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Sensemaking: What Makes Human Intelligence Essential in the Age of the Algorithm [Paperback] [Mar 20, 2017] Christian Madsbjerg
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240 pages, Paperback
Published March 28, 2017
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About the author
Christian Madsbjerg
5 books46 followersChristian Madsbjerg is a founder of ReD Associates and the Director of its New York office. ReD is a strategy consulting company based in the human sciences and employs anthropologists, sociologists, art historians, and philosophers. Christian studied philosophy and political science in Copenhagen and London. He lives in New York City.
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Displaying 1 - 30 of 84 reviews
March 4, 2018
What a disappointment.
I had started reading the book with the expectation that it would be a serious discussion of a really deep problem: how important is context in our age of big data? Indeed, one of the most controversial promises of big data is that it claims to be able to make accurate predictions without knowledge of the context - see for instance https://www.wired.com/2008/06/pb-theory/
On the other hand, some critics claim that without accurate information on the context, big data makes trivial mistakes that a ten year old human wouldn't. Fields where both extreme claims are well illustrated are of course machine translation and on-line search. Sometimes, Google search and translate give us the feeling that they can almost read our minds, up to the point where this eerie. In other cases, the results are laughable and the mistakes trivial.
Because big data algorithms are strictly based on observed behaviour, they also tend to confirm blindly existing biases, as has for instance been argued by Cathy O'Neil in a book that is less than perfect but at least based on a real understanding of the subject - see
https://www.goodreads.com/review/show...
So, I was very curious to hear what someone with a background in philosophy and political science has to say about the subject. In the case of this book, not a lot, I am afraid.
Instead of providing an in-depth analysis of the human's mind capacity to understand context where neural network or other algorithms fail, this book is a barely concealed advertisement for the author's consulting activities. If you allow me to make a caricature, the core of his message is that Heidegger provides a better guide to understanding human nature than quantitative analysis (given Heidegger's behaviour during Nazism, I am sure he understands a thing or two about opportunism and cowardice). The problem is that, once you get beyond the name- and terminology-dropping, the message is simply that understanding the context of data is important. Well, I have no problem with that. I am a quantitative social scientist myself, and I can assure you that the field is very aware of the dangers of blindly applying insights gained in one context to another. And, contrary to what Madsjberg suggests, issues of underlying values, lifestyles and social influences are a key topic in quantitative social analysis - if you're not cot convinced, just Google (yeah, right) search terms such as "latent variable" and "latent classes".
At the end of the day, this book is essentially another pretentious criticism of somebody else's field by an author who has never bothered to really understand what the people in the criticised field actually do. There is no serious attempt anywhere in the book to ponder the pros and the cons of an argument.
I will give just one concrete example.
In one of the final chapters, Madsjberg discusses oenology, and makes bold claims about the superior judgements of people who have their boots in the (vineyard) ground compared to quantitative analysts. As usual, Madsjberg does not prove his point: he just repeats it over and over again, and expects this will be enough to convince the reader. The point is that, if Madsjberg would ever have bothered to look up information that didn't confirm his prejudices, he would have come across the work of Orley Ashenfelter, whose purely quantitative analysis of wine has systematically outperformed the claims of the field experts. Ouch.
Now, let's be clear. I do not claim, and will never claim, that context-free quantitative analysis is always superior to the judgement of experts. I am just debunking Madsjberg confidence in the superiority of "field experts".
Actually, the conditions under which subjective expertise is a basis for accurate discussions has been the subject of a discussion between Nobel prize winning psychologist Daniel Kahneman
and Gary Klein. I refer the interested reader to the link below (this paper is actually a wonderful example of two scholars who had opposite views on a topic, and who, through constructive dialogue, have been able to find a common ground):
http://www.chrissnijders.com/eth2012/...
So, if you want to learn something really about the value of subjective expertise, read Kahneman
and Klein. But don't waste time and money on a book that is mostly self-promoting.
I had started reading the book with the expectation that it would be a serious discussion of a really deep problem: how important is context in our age of big data? Indeed, one of the most controversial promises of big data is that it claims to be able to make accurate predictions without knowledge of the context - see for instance https://www.wired.com/2008/06/pb-theory/
On the other hand, some critics claim that without accurate information on the context, big data makes trivial mistakes that a ten year old human wouldn't. Fields where both extreme claims are well illustrated are of course machine translation and on-line search. Sometimes, Google search and translate give us the feeling that they can almost read our minds, up to the point where this eerie. In other cases, the results are laughable and the mistakes trivial.
Because big data algorithms are strictly based on observed behaviour, they also tend to confirm blindly existing biases, as has for instance been argued by Cathy O'Neil in a book that is less than perfect but at least based on a real understanding of the subject - see
https://www.goodreads.com/review/show...
So, I was very curious to hear what someone with a background in philosophy and political science has to say about the subject. In the case of this book, not a lot, I am afraid.
Instead of providing an in-depth analysis of the human's mind capacity to understand context where neural network or other algorithms fail, this book is a barely concealed advertisement for the author's consulting activities. If you allow me to make a caricature, the core of his message is that Heidegger provides a better guide to understanding human nature than quantitative analysis (given Heidegger's behaviour during Nazism, I am sure he understands a thing or two about opportunism and cowardice). The problem is that, once you get beyond the name- and terminology-dropping, the message is simply that understanding the context of data is important. Well, I have no problem with that. I am a quantitative social scientist myself, and I can assure you that the field is very aware of the dangers of blindly applying insights gained in one context to another. And, contrary to what Madsjberg suggests, issues of underlying values, lifestyles and social influences are a key topic in quantitative social analysis - if you're not cot convinced, just Google (yeah, right) search terms such as "latent variable" and "latent classes".
At the end of the day, this book is essentially another pretentious criticism of somebody else's field by an author who has never bothered to really understand what the people in the criticised field actually do. There is no serious attempt anywhere in the book to ponder the pros and the cons of an argument.
I will give just one concrete example.
In one of the final chapters, Madsjberg discusses oenology, and makes bold claims about the superior judgements of people who have their boots in the (vineyard) ground compared to quantitative analysts. As usual, Madsjberg does not prove his point: he just repeats it over and over again, and expects this will be enough to convince the reader. The point is that, if Madsjberg would ever have bothered to look up information that didn't confirm his prejudices, he would have come across the work of Orley Ashenfelter, whose purely quantitative analysis of wine has systematically outperformed the claims of the field experts. Ouch.
Now, let's be clear. I do not claim, and will never claim, that context-free quantitative analysis is always superior to the judgement of experts. I am just debunking Madsjberg confidence in the superiority of "field experts".
Actually, the conditions under which subjective expertise is a basis for accurate discussions has been the subject of a discussion between Nobel prize winning psychologist Daniel Kahneman
and Gary Klein. I refer the interested reader to the link below (this paper is actually a wonderful example of two scholars who had opposite views on a topic, and who, through constructive dialogue, have been able to find a common ground):
http://www.chrissnijders.com/eth2012/...
So, if you want to learn something really about the value of subjective expertise, read Kahneman
and Klein. But don't waste time and money on a book that is mostly self-promoting.
September 28, 2017
This was an interesting experience - I agreed with so many of the points, and yet was also annoyed by the tone of a lot of this book.
Quite a continental philosophy approach, which has its benefits but can also border on the woo-woo. I 100% agree about the importance of context and understanding rich, or "thick", data but I also think there are data users and thinkers doing great jobs of that already. I think I felt personally affronted at the idea that all data trained folks were so narrow-minded and many of the examples seemed less than charitable early in the book. There are some nice examples of well-rounded successful thinkers later, though.
