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Data Jujitsu: The Art of Turning Data into Product
Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.Learn how to use a problem's "weight" against itself Break down seemingly complex data problems into simplified partsUse alternative data analysis techniques to examine themUse human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problemsLearn more about the problems before starting on the solutions and use the findings to solve them, or determine whether the problems are worth solving at all."
31 pages, Kindle Edition
First published July 17, 2012
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416 people want to read
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Displaying 1 - 28 of 28 reviews
February 23, 2015
This is one of those books where you get much more than you paid for. Not only because the book is actually free (no pun intended), but also because in its barely 30 pages the reader is confronted with many useful ideas in a simple, easy to grasp way. The title is a reference to the ancient martial art where the most important feature, its essence, is "...manipulating the opponent's force against himself rather than confronting it with one's own force". Here, "opponent" has a wide meaning, encompassing concepts such as client/user, one's own deceptions, technology inherent flaws and more.
Thus, the author suggests paying attention to actual or possible problems and actively looking for ways not of avoiding these problems but actually turning them upside down into valuable items. Interestingly, he broadens the definition of the object of study ("data products") as any product "that facilitate an end goal through the use of data". Obviously, under the right conditions, this can expand almost to every single field.
I specially liked the way he applies these ideas to getting data from users, turning friction and rejection into acceptance or even attraction.
Those already acquainted with agile development, LEAN principles, User Experience design and engineering... will find many common and familiar ideas here. The book's key strength for them will probably be how natural and smooth they become when applied to data products. Those readers not yet familiar with these concepts will probably have a gold mine here, sparking interest in many subjects.
Here are a few quotes I found interesting:
- Ideas for data products tend to start simple and become complex; if they start complex, they become impossible.
- The point is to have a conversation rather than just a form. Engage the user to help you, rather than relying on analysis.
- Trying to solve a problem on the back end is 100-1,000 times more expensive than on the front end.
- Watch for the signals the humans use to make their decisions.
- When you’re getting started, you don’t care about the long run. You just want to survive long enough to have a long run.
- We’re far enough into the data game that most users have realized that they’re not the customer, they’re the product.
- One of the biggest challenges of developing a data product is figuring out how to give data back to the user.
- The more data you present, the less interaction
- You need to explain to the user why you’re asking for data; you need to disarm the user’s resistance to providing more information by telling him that you’re going to provide value.
- Give them control. It turns annoyance into empowerment.
- A surprising amount of Data Jujitsu is about product design and user experience. (...) If you can enlist your users to help, you’re ahead on several levels: You’ve made the product more engaging, and you’ve frequently taken a shortcut around a huge data problem.
Thus, the author suggests paying attention to actual or possible problems and actively looking for ways not of avoiding these problems but actually turning them upside down into valuable items. Interestingly, he broadens the definition of the object of study ("data products") as any product "that facilitate an end goal through the use of data". Obviously, under the right conditions, this can expand almost to every single field.
I specially liked the way he applies these ideas to getting data from users, turning friction and rejection into acceptance or even attraction.
Those already acquainted with agile development, LEAN principles, User Experience design and engineering... will find many common and familiar ideas here. The book's key strength for them will probably be how natural and smooth they become when applied to data products. Those readers not yet familiar with these concepts will probably have a gold mine here, sparking interest in many subjects.
Here are a few quotes I found interesting:
- Ideas for data products tend to start simple and become complex; if they start complex, they become impossible.
- The point is to have a conversation rather than just a form. Engage the user to help you, rather than relying on analysis.
- Trying to solve a problem on the back end is 100-1,000 times more expensive than on the front end.
- Watch for the signals the humans use to make their decisions.
- When you’re getting started, you don’t care about the long run. You just want to survive long enough to have a long run.
- We’re far enough into the data game that most users have realized that they’re not the customer, they’re the product.
- One of the biggest challenges of developing a data product is figuring out how to give data back to the user.
- The more data you present, the less interaction
- You need to explain to the user why you’re asking for data; you need to disarm the user’s resistance to providing more information by telling him that you’re going to provide value.
- Give them control. It turns annoyance into empowerment.
- A surprising amount of Data Jujitsu is about product design and user experience. (...) If you can enlist your users to help, you’re ahead on several levels: You’ve made the product more engaging, and you’ve frequently taken a shortcut around a huge data problem.
June 22, 2021
Quick advice on how to think about data science projects as products. It helps to shift the usual train of thought of models + data to thinking about products, starting simple and not overcomplicating things. If your product ends up being successful, you'll have plenty of time to spin up fancy algorithms. Easy read, just 30 pages.
April 4, 2013
A very short collection of things to remember while building a data product with some good examples of the products we know ( LinkedIn, Facebook etc)
May 14, 2017
A very short read that is more-so a very light introduction to building data products through the application of an MVP and agile approach. Some good advice with examples from the LinkedIn team. Some of my favourite snippets are:
- When in doubt, use humans
- Giving back too much data in a way that’s overwhelming and paralyzing is “data vomit.”
- An “inverse interaction law” applies to most users: The more data you present, the less interaction.
- When in doubt, use humans
- Giving back too much data in a way that’s overwhelming and paralyzing is “data vomit.”
- An “inverse interaction law” applies to most users: The more data you present, the less interaction.
March 23, 2020
MVP is Key
Looking at data oriented products, the key is to remember the concepts of MVP and simplicity. This quick read gives good perspective on product design fundamentals in a data oriented product environment. A good reminder to keep it simple and focus on clean data over feature sets.
