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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.

 

Comments
Eugene commented on 02-Sep-2017 05:26 AM
Hi, this weekend is nice for me, since this occasion i am reading this fantastic informative piece of writing here at my house.


Here is my web site;sell merchandises
Gmail Customer Support Phone Number commented on 02-Sep-2017 06:26 PM
That's why i left android for an iPhone this year.
Jayne commented on 03-Sep-2017 07:22 AM
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Arlie Glassburn commented on 03-Sep-2017 07:23 AM
How make 900? Look here http://www.kusicreavision.com/blog.html
Tamie commented on 03-Sep-2017 09:58 PM
Great site you have got here.. It�s hard to find excellent writing like yours nowadays.

I truly appreciate individuals like you! Take care!!

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Juliane commented on 04-Sep-2017 01:14 AM
whoah this weblog is wonderful i like reading your articles.

Stay up the great work! You realize, many individuals are looking round for this information, you can help them greatly.

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LEAGUE OF LEGENDS 2017 RP HACK GENERATOR WORKS ON MAC commented on 05-Sep-2017 09:43 AM
Me enjoying, will read more. Thanks!
Hayden Homa commented on 05-Sep-2017 10:22 PM
Please check out my FREE course on how to drive targeted traffic to your website:

http://www.3001data.com/targeted-traffic-course

I hope it will help you get more visitors to datascientists.org
quickbooks customer service phone number commented on 06-Sep-2017 09:22 AM
Each time i turn round there is one other flaw in quickbooks online,
even easy stuff, like my most up-to-date flaw was I emailed a due on receipt invoice, the invoice shows due on receipt however
the e mail shows bill due in 30 days.
Simon Maltez commented on 06-Sep-2017 05:06 PM
Please check out my FREE course on how to drive targeted traffic to your website:

http://www.3001data.com/targeted-traffic-course

I hope it will help you get more visitors to datascientists.org
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Hey! Quick question that's completely off topic. Do you know how to make your site mobile friendly?

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to work on. You have done a formidable activity and our whole community
might be grateful to you.
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