And I guess I already agreed with the premise of the importance of arts degrees going in and had actively included liberal arts elements in my Science degree. Not sure if this book would convince you otherwise if you'd gone in with a different opinion and might make you think liberal arts graduates are a bit insufferable...
Quite a continental philosophy approach, which has its benefits but can also border on the woo-woo. I 100% agree about the importance of context and understanding rich, or "thick", data but I also think there are data users and thinkers doing great jobs of that already. I think I felt personally affronted at the idea that all data trained folks were so narrow-minded and many of the examples seemed less than charitable early in the book. There are some nice examples of well-rounded successful thinkers later, though.
And I guess I already agreed with the premise of the importance of arts degrees going in and had actively included liberal arts elements in my Science degree. Not sure if this book would convince you otherwise if you'd gone in with a different opinion and might make you think liberal arts graduates are a bit insufferable...
June 28, 2017
The message that there is more to algorithms than math is compelling. I agree with the author that there is something missing in the field of human decision making when math and statistics are used in a manner that is divorced from inference and understanding of the numbers.
I'm not sure that this is the absolute strongest argument that could be made. Thick and Thin data are a bit of a sad attempt to say analysis that is good versus stuff could be done by a 10 year old.
Still, this work dovetails with some of the work I'm doing and going to be authoring in the future. Hence 4 stars for helping to bring audience awareness of the issues of thinking the problem is a human/social one. The world is - I agree - looking in the wrong direction.
I'm not sure that this is the absolute strongest argument that could be made. Thick and Thin data are a bit of a sad attempt to say analysis that is good versus stuff could be done by a 10 year old.
Still, this work dovetails with some of the work I'm doing and going to be authoring in the future. Hence 4 stars for helping to bring audience awareness of the issues of thinking the problem is a human/social one. The world is - I agree - looking in the wrong direction.
April 18, 2018
The point of not using big data and algorithms senselessly is important, but it's the only real point of the book. The author could have saved both the readers and himself a lot of time by conveying the same message in a blog post instead of reiterating it again and again for 200+ pages.
April 18, 2017
Anyone in business should consider this book an imperative read, however, if you are low on emotional intelligence the gist will be lost.
We are in a data driven world. Madsbjerg gives example after example of the power of connecting humanities to algorithms. Whether it's in stock purchasing, training leaders or even in hostage negotiation, adding in the human element wins.
You will even learn what phenomenology means!
Note: at the beginning of the book, he used several examples from companies he was training....didn't care for the blatant advertisement. Then he left it alone the rest of the book and used other people's work
We are in a data driven world. Madsbjerg gives example after example of the power of connecting humanities to algorithms. Whether it's in stock purchasing, training leaders or even in hostage negotiation, adding in the human element wins.
You will even learn what phenomenology means!
Note: at the beginning of the book, he used several examples from companies he was training....didn't care for the blatant advertisement. Then he left it alone the rest of the book and used other people's work
May 6, 2021
A well-constructed critique of the current fascination with STEM and a inspiring call to the humanities. I appreciated the scorching review of Design Thinking—a reminder of the importance of ethnographic inquiry.
September 23, 2023
A reasonable study of fostering our humanity, in addition to simply relying on technology. Some of the examples used are useful, and could be applied broadly.
August 16, 2018
This is a book that argues in favor of applying a humanist perspective based on phenomenology to make sense of the complex problems of the world, and against relying on STEM-based approaches. Madsbjerg trots out specious and hoary arguments against such approaches. In his introduction he indicates that:
I believe that Madsbjerg is right when he claims that there are still people in important places that cling onto misguided reductionist, quantitative perspectives and need help in grasping the “bigger picture” but I also believe that there are still people who cling onto no less misguided fuzzy, intuitive perspectives and need help grounding them in reality. Bashing strawmen you set up may be emotionally rewarding but it must not be confused with serious argumentation. Critical thinking may be in shorter supply than what we need, but it is required in STEM, in the Humanities, in Art and in any attempt to integrate them. Researchers working with “big data” strive to find interesting, useful patterns in that data; whether “sense” is an emergent property of certain connectionist models applied to rich data sets is an interesting question in its own right. Remember however that for decades now, statisticians have been routinely warning their students not to confuse correlation with causality -Spurious correlations is a hilarious book based on a well-known website which helps drive home this point.
Madsbjerg’s foward starts by presenting several interesting examples of people so engrossed in the trees, they cannot see the forest. He then takes an unfair swipe at a caricature of STEM and big data and makes his clarion call on behalf of “critical thinking”.
In his introduction, The Human Factor, he again tells a rather lopsided story:
In his chapter on Crearivity, -not Manufacturing, Madsbjerg also suprisingly and violently lashes out against “design thinking” (Design Thinking: The anatomy of a bullshit tornado) and is particularly cutting in his comments about IDEO -to an unkind outsider this seems reminiscent of the pot calling the kettle black or the bitter feuds between closely related schools of thought, scathingly portrayed by Swift in his account of little-endians and big-endians in Lilliput…. The chapter is a close runner-up to the worst chapter in the book, not only for the gratuitious and unfair onslaught on design thinking but also for the mocking and frankly unnecessary section on an expert charlatan (Martin solves the problems).
The final chapter, What are people for?, gracefully ends the book.
To wrap up, this is a very uneven book, interspersing frankly misleading or unfair material as well as some interesting insights and ideas that should have been better served had they been developed in more detail. To be read with more than a pinch of salt.
This is a book about people. More specifically, this is a book about culture and the pendulum shifts of our age. Today we are so focused on STEM-based knowledge -theories from science, technology, engineering and math- and the abstractions of “big data”- that alternative frameworks for explaining reality have been rendered close to obsolete.Yes, it is true that some writers get carried away and extravagantly claim that blind big data mining -this used to be called senseless and obsessive number crunching- can provide answers to important questions. This reminds me of The Hitchhiker’s Guide to the Galaxy (1979) where Douglas Adams brilliantly satirized such claims, when he wrote that after 7.5 million years of computations, Deep Thought found that the “Ultimate Answer to Life, the Universe and Everything” was 42... However a glimpse at the 2017 National Academies of Science, Engineering an Medicine’s “Committee on Integrating Higher Education in the Arts, Humanities, Sciences, Engineering and Medicine” report on The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education: Branches from the Same Tree clearly shows that the current focus on STEM is not only not opposed to other frameworks for explaining reality, but, at least in some mainstream quarters is actively working to integrate them. Madsbjerg ‘s stirring:
If we truly want to make sense of our challenges, we must return to a a process that feels old-fashioned and out of date in today’s anesthetizing world of algorithmic promise. It’s something that has been sorely lacking in all of our organizations and across all aspects of our civic discourse. It’s called critical thinking.falls somewhat flat in the light of one of the committee’s least surprising findings:
Surveys show that employers value graduates who have both technical depth in a given discipline and cross-cutting “twenty-first century” skills and knowledge, such as critical thinking, communications skills, the ability to work well in teams, ethical reasoning, and creativity.So, is Madsbjerg barking up the wrong tree?
