the funniest figure of all, "Categorizing items into groups using color." This, like the rest of the book, is printed in grayscale and the dots are indistinguishable when they are clearly intended to be color-coded in some way.
July 31, 2021
Full disclosure: I received a copy of this book from someone representing the author/publisher.
Turning Data into Wisdom seems to have a couple objectives in mind and at times they seem to collide a bit. On one hand, I think this is an excellent introduction to the emerging world of Data Science and Analytics. I can see students using this book as a textbook in many ways. I suspect however that the intention of the book was more geared towards those already in the business world so some of this may be a little too elementary for their liking and doesn't get quite down far enough into the weeds.
The Good: The book is great at explaining concepts at the high level and gives the reader just enough ammunition to become dangerous. This good act as a gateway for more study and exploration which is a great accomplishment. The book is well organized and has a nice flow to it and doesn't seem to get lost within itself like other texts tend to do. I loved the inclusion of various models to the fact that it even contains a chapter (albeit short) dedicated to just models. As the Chief Enterprise and Solutions Architect for my firm, models are a must, and premium models are invaluable.
The Not So Good: Again, for someone just getting into Data Science, this is a pretty solid ice breaker, however for those needing to get more into the meat of things, this book may just get in the way.
Ideas for improvement to consider:
1. Perhaps include worksheet sections of the book for notes and exercises. Often times I like to work within the text to solve problems that I am currently experiencing and I would prefer not having separate sheets of notes that can be easily lost.
2. The book was written with a large or larger enterprise in mind, but it doesn't give a good sense of scalability for smaller company's or organizations.
3. The book was also written with sales and retail in mind, not every organization exists in the world, but has a need or desire to build in elements of Data Science and Analytics into their portfolio, so I would have loved to have seen examples of how the information and lessons could have been used in different manners or scenerios rather than the typical maximizing of units sold.
Overall a solid effort with room for improvement through revisions and expansion. I am definitely happy to have this sitting on my office shelf as a reference tool.
Turning Data into Wisdom seems to have a couple objectives in mind and at times they seem to collide a bit. On one hand, I think this is an excellent introduction to the emerging world of Data Science and Analytics. I can see students using this book as a textbook in many ways. I suspect however that the intention of the book was more geared towards those already in the business world so some of this may be a little too elementary for their liking and doesn't get quite down far enough into the weeds.
The Good: The book is great at explaining concepts at the high level and gives the reader just enough ammunition to become dangerous. This good act as a gateway for more study and exploration which is a great accomplishment. The book is well organized and has a nice flow to it and doesn't seem to get lost within itself like other texts tend to do. I loved the inclusion of various models to the fact that it even contains a chapter (albeit short) dedicated to just models. As the Chief Enterprise and Solutions Architect for my firm, models are a must, and premium models are invaluable.
The Not So Good: Again, for someone just getting into Data Science, this is a pretty solid ice breaker, however for those needing to get more into the meat of things, this book may just get in the way.
Ideas for improvement to consider:
1. Perhaps include worksheet sections of the book for notes and exercises. Often times I like to work within the text to solve problems that I am currently experiencing and I would prefer not having separate sheets of notes that can be easily lost.
2. The book was written with a large or larger enterprise in mind, but it doesn't give a good sense of scalability for smaller company's or organizations.
3. The book was also written with sales and retail in mind, not every organization exists in the world, but has a need or desire to build in elements of Data Science and Analytics into their portfolio, so I would have loved to have seen examples of how the information and lessons could have been used in different manners or scenerios rather than the typical maximizing of units sold.
Overall a solid effort with room for improvement through revisions and expansion. I am definitely happy to have this sitting on my office shelf as a reference tool.
September 11, 2022
I also received a free copy with the promise to write an honest review.
This book is written like a textbook but it's meant to be a how-to guide to collect, process and then group-think one's way to specific conclusions that the data shows.
In that sense, it's a little deceiving because it assumes that one can be some what bias free, but also that this is what is needed to make decisions for specific business contexts that are purely data driven.
In the quest for practicality Hanegan promotes an ideology -- an ideal image of "how to be" -- in order to guide the process towards some kind of resolution. Because the world is not ideal however, the best Hanegan can do is present a list heuristics arranged by a narrative process (lists in lists) of what to do at each step, and what to look out for in order to proceed to the next step. This does not make for great reading but it might be useful for new practitioners at each stage (entry level all the way to management).