Looking at data oriented products, the key is to remember the concepts of MVP and simplicity. This quick read gives good perspective on product design fundamentals in a data oriented product environment. A good reminder to keep it simple and focus on clean data over feature sets.
April 27, 2020
A very good primer on how to start thinking about creating data products and how sometimes getting human involvement at the beginning is better than going all out technical. Wish I had read this a few years ago.
June 2, 2018
The book provides ground rules for creating a feasible data product with real-world examples. I liked it for its clear-cut explanation.
September 17, 2018
Some useful advice from an experienced data scientist. The book was Okay overall, but I gave it an extra star because it's free.
January 21, 2019
Simple, clear and well-written.
February 24, 2019
Great book but very short read
October 26, 2020
Llamar libro a algo con 33 páginas...
El contenido es muy claro e introduce la idea de los datos, aunque está un poco antiguo
El contenido es muy claro e introduce la idea de los datos, aunque está un poco antiguo
January 3, 2021
Clean "book"
Good amount of information and inspiration in 31 pages. It is good for people who has some experience in making a product.
Good amount of information and inspiration in 31 pages. It is good for people who has some experience in making a product.
May 14, 2021
I like how short and concise this book is with the lesson that struck to me the most was not to overdevelop your system if you’re just starting out. Additional features could be added later on once there’s a market for it.
June 21, 2021
There are some perspectives in the book, helpful for a newbie.
March 12, 2017
A short but powerful take on data product design.
July 30, 2016
I found this to be a very interesting and insightful article on data science as it is practiced in the real world. However, your impressions of this short e-book will strongly depend on your expectations. If you are looking for an detailed and technical how-to book, then you will be severely disappointed. I think that people who will most appreciate this e-book article are either those who have very little to no experience with data science, or potentially the high-level experts and veterans of the field. For the first group this e-book could serve as a gentle introduction to the field in the most general way, while the latter could appreciate the big picture take by one of their very experienced colleagues. I am definitely in the first group, and I really enjoyed this short e-book.
The central idea of this e-book is that in design of data-driven products it helps to use the actual usage of the product as a guide and a driving force. This is where the Jujitsu metaphor comes in play: just like a practitioner of that martial arts relies on the opponent's own attacks and forces and tries to martial them to his own advantage, so also a designer of a data-driven product will ideally try to use the "gravitational pull" of data and its use to his advantage.
Patil illustrates his ideas and concepts with several useful examples, mostly from his work at LinkedIn. These are useful examples in their own right, as they also give the reader a few insights into how LinkedIn actually connects people. Some of the ideas in the book are already well known to most software designers and entrepreneurs (good data structures are crucial, try to design a minimally functioning prototype and then iterate, etc.), but others are a bit counterintuitive and novel.
This short e-book is very readable and well written, something that one can never take for granted for a geeky book on data science. It's an interesting read and I was able to finish it in a single sitting.
The central idea of this e-book is that in design of data-driven products it helps to use the actual usage of the product as a guide and a driving force. This is where the Jujitsu metaphor comes in play: just like a practitioner of that martial arts relies on the opponent's own attacks and forces and tries to martial them to his own advantage, so also a designer of a data-driven product will ideally try to use the "gravitational pull" of data and its use to his advantage.
Patil illustrates his ideas and concepts with several useful examples, mostly from his work at LinkedIn. These are useful examples in their own right, as they also give the reader a few insights into how LinkedIn actually connects people. Some of the ideas in the book are already well known to most software designers and entrepreneurs (good data structures are crucial, try to design a minimally functioning prototype and then iterate, etc.), but others are a bit counterintuitive and novel.
This short e-book is very readable and well written, something that one can never take for granted for a geeky book on data science. It's an interesting read and I was able to finish it in a single sitting.
December 30, 2014
Takes the concepts of breaking down projects into simple iterative feature releases to get the user in the app as early as possible and drive direction and acceptance. Even condones crowd sourcing some proof of concept work up front to prove viable before committing resources to am unproven hunch. Very brief read but full of good advise backed up by real works examples.
July 22, 2012
Very good advices there.
July 26, 2012
Short, but contains many useful advices applicable not only for big-data related projects
August 4, 2012
The book takes the MVP and agile principles and applies them to the data space and products. A quick read with some good examples from the LinkedIn team.
January 2, 2013
Good tips, probably, for the working data scientist.
December 4, 2013
Interesting ideas on how to approach data projects. This is more an essay rather than a book. You can read it online at http://radar.oreilly.com/2012/07/data...
January 25, 2015
Good, for what it is. I didn't realize it was SO short when I started. This was more of a blog post than a book.
March 22, 2014
An excellent read for any data scientist or someone thinking of becoming data entrepreneur.
December 20, 2014
Interesting but not as useful
Good theoretical content with opaque examples.
More concrete examples and cases would have made it a lot more useful to a broader set of engineers.
Good theoretical content with opaque examples.
More concrete examples and cases would have made it a lot more useful to a broader set of engineers.
September 2, 2015
Describing some ideas in details like Data Vomiting, using user as product and give him some processed data to engage him, using Juijutsu to use the size of the data against itself by humans, ..
November 12, 2015
Great reminder that building data products is an MVP in and on itself.
March 28, 2016
One of the best books to understand the importance of data, analytics, startups and data science!
Displaying 1 - 28 of 28 reviews