I believe that Madsbjerg is right when he claims that there are still people in important places that cling onto misguided reductionist, quantitative perspectives and need help in grasping the “bigger picture” but I also believe that there are still people who cling onto no less misguided fuzzy, intuitive perspectives and need help grounding them in reality. Bashing strawmen you set up may be emotionally rewarding but it must not be confused with serious argumentation. Critical thinking may be in shorter supply than what we need, but it is required in STEM, in the Humanities, in Art and in any attempt to integrate them. Researchers working with “big data” strive to find interesting, useful patterns in that data; whether “sense” is an emergent property of certain connectionist models applied to rich data sets is an interesting question in its own right. Remember however that for decades now, statisticians have been routinely warning their students not to confuse correlation with causality -Spurious correlations is a hilarious book based on a well-known website which helps drive home this point.
Madsbjerg’s foward starts by presenting several interesting examples of people so engrossed in the trees, they cannot see the forest. He then takes an unfair swipe at a caricature of STEM and big data and makes his clarion call on behalf of “critical thinking”.
In his introduction, The Human Factor, he again tells a rather lopsided story:
We humans have been getting some bad press lately. Not a day goes by without hearing about how irrational or inefficient we are when compared with machines. Next to our sleek silicon-powered computer counterparts, our brains are sluggish and burdened by emotions. [...]We need to learn through experience, and what we learn doesn’t have the same precision, rigor, or consistency as algorithms.If this were true, why are researchers so excited about programming robots with “emotions”? Why indeed are neural networks and deep learning, systems which needs to learn through experience, such hot areas of research in Artifical Intelligence? It is simply not true as he claims, that in engineering circles “the human factor […] is another way of saying the capacity for error”. If you read the introduction quickly, what will stick in your mind are phrases like:
...The solution to the human problem seems straightforward. If we want to remain useful – and employed – we should cede territory to the algorithms all around us – even become subservient to them.but if you read it more carefully you will notice that, perhaps a little grudgingly, Madsbjerg is actually arguing for humanities + STEM:
[…]
At the most prestigious universities in the United States, liberal arts fields like English and history used to be among the most popular majors, but a surge in interest in engineering and the natural sciences has decimated many humanities departments.
[…]
A humanities-based understanding of different people and their worlds is now officially useless. After all, compared to the endless information accessible through big data, what value is there in human-led cultural inquiry?
[…]
Too many of the top cadre of leadership I have met are isolated in their worldview. They have lost touch with the humanity of their customers and their constituents and, as a result, they mistake numerical representations and models for real life.
[…]
[F]ixation with hard data often masks stunning deficiencies, and many such lower-level managers will hit a glass ceiling in today’s business world. They are reductionists without the sensitivity to recognize the most exciting and essential patterns.
After nearly twenty years of counseling the very top executives and management around the world, I can tell you that the most successful leaders are curious, broadly educated people who can read both a novel and a spreadsheet.In the first chapter the author briefly explains what he means by his practice of sensemaking. Although the author would eschew the term the term, this practice is a design practice based on ethnographical studies of concrete human experience and based on five “principles”:
[A] former CEO of Procter & Gamble, had one single piece of advice for achieving business success in today’s complex managerial environment: pursue a degree in the liberal arts. “By studying art, science, the humanities, social science, and languages […] the mind develops the mental dexterity that opens a person to new ideas, which is the currency for success in a constantly changing environment….”
1. Culture - not individuals;After perhaps the worst chapter (Silicon Valley is a State of Mind) in the book in which the author indulges himself by knocking over flimsy versions of the assumptions underlying disruptive innovations, big data, and frictionless technology, he devotes a chapter to each of the five principles mentioned above. There are some interesting insights in some of these uneven chapters, but in general he hovers over rather than grapples with the principles., plops in some interesting anecdotal evidence usually based on his consulting work and provides some very slapdash pointers to semiotics, discourse theory, Niklas Luhmann’s social systems theories, Ervin Goffman dramaturgical analysis, Marshall Sahlin’s anthropological theories of reciprocity, and Wittgenstein’s theory of language, Heidegger’s philosophical ideas as examples of pointers that help bolster Madsbjerg’s claim of the importance of sociological, anthropological, language and philosophical theories to real-life consulting work.
2. Thick data -not just thin data;
3. The savannah -not the zoo [by which he means study the problem in its natural surroundings, not just in the lab]
4. Creativity -not manufacturing [by which he means engaging in a mindset that searches for insights and breakthroughs and steering clear of a “business as usual”mindset]
5. The North Star -not GPS […] learn to navigate through the rich reality of our world, developing a finely honed perspective on where we are, and where we are headed.
In his chapter on Crearivity, -not Manufacturing, Madsbjerg also suprisingly and violently lashes out against “design thinking” (Design Thinking: The anatomy of a bullshit tornado) and is particularly cutting in his comments about IDEO -to an unkind outsider this seems reminiscent of the pot calling the kettle black or the bitter feuds between closely related schools of thought, scathingly portrayed by Swift in his account of little-endians and big-endians in Lilliput…. The chapter is a close runner-up to the worst chapter in the book, not only for the gratuitious and unfair onslaught on design thinking but also for the mocking and frankly unnecessary section on an expert charlatan (Martin solves the problems).
The final chapter, What are people for?, gracefully ends the book.
To wrap up, this is a very uneven book, interspersing frankly misleading or unfair material as well as some interesting insights and ideas that should have been better served had they been developed in more detail. To be read with more than a pinch of salt.
December 22, 2020
Oivallinen, ajatuksia herättävä kirja! Määräisin pakolliseksi luettavaksi erityisesti kuluttajabisneksessä toimiville ja kaikille, jotka uskovat numeerisen datan autuaaksi tekevään voimaan.
Madsbjergin pääpointtina on, että hänestä yritysten päätöksenteossaan käyttämä data on usein aivan liian yksipuolista, liian yksinkertaistettua, abstrahoitua ja atomisoitua. Dataa, jossa ihmiset on redusoitu erillisiksi, numeerisesti mitattaviksi yksiköiksi, kun todellisuudessa ihmisten käyttäytyminen on aina kontekstisidonnaista. Kultuurilliset rakenteet ohjaavat ja muovaavat meitä. Ajatteluamme, arvostuksenkohteitamme ja käyttäytymistämme.
Abstrakti data vieraannuttaa yritykset asiakkaidensa todellisuudesta. Tämä todellisuus on kuitenkin tunnettava ja ymmärrettävä syvällisesti, jotta on mahdollista kehittää puhuttelevia ja merkityksellisiä ratkaisuja. Big datan rinnalle tarvitaan syvällistä ja tiheää dataa, joka selittää miksi jotain tapahtuu tai miksi joku asia koetaan tietyllä tavalla . Tarvitaan kokonaisvaltaista ymmärtämistä, sensemakingia, tolkuntekoa. Ihmistieteillä olisikin Madsbjergin mukaan valtavan paljon annettavaa. Esimerkiksi antropologia, filosofia, sosiologia, historia ja psykologia tarjoavat työkaluja ja malleja ihmisen ymmärtämiseen.
Madsbjerg konkretisoi näkemystään kiinnostavien esimerkkien kautta.
Miksi Lehman Brothers kaatui? Jos olisi tehty edes pari päivää kenttätutkimusta ja jalkauduttu asiakkaiden maailmaan, olisi taatusti selvinnyt, että asuntolainoja on myönnetty hälyttävän paljon ihmisille, jotka eivät koskaan kykenisi maksamaan niitä takaisin. Sen sijaan hyödynnettiin abstraktia numerodataa, joka epäonnistui ennustamaan mitä on tapahtumassa.