As interpretation is hard, one has to believe one can achieve success for one to have some direction. In the final picture, when Hanegan allows that we must question our values, he reaches the limit of his approach -- because the impetus to present a universal process requires generality, and values are always specific in their expression. For example, suffering must always be of a specific individual for us to recognize that there is suffering in the world.
Interesting send though.
This suggests somethings about how we approach things as scientists and business people. But perhaps that's a completely different topic.
This book is written like a textbook but it's meant to be a how-to guide to collect, process and then group-think one's way to specific conclusions that the data shows.
In that sense, it's a little deceiving because it assumes that one can be some what bias free, but also that this is what is needed to make decisions for specific business contexts that are purely data driven.
In the quest for practicality Hanegan promotes an ideology -- an ideal image of "how to be" -- in order to guide the process towards some kind of resolution. Because the world is not ideal however, the best Hanegan can do is present a list heuristics arranged by a narrative process (lists in lists) of what to do at each step, and what to look out for in order to proceed to the next step. This does not make for great reading but it might be useful for new practitioners at each stage (entry level all the way to management).
As interpretation is hard, one has to believe one can achieve success for one to have some direction. In the final picture, when Hanegan allows that we must question our values, he reaches the limit of his approach -- because the impetus to present a universal process requires generality, and values are always specific in their expression. For example, suffering must always be of a specific individual for us to recognize that there is suffering in the world.
Interesting send though.
This suggests somethings about how we approach things as scientists and business people. But perhaps that's a completely different topic.
August 1, 2021
This is a book that is more than the sum of its parts.
It is formatted like a textbook, and it provides a good review of basic and advanced terms that those who have taken statistics will recognize. But it's not a data book, or a college textbook.
I believe that Turning Data into Wisdom is a leadership book.
In his preface, Kevin Hanegan writes, "decision-making should celebrate diversity and inclusion and be a team sport, as these different perspectives allow you to see multiple sides of the data and what its story is." Revealing that his son is autistic, Hanegan tailors this book to the diverse ways that leaders process data--and the high-quality decisions that come from diverse work cultures and data-processing models.
After one chapter examining how bias and groupthink mar decision-making, Hanegan uses the bulk of the book to lay out a six-step decision-making process. I found this process really insightful, as did my wife, the CEO of a nonprofit.
Hanegan ends the book with a series of case studies, showing how the six-step process works in real time. Appropriately, he includes a case study of how he worked with his son's school to address a behavior problem his son had faced.
I found the book engaging, worth holding onto as a resource for future big decisions. I would recommend it for organizational leaders who hope to make better decisions -- who see data analysis as the key.
Full disclosure, I received an advance copy from the publisher. The opinions are my own.
It is formatted like a textbook, and it provides a good review of basic and advanced terms that those who have taken statistics will recognize. But it's not a data book, or a college textbook.
I believe that Turning Data into Wisdom is a leadership book.
In his preface, Kevin Hanegan writes, "decision-making should celebrate diversity and inclusion and be a team sport, as these different perspectives allow you to see multiple sides of the data and what its story is." Revealing that his son is autistic, Hanegan tailors this book to the diverse ways that leaders process data--and the high-quality decisions that come from diverse work cultures and data-processing models.
After one chapter examining how bias and groupthink mar decision-making, Hanegan uses the bulk of the book to lay out a six-step decision-making process. I found this process really insightful, as did my wife, the CEO of a nonprofit.
Hanegan ends the book with a series of case studies, showing how the six-step process works in real time. Appropriately, he includes a case study of how he worked with his son's school to address a behavior problem his son had faced.
I found the book engaging, worth holding onto as a resource for future big decisions. I would recommend it for organizational leaders who hope to make better decisions -- who see data analysis as the key.
Full disclosure, I received an advance copy from the publisher. The opinions are my own.
August 22, 2021
I accepted a free copy of this book for a review. Unfortunately, that was a mistake. This book was not for me, and I'm not sure who it would be good for. It's mostly Business 101 about decision making, but it isn't particularly useful for that because it's mostly just a catalog of different techniques and tools for gathering and using data that the book doesn't explain in enough depth to be useful. It gives you just enough information about the different stages and tools of data gathering and analysis that you could go into a business meeting and sound like the pointy haired boss in Dilbert. I can't imagine using this book as a reference the next time my firm or a client has to make a complex decision.
