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How Can Thick Data Improve the Way We Use Big Data?

Troy Sadkowsky - Friday, June 30, 2017

 

April 16, 2017

TedTalk: https://www.youtube.com/watch?v=WaOUJa9fjXU

Medium: https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7

Big data can help us solve problems, optimize services, predict the future and learn more about ourselves and human behavior. But when big data cannot answer a question, or makes an incorrect prediction, what if it is because data is not telling us the whole story?

In a Ted Talk and an article on Medium, Tricia Wang, a technology ethnographer, implores us to realize that, even in the age of data-driven decision making, big data can have some serious blind spots, and often suffers from a loss of context through valuable variables and trends that defy quantification.

Insights on contained systems, such as electricity grids or delivery logistics, have established big data’s reputation for success. Although this reputation has attracted investment from a huge array of companies, Wang claims, 73% of big data projects are not profitable, because big data projects frequently do not produce the insights that they seem to promise. For dynamic systems, especially those that depend on human emotions, behavior, and how people are impacted and interact with their environment, certain variables are difficult to measure and model.

She describes how opportunities can be gained and losses diminished by using big data in conjugation with “thick data”, observations that use qualitative methods to understand human emotions, behaviors and interactions with context. For example, the realization by ethnographer Graham McCracken that Netflix users actually enjoy binge-watching, led Netflix to redesign their user experience, improve their business and transform the way we consume media. Through qualitative research and collecting stories about how users interact with their product, Netflix was able to go to their data science team, validate these findings, and act on this competitive insight.

Wang summarizes that while “Thick Data loses scale…Big Data loses resolution.” While big data analytics can have a huge sample size, recognize large-scale patterns, and benefit from the strengths of machine learning, thick data uses small samples sizes, but understands subjects at a much greater depth, and benefits from aspects of human intelligence and pattern recognition that still elude computer programs. As data scientists, the more we understand the limitations of our methods and pay attention to thick data, the more we can recognize the factors and variables our data is missing. With a more holistic perspective, data analysis is better poised to uncover more truthful and powerful insights.

 

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How Can Thick Data Improve the Way We Use Big Data?

Troy Sadkowsky - Friday, June 30, 2017

 

April 16, 2017

TedTalk: https://www.youtube.com/watch?v=WaOUJa9fjXU

Medium: https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7

Big data can help us solve problems, optimize services, predict the future and learn more about ourselves and human behavior. But when big data cannot answer a question, or makes an incorrect prediction, what if it is because data is not telling us the whole story?

In a Ted Talk and an article on Medium, Tricia Wang, a technology ethnographer, implores us to realize that, even in the age of data-driven decision making, big data can have some serious blind spots, and often suffers from a loss of context through valuable variables and trends that defy quantification.

Insights on contained systems, such as electricity grids or delivery logistics, have established big data’s reputation for success. Although this reputation has attracted investment from a huge array of companies, Wang claims, 73% of big data projects are not profitable, because big data projects frequently do not produce the insights that they seem to promise. For dynamic systems, especially those that depend on human emotions, behavior, and how people are impacted and interact with their environment, certain variables are difficult to measure and model.

She describes how opportunities can be gained and losses diminished by using big data in conjugation with “thick data”, observations that use qualitative methods to understand human emotions, behaviors and interactions with context. For example, the realization by ethnographer Graham McCracken that Netflix users actually enjoy binge-watching, led Netflix to redesign their user experience, improve their business and transform the way we consume media. Through qualitative research and collecting stories about how users interact with their product, Netflix was able to go to their data science team, validate these findings, and act on this competitive insight.

Wang summarizes that while “Thick Data loses scale…Big Data loses resolution.” While big data analytics can have a huge sample size, recognize large-scale patterns, and benefit from the strengths of machine learning, thick data uses small samples sizes, but understands subjects at a much greater depth, and benefits from aspects of human intelligence and pattern recognition that still elude computer programs. As data scientists, the more we understand the limitations of our methods and pay attention to thick data, the more we can recognize the factors and variables our data is missing. With a more holistic perspective, data analysis is better poised to uncover more truthful and powerful insights.

 

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Ramona commented on 15-Mar-2019 09:18 AM
Wow, superb blog structure! How long have you been running a blog for?
you made blogging glance easy. The entire glance of your site is magnificent, let alone the content material!
hur gör man bodelning commented on 15-Mar-2019 09:25 AM
This is the right site for anyone who would like to find out about this topic.

You understand so much its almost hard to argue with you (not that I personally will need to…HaHa).
You definitely put a new spin on a subject that's been discussed for many
years. Wonderful stuff, just great!
sklepy www, commented on 15-Mar-2019 09:30 AM
What's Happening i am new to this, I stumbled upon this I have found It positively helpful
and it has helped me out loads. I am hoping to contribute & assist other users like its aided me.
Good job.
If you are not redirected in 5 seconds commented on 15-Mar-2019 09:35 AM
Saved as a favorite, I really like your site!
rakesh yadav reasoning book pdf commented on 15-Mar-2019 10:10 AM
There's definately a lot to find out about this issue. I love
all the points you made.
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Thanks for sharing!
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There's a lot of folks that I think would really enjoy your
content. Please let me know. Cheers
máy giặt công nghiệp giá rẻ commented on 15-Mar-2019 02:41 PM
Its like you read my mind! You appear to know a lot about this, like you wrote the book
in it or something. I think that you can do with a few
pics to drive the message home a little
bit, but instead of that, this is magnificent blog. An excellent read.
I'll certainly be back.
administracja commented on 15-Mar-2019 02:44 PM
Wow, fantastic blog layout! How long have you been blogging for?

you make blogging look easy. The overall look of your site is great, let alone
the content!
Reginald commented on 15-Mar-2019 03:21 PM
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website, commented on 15-Mar-2019 03:30 PM
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reason seemed to be on the web the easiest factor to remember
of. I say to you, I certainly get annoyed while folks think about concerns that they just do
not realize about. You managed to hit the nail upon the top
and defined out the entire thing without having side effect , other people
could take a signal. Will probably be again to get more.
Thank you
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