Madsbjeg on tehnyt töitä Fordin kanssa ja käyttää Fordia esimerkkinä useammankin kerran kirjassaan. Fordilla ymmärrettiin, mitä siellä ei ymmärretä. On mahdotonta suunnitella jotain merkityksellistä ihmisille, joiden maailmaa, kulttuuria ja todellisuutta ei tunneta. Jos oma autonkuljettaja on vahva statussymboli Intiassa, kuinka kiinnostavaksi koetaan autonomisesti ajava auto?
Eräässä projektissa Fordilla lähdettiinkin liikkeelle siitä, että pyritään ensin ymmärtämään mitä luksus merkitsee eri ihmisille eri kulttuureissa. Mitä se on ilmiönä. Millaista oikeastaan on ylellisyyden kokeminen? Tutkimusote oli antropologinen. Tutkijat seurasivat tutkittavan henkilön elämää pidemmän aikaa ja pyrkivät ymmärtämään tämän maailmaa ja kulttuuria mahdollisimman kokonaisvaltaisesti. Vasta kun todella oli oivallettu mitä on ylellisyyden kokeminen ja mikä on sen perusta, suunniteltiin ratkaisuja, jotka tukisivat tätä kokemusta parhaalla mahdollisella tavalla.
Oman osansa Madsbjergin kritiikistä saavat design ajattelu ja jargonia suoltavat designerit. Pakko sanoa, että Madsbjergin näkemys designereista on kyllä turhan stereotyyppinen. Hän kritisoi design ajattelua erityisesti siitä, että designerit väittävät, että substanssiosaaminen ja toimialatuntemus on tarpeetonta, jopa rajoittavaa ja että haasteita kannattaa lähestyä freesillä ajattelulla. En ole itse koskaan mieltänyt design ajattelun jotenkin dissaavan substanssia. Päinvastoin kontekstin, rajoitteiden ja eri sidosryhmien tarpeiden ymmärtämistä pidetään välttämättömänä. Ilman lähtötilanteen syvällistä hahmottamista ei ole mahdollista tunnistaa mikä on merkityksellistä ja mihin kannattaa keskittyä.
On toki totta ja vähän pelottavaakin miten erilaiset "tyhjiöissä" tapahtuvat hackathonit ja sprintit halutaan nähdä helppoina ja nopeina hopealuoteina, joilla olisi muka mahdollista ratkaista perustavanlaatuisia, vaikkapa jonkun instituution perustehtävään ja olemassaoloon liiittyviä kysymyksiä. Mieleen tulee hilpeä metafora hackathoneista eväsretkenä kesäisessä puistossa kun todellinen innovaatio on sitä, että yrität valloittaa Venäjää talvella.
Se on kyllä valitettavan totta, että projekteissa ei ole juuri koskaan mahdollista uppoutua riittävän syvällisesti ja kokonaisvaltaisesti ilmiöihin, joiden kontekstissa ratkaisut toimivat. En kuitenkaan koe, että se johtuisi design ajattelusta sinänsä. Enemminkin ei ole onnistuttu syystä tai toisesta perustelemaan miksi tutkimukseen kannattaa panostaa. Ideaalitapauksessa olisi aina mahdollista syventyä ja hyödyntää triangulaatiota.
Madsbjergin pääpointtina on, että hänestä yritysten päätöksenteossaan käyttämä data on usein aivan liian yksipuolista, liian yksinkertaistettua, abstrahoitua ja atomisoitua. Dataa, jossa ihmiset on redusoitu erillisiksi, numeerisesti mitattaviksi yksiköiksi, kun todellisuudessa ihmisten käyttäytyminen on aina kontekstisidonnaista. Kultuurilliset rakenteet ohjaavat ja muovaavat meitä. Ajatteluamme, arvostuksenkohteitamme ja käyttäytymistämme.
Abstrakti data vieraannuttaa yritykset asiakkaidensa todellisuudesta. Tämä todellisuus on kuitenkin tunnettava ja ymmärrettävä syvällisesti, jotta on mahdollista kehittää puhuttelevia ja merkityksellisiä ratkaisuja. Big datan rinnalle tarvitaan syvällistä ja tiheää dataa, joka selittää miksi jotain tapahtuu tai miksi joku asia koetaan tietyllä tavalla . Tarvitaan kokonaisvaltaista ymmärtämistä, sensemakingia, tolkuntekoa. Ihmistieteillä olisikin Madsbjergin mukaan valtavan paljon annettavaa. Esimerkiksi antropologia, filosofia, sosiologia, historia ja psykologia tarjoavat työkaluja ja malleja ihmisen ymmärtämiseen.
Madsbjerg konkretisoi näkemystään kiinnostavien esimerkkien kautta.
Miksi Lehman Brothers kaatui? Jos olisi tehty edes pari päivää kenttätutkimusta ja jalkauduttu asiakkaiden maailmaan, olisi taatusti selvinnyt, että asuntolainoja on myönnetty hälyttävän paljon ihmisille, jotka eivät koskaan kykenisi maksamaan niitä takaisin. Sen sijaan hyödynnettiin abstraktia numerodataa, joka epäonnistui ennustamaan mitä on tapahtumassa.
Madsbjeg on tehnyt töitä Fordin kanssa ja käyttää Fordia esimerkkinä useammankin kerran kirjassaan. Fordilla ymmärrettiin, mitä siellä ei ymmärretä. On mahdotonta suunnitella jotain merkityksellistä ihmisille, joiden maailmaa, kulttuuria ja todellisuutta ei tunneta. Jos oma autonkuljettaja on vahva statussymboli Intiassa, kuinka kiinnostavaksi koetaan autonomisesti ajava auto?
Eräässä projektissa Fordilla lähdettiinkin liikkeelle siitä, että pyritään ensin ymmärtämään mitä luksus merkitsee eri ihmisille eri kulttuureissa. Mitä se on ilmiönä. Millaista oikeastaan on ylellisyyden kokeminen? Tutkimusote oli antropologinen. Tutkijat seurasivat tutkittavan henkilön elämää pidemmän aikaa ja pyrkivät ymmärtämään tämän maailmaa ja kulttuuria mahdollisimman kokonaisvaltaisesti. Vasta kun todella oli oivallettu mitä on ylellisyyden kokeminen ja mikä on sen perusta, suunniteltiin ratkaisuja, jotka tukisivat tätä kokemusta parhaalla mahdollisella tavalla.
Oman osansa Madsbjergin kritiikistä saavat design ajattelu ja jargonia suoltavat designerit. Pakko sanoa, että Madsbjergin näkemys designereista on kyllä turhan stereotyyppinen. Hän kritisoi design ajattelua erityisesti siitä, että designerit väittävät, että substanssiosaaminen ja toimialatuntemus on tarpeetonta, jopa rajoittavaa ja että haasteita kannattaa lähestyä freesillä ajattelulla. En ole itse koskaan mieltänyt design ajattelun jotenkin dissaavan substanssia. Päinvastoin kontekstin, rajoitteiden ja eri sidosryhmien tarpeiden ymmärtämistä pidetään välttämättömänä. Ilman lähtötilanteen syvällistä hahmottamista ei ole mahdollista tunnistaa mikä on merkityksellistä ja mihin kannattaa keskittyä.
On toki totta ja vähän pelottavaakin miten erilaiset "tyhjiöissä" tapahtuvat hackathonit ja sprintit halutaan nähdä helppoina ja nopeina hopealuoteina, joilla olisi muka mahdollista ratkaista perustavanlaatuisia, vaikkapa jonkun instituution perustehtävään ja olemassaoloon liiittyviä kysymyksiä. Mieleen tulee hilpeä metafora hackathoneista eväsretkenä kesäisessä puistossa kun todellinen innovaatio on sitä, että yrität valloittaa Venäjää talvella.