I recently read another book about improving business decison making that was far better - Noise by Daniel Kahneman, Olivier Sibony and Cass Sunstein. Noise presents a coherent theory about a specific but pervasive problem in decision making and then provides a well-thought out program for dealing with the problem. It's a much more interesting read than this book and much more valuable in giving business advice that I might actually follow.
I recently read another book about improving business decison making that was far better - Noise by Daniel Kahneman, Olivier Sibony and Cass Sunstein. Noise presents a coherent theory about a specific but pervasive problem in decision making and then provides a well-thought out program for dealing with the problem. It's a much more interesting read than this book and much more valuable in giving business advice that I might actually follow.
July 29, 2021
I received a free copy of this book with the intent to provide a review.
This was a fun one for someone who works in data. I found a lot of good tactics in this book for guiding decision making based on data and analysis. There are a number of diagrams and methods for data presentation that are described in detail and would be very helpful to a person new to data-based analytics. Overall, I think this would be a useful text for college courses and for junior engineers or data analysts who will do this kind of analysis and presentation over their careers. Even for mid-career data analysts, there are topics and constructs presented in this book that I think can provide a useful framework for thinking through the impacts of data in their work. I really enjoyed the chance to read this book and found it thought-provoking. It even helped me with a sticky problem I'm solving in my own work!
This was a fun one for someone who works in data. I found a lot of good tactics in this book for guiding decision making based on data and analysis. There are a number of diagrams and methods for data presentation that are described in detail and would be very helpful to a person new to data-based analytics. Overall, I think this would be a useful text for college courses and for junior engineers or data analysts who will do this kind of analysis and presentation over their careers. Even for mid-career data analysts, there are topics and constructs presented in this book that I think can provide a useful framework for thinking through the impacts of data in their work. I really enjoyed the chance to read this book and found it thought-provoking. It even helped me with a sticky problem I'm solving in my own work!
March 22, 2024
I tend to refrain from writing reviews here on GoodReads because my experience with every book I read is deeply personal and probably not relevant to lots of other folks. This book, however, deserves my special attention. First, I should make it clear that I actually think data science is interesting. So my objections are not related to the subject matter. Rather, my issue is that this textbook is powerfully unreadable. Further, it has no index, making navigating the already poorly written text that much more cumbersome. It appears to be self-published, with the author listed as the publisher. I’m not opposed to self-publishing, but perhaps the author’s points would have been better served by some serious editing of the text. This whole thing could’ve been written via ChatGPT. It’s literally just one list after another.
August 13, 2021
I'm not sure why I was sent a copy of this book to review as I don't think it's particularly relevant to me or anything I'm doing. Perhaps that's why the book fell so flat. I'm not sure who the intended audience was. I found the pages to be inundated with distracting graphics and confusing writing. Each chapter was a firehose of dozens of seemingly disconnected ideas that left me trying to figure out just how any of this was related. It felt like the author was taking everything they'd ever learned about data ever and they tried to cram it into this book. Sometimes the book meandered through various frameworks and then we'd be learning the equations for mean and standard deviation. I'm sure there's someone this book is written for but to be honest, I couldn't tell you who that is.
May 13, 2021
Interesting book-- integrates data and decision-making. Perhaps best for people in the business world that need a quick introduction. A bit quirky in terms of topics, and trades some precision for attempted clarity, not always successfully.
December 1, 2021
I was sent a copy of this book for free in exchange for an honest review.
First thoughts - it looks like a text book (size wise), though the excerpt I read sounded more like one of the other data books I have read recently - so hoping for a bit more entertainment than a text book might provide.
This is indeed more of a text book than a read-for-fun book, however it is getting 5 stars because it is a pretty dang good text book. I appreciated the bolded words (which are defined in the chapter, and they are also in the glossary at the back) AND as I have mentioned in my progress updates the examples are actual real world examples. I can see this book being a great addition to a Data Analyzing class, and the examples would open up to some great discussions (e.g. if the data said this, what kind of interpretation could we have? )
First thoughts - it looks like a text book (size wise), though the excerpt I read sounded more like one of the other data books I have read recently - so hoping for a bit more entertainment than a text book might provide.
This is indeed more of a text book than a read-for-fun book, however it is getting 5 stars because it is a pretty dang good text book. I appreciated the bolded words (which are defined in the chapter, and they are also in the glossary at the back) AND as I have mentioned in my progress updates the examples are actual real world examples. I can see this book being a great addition to a Data Analyzing class, and the examples would open up to some great discussions (e.g. if the data said this, what kind of interpretation could we have? )
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