Se on kyllä valitettavan totta, että projekteissa ei ole juuri koskaan mahdollista uppoutua riittävän syvällisesti ja kokonaisvaltaisesti ilmiöihin, joiden kontekstissa ratkaisut toimivat. En kuitenkaan koe, että se johtuisi design ajattelusta sinänsä. Enemminkin ei ole onnistuttu syystä tai toisesta perustelemaan miksi tutkimukseen kannattaa panostaa. Ideaalitapauksessa olisi aina mahdollista syventyä ja hyödyntää triangulaatiota.
September 29, 2020
I honestly had high hopes for this book just based on the description. The last humanities class I have taken was back in high school. I have since gotten a Masters degree and work at a Liberal Arts college where I questioned whether humanities classes or majors were a good idea since data shows that STEM fields generally lead to higher paying jobs, or just jobs in general related to the material studied. With increasing student debt, I also questioned why or even how students were able to have these majors. Throughout my time working, I have changed my view point slightly, and do see the value of being able to think more broadly.
However, this book did not give me any new ideas. If anything, there were a lot of references to other people or books that I have read, such as Chris Voss's negotiation skills based on extraordinary observation skills and Daniel Kahneman's behavioral economics. The first few chapters it just seemed like the author was really trying to put down data and STEM fields for being very closed minded and following set rules, but that has not been my experience at all. For any algorithm to work, creativity does need to be included, the same with the sciences, maths, engineering, and technology. Unfortunately for the author, there have been algorithms that have been able to generate articles or even stories that have been liked by people. If there is a way to think through the process of an action, or even explain a pattern, and algorithm can be written, such as how the author interprets how on teacher connects with her students. A lot of the time, this felt like a lecture or textbook in the sense that I would read a few paragraphs, and not remember what I had just read. There were a lot of interesting examples of where creativity was used to ask different questions of the intended customers/audience, but that still resulted in data being collected. Sciences, even the hard/physical sciences, do teach the difference between quantitative and qualitative data, and why they are both important. It is just that currently, our human species seems to be more interested in the quantitative because of how far it seems to be getting us as a society.
However, this book did not give me any new ideas. If anything, there were a lot of references to other people or books that I have read, such as Chris Voss's negotiation skills based on extraordinary observation skills and Daniel Kahneman's behavioral economics. The first few chapters it just seemed like the author was really trying to put down data and STEM fields for being very closed minded and following set rules, but that has not been my experience at all. For any algorithm to work, creativity does need to be included, the same with the sciences, maths, engineering, and technology. Unfortunately for the author, there have been algorithms that have been able to generate articles or even stories that have been liked by people. If there is a way to think through the process of an action, or even explain a pattern, and algorithm can be written, such as how the author interprets how on teacher connects with her students. A lot of the time, this felt like a lecture or textbook in the sense that I would read a few paragraphs, and not remember what I had just read. There were a lot of interesting examples of where creativity was used to ask different questions of the intended customers/audience, but that still resulted in data being collected. Sciences, even the hard/physical sciences, do teach the difference between quantitative and qualitative data, and why they are both important. It is just that currently, our human species seems to be more interested in the quantitative because of how far it seems to be getting us as a society.
September 19, 2021
As a software engineer who has worked for many years I felt this book was quite eye opening for me! I realized that big data and ML can never replace human intelligence ... But for humans to apply intelligence in the age of algorithms we need to understand culture, context and philosophy ... In other words humanities studies is important if you need to grow in your career!! The author does make some controversial statements like dissing the whole silicon valley or ridiculing design thinking as a process for manufacturing creativity with no cultural context or subject matter expertise ... But still I quite enjoyed reading this book and hopefully apply these in my career!!
August 27, 2017
Sensemaking attempts to make Heidegger digestible and relevant for modern decision-makers. The central argument is that context is everything when it comes to understanding and predicting collective human behavior. Our cultures - religion, norms, values - determine our behavior. Our independent will is largely an illusion. This view sets the stage for an argument that literature, history, sociology etc are vastly more important domains for understanding context and human actions than are "data and algorithms." The book is worthwhile for anyone who is unconvinced that data are not the key to every insight.
January 7, 2018
En bog til virksomhedsledere og måske allermest til vores regering og offentlige administration, der efterhånden tror, at man kan lade hjernen ligge på hylden og basere alt på "big data". Sensemaking betyder, at man bruger sit intellekt, sin viden, sin uddannelse til at vurdere de problemer, man møder. Data er selvfølgelig en stor hjælp i mange sammenhænge, men hvis man ikke OGSÅ bruger sin hjerne, sin viden, sin indsigt, sin empati, så kan data have en masse indbyggede fejl - ikke mindst fordi data er bagudskuende og fyldt med bias.
August 12, 2022
This book should be required reading for those of us involved in the humanities, especially within higher education. The general idea of making humanities work more relevant, particularly as it relates to technology (and in this book's case, artificial intelligence), is a point that deserves our consideration. The book sometimes rests on cases that feel a little too easy, which is often the case of books like this. However, there is a genuine need for these kinds of conversations, and I can admire how this book furthers the discussion of humanities in a specific, relevant way.
May 26, 2019
I enjoyed the early part of this book tremendously. The discussions about how big data only makes sense in context and the difficulty of making good decisions in business when you are removed from the environment in which that decision needs to be made were common sense but still fascinating. However, as the book progressed I found it became a little repetitive and I felt that the author moved away from evidence-based discussion to personal opinion.
May 14, 2023
Madsbjerg questions: “What if we are trying to understand [a person] in a completely different cultural context… inductive reasoning will shut out possible insights before we even know the context of our investigation.” (p.19). He makes it abundantly clear that he values the presence of context, but if we take a step back and analyze the context of his work as a whole, a plausible argument becomes clear. As previously stated, Madsbjerg works for a consulting company based in the human sciences, and the entirety of revenue generated for consulting jobs is service based. The more clients that he can get, the more money he makes. In the sentence “the humanities aren’t a luxury; they are your competitive advantage” (p.208), he is provoking the reader to consider humanities as an advantage in their own life. This is known as the illusory truth effect: when something is repeated multiple times, we start to think that it is true. This book is essentially an advertisement for his own company, and readers need to recognize the context of Madsbjerg’s bias to critique his work - something that he repeatedly enforces throughout Sensemaking.
Madsbjerg continuously uses pointless examples that not only confuse the reader, but end up contradicting his own thesis. Information overload is a problem with quantitative information, which Madsbjerg continues to repeat throughout his work. He states that “today’s world feels overwhelmingly complex because we are obsessed with organizing it as an assembly of facts.” (p.22). Yet, he continues to input confusing jargon and complex examples filled with data throughout his book in an attempt to further his thesis. One story that really stuck out to me was the story of Nicole Pollientier, and her experience of her brain injury and her journey to reignite her love for poetry by making a tamale. As inspiring and heartwarming the story was, its relevance to sensemaking as a whole was non-existent. He explains her story, and argues that her case shows how we “learn, think and live in worlds” (p.55). He states: “When [Nicole] reaches out for the olive oil to her left and the scrap of paper to her right, her actions are summoned from deep within her by the social context.” (p.55). What social context is Madsbjerg referring to? The social context of making her tamales? Of recovering from her brain injury? It remains ambiguous. He then proceeds to poorly transition into the work of Hubert Dreyfus and his “phenomenology of skill that directly challenges computational theory of mind.” (p.55). All three of those ideas were combined on the same page. He spends the rest of the chapter rambling on about human intelligence, rarely tying his ideas together. What does human intelligence have to do with remembering how to write poetry after a brain injury? One is then left to interpret that information, and with the amount of content and examples in this book, this comes full circle to the presence of information overload.
The poor execution of, not only Madsbjerg’s writing style, but his lack of clarity proves my argument of why this is not a ‘ground-breaking’ piece of writing. With respect to the racist allegations surrounding him and his company, Madsbjerg becomes a discreditable author when discussing culture. If one cannot recognize race as a part of culture, they should not be credible enough to discuss culture as a whole. The overall contradictions within Madsbjerg’s writings make the book an overall poor read from both an academic and a personal aspect.
Madsbjerg continuously uses pointless examples that not only confuse the reader, but end up contradicting his own thesis. Information overload is a problem with quantitative information, which Madsbjerg continues to repeat throughout his work. He states that “today’s world feels overwhelmingly complex because we are obsessed with organizing it as an assembly of facts.” (p.22). Yet, he continues to input confusing jargon and complex examples filled with data throughout his book in an attempt to further his thesis. One story that really stuck out to me was the story of Nicole Pollientier, and her experience of her brain injury and her journey to reignite her love for poetry by making a tamale. As inspiring and heartwarming the story was, its relevance to sensemaking as a whole was non-existent. He explains her story, and argues that her case shows how we “learn, think and live in worlds” (p.55). He states: “When [Nicole] reaches out for the olive oil to her left and the scrap of paper to her right, her actions are summoned from deep within her by the social context.” (p.55). What social context is Madsbjerg referring to? The social context of making her tamales? Of recovering from her brain injury? It remains ambiguous. He then proceeds to poorly transition into the work of Hubert Dreyfus and his “phenomenology of skill that directly challenges computational theory of mind.” (p.55). All three of those ideas were combined on the same page. He spends the rest of the chapter rambling on about human intelligence, rarely tying his ideas together. What does human intelligence have to do with remembering how to write poetry after a brain injury? One is then left to interpret that information, and with the amount of content and examples in this book, this comes full circle to the presence of information overload.
The poor execution of, not only Madsbjerg’s writing style, but his lack of clarity proves my argument of why this is not a ‘ground-breaking’ piece of writing. With respect to the racist allegations surrounding him and his company, Madsbjerg becomes a discreditable author when discussing culture. If one cannot recognize race as a part of culture, they should not be credible enough to discuss culture as a whole. The overall contradictions within Madsbjerg’s writings make the book an overall poor read from both an academic and a personal aspect.
June 13, 2019
Refreshing to see a book promoting a sensible approach towards integrating data driven approaches to problem solving along with narrative/story driven methods as opposed to idolizing big data and machine learning as some sort of ultimate destination, which sadly is not exactly uncommon these days. In the age of measurement and algorithms it is taken for granted that objective facts must always be valued over subjective feelings and intuitions. The human element is often seen as an obstacle towards achieving complete clarity. But if we look deeper, we see that stories are powerful and human narratives allow us to uncover different truths about the world we live in. The reason is that data driven approaches ignore context in an effort to scale the solution whereas stories don't. And context can make a big difference in determining whether we can understand what is really going.
I liked the real-world examples on how the de-contextualizing effect of data driven methods (that operate by choosing a set of metrics to measure and ignore completely the context in which the phenomenon occurs) lead to suboptimal decision in industries, politics, and the sciences. The author convincingly argues that algorithms that operate on data (at least the ones we know of till date) are often only good for making progress of a very specific kind--incremental progress, whereas the power of the narrative is that it celebrates context and allows us to uncover the underlying, deeper human motivations behind the problem at hand and thereby paves the way towards progress is that game-changing or "disruptive". The quote attributed to Henry Ford about people wanting faster horses comes to mind.
Some of the chapters are really well-written whereas there were a few odd ones which read like a strawman bash-fest that were less interesting to read.
I liked the real-world examples on how the de-contextualizing effect of data driven methods (that operate by choosing a set of metrics to measure and ignore completely the context in which the phenomenon occurs) lead to suboptimal decision in industries, politics, and the sciences. The author convincingly argues that algorithms that operate on data (at least the ones we know of till date) are often only good for making progress of a very specific kind--incremental progress, whereas the power of the narrative is that it celebrates context and allows us to uncover the underlying, deeper human motivations behind the problem at hand and thereby paves the way towards progress is that game-changing or "disruptive". The quote attributed to Henry Ford about people wanting faster horses comes to mind.
Some of the chapters are really well-written whereas there were a few odd ones which read like a strawman bash-fest that were less interesting to read.
July 9, 2024
I've been reading through a series of books related to the role of AI in the workplace, and since technology is advancing so rapidly, I find it important to note when a book was published - in this case, 2017. I note this because the author is skeptical that AI can replace some of the most creative human jobs, and yet that is what appears to be coming quickly as I write this review in 2024. That said, I do find the book offers some valuable reminders and/or counterpoints to the arguments in favor of an AI-filled future. Most importantly, he argues that algorithms, or even advanced AI learning models, don't care about people, and that only people can truly understand society and psychology and what it means to be human. Toward that end, the recommendations he provides in this book are intended to double down on those things that make us most human. Rather than relying on pure metrics or market research, he stresses the importance of ethnographic research and sociological understanding, arguing that unless we understand the cultural context for any data we are evaluating, we could easily be led to the wrong conclusions. While I appreciate the emphasis on the humanities in business, I do feel that he went off on a bit of a diatribe about "design thinkers", offering up a caricature of the self-important designer in a silicon valley stereotype. Having read extensively and used design thinking in my work, I feel that the best of design thinking starts with quality ethnographic research and a solid sociological and cultural understanding of the user, so I don't think these two approaches are at odds with each other. Despite this small gripe, I did find the book helpful, especially as a reminder of what is most important. In a future defined by AI, we need to be human above all.
May 1, 2023
Reminds you to Look. Observe. Don’t think [too much].
It’s easy to get lost in the sea of big data. But what does it all mean? Who actually understands all that information and more importantly, can do something with it!
Sensemaking reminds you that at the end of the day- being immersed in what people are doing, and how /why they are doing, being present to observe all that is what really should be the guiding light for decisions. He refers to this kind of data as thick, very ethnographic based.
I appreciated the balance of taking a different look at design thinking (a chapter ). All the brainstorming and sprints and such can be good, but…. And I have found that practice helpful in that it got me unstuck from my fear of testing hunches. The Just go do! motto.
But, when you’re tapped out and can’t read the signals or have info you don’t know what to do with- most likely some data, even the thought of going into sprint mode is pointless and so stressful (talk about creators block!). Sensemaking says to spend time observing, immersing, look at all the influences effecting who your target is and then let it strew, take a break, walk away. The mind will surface gems.
He sites an example of an annuity firm. The target audience was pulling out and going elsewhere. When the company immersed themselves into the daily lives of their target, so much was surfaced. What the analysts could never show was the all the human fears and worries the intended audience had. ROI and KPI would never uncovered.
It’s easy to get lost in the sea of big data. But what does it all mean? Who actually understands all that information and more importantly, can do something with it!
Sensemaking reminds you that at the end of the day- being immersed in what people are doing, and how /why they are doing, being present to observe all that is what really should be the guiding light for decisions. He refers to this kind of data as thick, very ethnographic based.
I appreciated the balance of taking a different look at design thinking (a chapter ). All the brainstorming and sprints and such can be good, but…. And I have found that practice helpful in that it got me unstuck from my fear of testing hunches. The Just go do! motto.
But, when you’re tapped out and can’t read the signals or have info you don’t know what to do with- most likely some data, even the thought of going into sprint mode is pointless and so stressful (talk about creators block!). Sensemaking says to spend time observing, immersing, look at all the influences effecting who your target is and then let it strew, take a break, walk away. The mind will surface gems.
He sites an example of an annuity firm. The target audience was pulling out and going elsewhere. When the company immersed themselves into the daily lives of their target, so much was surfaced. What the analysts could never show was the all the human fears and worries the intended audience had. ROI and KPI would never uncovered.
May 13, 2017
Sensemaking: The Power of the Humanities in the Age of the Algorithm is a noble attempt and pushback at showing how thinking and practices dominated by skills grounded in the broad humanities allows one to understand the context and uses of what is commonly known as "big data".
Madsbjerg, a Danish business consultant, does a fine job of showing how the liberal arts is helping corporations like Ford and notable architects, derive meaning from all its gathered data about consumer habits. He strongly points out a single reliance on the information will, by nature lead the producers and managers of the world away from understandings of how their customers and users are humans and not producers on content and information.
I do wish he had delved into how a life trained in the liberal arts, with its emphasis on precedent and experience, context and broad reading and gathering main theses and showing the inner connectedness of information has worked so well for people.
His concluding thoughts of asking "what are people for", echoing Kentucky writer Wendell Berry, hits on this book's greatest points, that people are meant to connect with each other and not simply bounce content and be independent data collectors off each other. Madsbjerg's contribution here is showing how he and others are helping organizations get unstuck to unleash their energies, rather than simply be led by stunted data conclusions.
Madsbjerg, a Danish business consultant, does a fine job of showing how the liberal arts is helping corporations like Ford and notable architects, derive meaning from all its gathered data about consumer habits. He strongly points out a single reliance on the information will, by nature lead the producers and managers of the world away from understandings of how their customers and users are humans and not producers on content and information.
I do wish he had delved into how a life trained in the liberal arts, with its emphasis on precedent and experience, context and broad reading and gathering main theses and showing the inner connectedness of information has worked so well for people.
His concluding thoughts of asking "what are people for", echoing Kentucky writer Wendell Berry, hits on this book's greatest points, that people are meant to connect with each other and not simply bounce content and be independent data collectors off each other. Madsbjerg's contribution here is showing how he and others are helping organizations get unstuck to unleash their energies, rather than simply be led by stunted data conclusions.
March 7, 2021
I agree with Madsbjerg, " Celestial navigation provides an apt metaphor for leadership in today’s organizations and companies. Instead of simply reacting to one type of data, it is the role of the leader to make sense of all data: to interpret the facts available from multiple sources—technical and human—and to develop a strategy accordingly".
If you agree, Madsbjerg offers deeper context on what is involved in sense making. Where data sets and reductionist thinking fail is illustrated in the many examples the author shares. Here's one:
" a cup of champagne at a loud party is a vastly different experience than receiving a champagne flute from a white gloved waiter at a fine restaurant. Yes, it is correct that both may be made with grapes originating from the same field in France or that both contain identical milligrams of yeast, but one experience will leave you feeling sloppy and raucous, while the other can enchant and elevate you. The difference between the two experiences is where we find truth."
Failure to uncover deeper insights and discern what else is unseen in our analysis is an active discovery process too often missing in visioning. Can we afford to ignore the larger ecosystem in which we make decisions? Madsbjerg advocates that without immersion in that ecosystem, deeper understanding that informs visioning is flawed (my words not his).
I read this book hoping to glean more about approaches to dealing with uncertainty. Lots of nuggets to chew on.
If you agree, Madsbjerg offers deeper context on what is involved in sense making. Where data sets and reductionist thinking fail is illustrated in the many examples the author shares. Here's one:
" a cup of champagne at a loud party is a vastly different experience than receiving a champagne flute from a white gloved waiter at a fine restaurant. Yes, it is correct that both may be made with grapes originating from the same field in France or that both contain identical milligrams of yeast, but one experience will leave you feeling sloppy and raucous, while the other can enchant and elevate you. The difference between the two experiences is where we find truth."
Failure to uncover deeper insights and discern what else is unseen in our analysis is an active discovery process too often missing in visioning. Can we afford to ignore the larger ecosystem in which we make decisions? Madsbjerg advocates that without immersion in that ecosystem, deeper understanding that informs visioning is flawed (my words not his).
I read this book hoping to glean more about approaches to dealing with uncertainty. Lots of nuggets to chew on.
June 7, 2017
Sensemaking is an interesting and thought-provoking book, which pinpoints the pivotal role of human sciences in driving not only optimal individual but also enterprise-wide performance. Mr. Madsbjerg describes vividly case studies that validate the conviction that human sciences can cultivate the essential perspective to solve business problems, and at the same time depicts the inefficiency of the “silicon valley state of mind” to confront and make sense of the non-linear changes of our times. The question in the title of the book (“What Makes Human Intelligence Essential in the Age of the Algorithm”) has come to the fore lately, either in the context of the rapid development of AI or in terms of the changes in human behavior and choice (as consumers or voters) and his book yields a rigorous answer
Mr. Madsbjerg asserts in his book that “…by studying art, science, the humanities, social science and languages the mind develops the mental dexterity that opens a person to new ideas, which is the currency for success in a constantly changing environment…”. Reading this book and contemplating on the ideas that are articulated in it, makes unambiguously sense of the above sentence.
Mr. Madsbjerg asserts in his book that “…by studying art, science, the humanities, social science and languages the mind develops the mental dexterity that opens a person to new ideas, which is the currency for success in a constantly changing environment…”. Reading this book and contemplating on the ideas that are articulated in it, makes unambiguously sense of the above sentence.
September 30, 2019
While doing my masters in Computer Science, I had not a single course on the softer mushy human and social sciences. So, while we learnt formal logic and how to write a compiler, or even design an expert system, unfortunately, we didn't learn almost anything about people, the human beings! Little much has changed in those thirty years. Even today, I continue to find armies of tech graduates year after year with practically no knowledge or appreciation of #humanities. And in the tech bubble that we techies live in, we hardly ever think of its "utility" - indeed, one would learn Python and R than reading "useless" theories of human learning or reading classics.
The result is that we can make sense out of a well-structured and bounded #complexity problem, but are ill-equipped to handle ill-structured and unbounded complexity problem. We know how to write code, but we still don't quite have a faintest idea on how to make sense out of everyday complexity. So, how does one go about #sensemaking? Are there mathematical formulas or sophisticated algorithms that could help? Fortunately, none of them help, and the only way we could hope to find a way is by looking up to humanities for help.
The result is that we can make sense out of a well-structured and bounded #complexity problem, but are ill-equipped to handle ill-structured and unbounded complexity problem. We know how to write code, but we still don't quite have a faintest idea on how to make sense out of everyday complexity. So, how does one go about #sensemaking? Are there mathematical formulas or sophisticated algorithms that could help? Fortunately, none of them help, and the only way we could hope to find a way is by looking up to humanities for help.
October 18, 2018
I have mixed feelings for this book.
Initially, I thought this book was going to explain how humanities and liberal arts can complement our new world of algorithms, machine learning and artificial intelligence, which would be really useful for my work. So I was quite disappointed when this was not what the book was trying to do.
This said, the book did cover some interesting topics on culture and sociology that still end up being a rather insightful and meaningful read. I particularly like how the book concluded on the use of humanities in the modern AI world.
I would say I didn't appreciate the author's critic at design thinking. I am a data analyst, and although I have some exposure to design thinking approaches, I have no vested interest defending design thinking in any way.
All in all, I was glad to have read this book because I felt that it did open up my mind to many new things beyond my work, which I have to admit, was a very narrow view to adopt for reading any book, at least not all the time.
Initially, I thought this book was going to explain how humanities and liberal arts can complement our new world of algorithms, machine learning and artificial intelligence, which would be really useful for my work. So I was quite disappointed when this was not what the book was trying to do.
This said, the book did cover some interesting topics on culture and sociology that still end up being a rather insightful and meaningful read. I particularly like how the book concluded on the use of humanities in the modern AI world.
I would say I didn't appreciate the author's critic at design thinking. I am a data analyst, and although I have some exposure to design thinking approaches, I have no vested interest defending design thinking in any way.
All in all, I was glad to have read this book because I felt that it did open up my mind to many new things beyond my work, which I have to admit, was a very narrow view to adopt for reading any book, at least not all the time.
October 22, 2020
Full review/key learnings on my website
In Sensemaking, Madsbjerg advocates for educating corporate decision-makers about the subtleties of the humanities and social sciences to future-proof progress for the greater good.
The premise of this book is about the unbalance in the world of business between STEM-based knowledge and the humanities. The author argues that if we bring more social sciences to decision-making, we can work with thick data —data infused with meaning by keeping it embedded in its original context— instead of thin data —abstracted values and numbers.
The practice of sensemaking is about pattern recognition. Pattern recognition is invaluable to extract any relevant insight. And to achieve that, the only way is to extensively read and consume culture and theories from all parts of history and from a variety of topics.
In Sensemaking, Madsbjerg advocates for educating corporate decision-makers about the subtleties of the humanities and social sciences to future-proof progress for the greater good.
The premise of this book is about the unbalance in the world of business between STEM-based knowledge and the humanities. The author argues that if we bring more social sciences to decision-making, we can work with thick data —data infused with meaning by keeping it embedded in its original context— instead of thin data —abstracted values and numbers.
The practice of sensemaking is about pattern recognition. Pattern recognition is invaluable to extract any relevant insight. And to achieve that, the only way is to extensively read and consume culture and theories from all parts of history and from a variety of topics.
"The humanities aren't a luxury; they're your competitive advantage."
February 8, 2022
Many debates and discussions get clouded with abstract numbers and statistics. Perhaps you’ve come across some know-it-all who dismissed your arguments with a lofty “Well, what the data actually shows is…”
There seems to be a growing tendency for algorithms and automated number crunching to be prioritized over philosophy- and humanities-based thinking. This skewed focus compromises education and business and has detrimental effects on society.
In these blinks, you’ll find out why we should instead turn to “sensemaking,” a better way to engage with the culture around us.
Sensemaking is about understanding human culture and the context in which it operates. In contrast with the data-driven way the natural sciences encourage us to interpret the world, sensemaking is rooted in the humanities and acknowledges the richness of human culture, of people’s stories, art, philosophy, and history.
There seems to be a growing tendency for algorithms and automated number crunching to be prioritized over philosophy- and humanities-based thinking. This skewed focus compromises education and business and has detrimental effects on society.
In these blinks, you’ll find out why we should instead turn to “sensemaking,” a better way to engage with the culture around us.
Sensemaking is about understanding human culture and the context in which it operates. In contrast with the data-driven way the natural sciences encourage us to interpret the world, sensemaking is rooted in the humanities and acknowledges the richness of human culture, of people’s stories, art, philosophy, and history.
June 2, 2019
Fascinating guy, but it took me a while to get into this book, and I was a bit frustrated by how the arguments didn’t seem to land as decisively as I was hoping for. They just kind of flowed on to the next and the next without good strong punctuation between them (at least that’s how I felt).
But the content is undeniably relevant for the big data obsessed world we live in at the moment. What resonated with me the most is how our education systems are churning out people with precisely all the skills that we know computers are better at (or going to be better at). Doesn’t it make a ton more sense to be educating and training people in those skills/areas where the human touch is still essential and most likely will be for quite a long time?
The last chapter with the case studies was my favourite. Skim that if you’re wondering whether to try your book.
But the content is undeniably relevant for the big data obsessed world we live in at the moment. What resonated with me the most is how our education systems are churning out people with precisely all the skills that we know computers are better at (or going to be better at). Doesn’t it make a ton more sense to be educating and training people in those skills/areas where the human touch is still essential and most likely will be for quite a long time?
The last chapter with the case studies was my favourite. Skim that if you’re wondering whether to try your book.
December 27, 2021
The book has some insightful stories behind the success of Henry Ford, George Sonos and so on.
The five principles of Sensemaking are:
1. Culture - not individual
2. Thick data - not just thin data
3. The savannah - not the zoo
4. Creativity - not manufacturing
5. The North Star - not the GPS
The point is that we should not just make decisions based on data and analytics but also need to consider the culture background, the insights laid under the data and analysis. I 100% agree with that. However, we should not overestimate the capabilities of intuition. Our brain is full of biases! Another reason I didn’t like this book is that Mr. Madsbjerg went too far in the criticism against the “silicon valley state of mind”. Want to know his points? Please read this book! It was an interesting read!
The five principles of Sensemaking are:
1. Culture - not individual
2. Thick data - not just thin data
3. The savannah - not the zoo
4. Creativity - not manufacturing
5. The North Star - not the GPS
The point is that we should not just make decisions based on data and analytics but also need to consider the culture background, the insights laid under the data and analysis. I 100% agree with that. However, we should not overestimate the capabilities of intuition. Our brain is full of biases! Another reason I didn’t like this book is that Mr. Madsbjerg went too far in the criticism against the “silicon valley state of mind”. Want to know his points? Please read this book! It was an interesting read!
December 19, 2017
The initial part, well almost 75% of the book, was not so exciting to me (interestingly, I pre-ordered this book and bought it). First, as a control theorist I am fully aware of all the technical matter behind the narration. Secondly, after having a heavy dose from Nassim Taleb (Fooled by Randomness and The Black Swan), the issues addressed here were largely repetitive. However, it is the last two chapters - one on navigation (although this has the flavour of Blink by Malcolm Gladwell) and the last one "What are People for?" - were written exceedingly well, and tipped my rating to 4 stars!
I believe it is time that we sensitize our industry as well as the governments about the need for research and development towards a holistic product rather than an optimal product.
I believe it is time that we sensitize our industry as well as the governments about the need for research and development towards a holistic product rather than an optimal product.
September 25, 2017
Brilliant book on decision-making in a data driven age. Sensemaking is about understanding human culture and the context in which it operates. It revolves around holistic thinking. See between the lines of data; understand not just what people buy but what these people buy.
Sensemaking is a relevant skill in the age of big data, and could be a competitive advantage in the age of AI. Humans can comprehend "thick data" better, that is the context of the data.
Important concepts for entrepreneurs:
1) Phenomenology: sensemaking based on experiencing not hypothizing. Experience things as they are, not as how we think they are.
2) Creative ideas begin with immersion and sensitivity. Henry Ford got his vision of famous factor line by a visit to a pig farm.
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