Archive | May, 2013

Big Data Needs Thick Data

Tricia Wang

Tricia Wang

Editor’s Note: Tricia provides an excellent segue between last month’s “Ethnomining” Special Edition and this month’s on “Talking to Companies about Ethnography.” She offers further thoughts building on our collective discussion (perhaps bordering on obsession?) with the big data trend. With nuance she tackles and reinvents some of the terminology circulating in the various industries that wish to make use of social research. In the wake of big data, ethnographers, she suggests, can offer thick data. In the face of derisive mention of “anecdotes” we ought to stand up to defend the value of stories.


image from Mark Smiciklas at Intersection Consulting

image from Mark Smiciklas at Intersection Consulting

Big Data can have enormous appeal. Who wants to be thought of as a small thinker when there is an opportunity to go BIG?

The positivistic bias in favor of Big Data (a term often used to describe the quantitative data that is produced through analysis of enormous datasets) as an objective way to understand our world presents challenges for ethnographers. What are ethnographers to do when our research is seen as insignificant or invaluable? Can we simply ignore Big Data as too muddled in hype to be useful?

No. Ethnographers must engage with Big Data. Otherwise our work can be all too easily shoved into another department, minimized as a small line item on a budget, and relegated to the small data corner. But how can our kind of research be seen as an equally important to algorithmically processed data? What is the ethnographer’s 10 second elevator pitch to a room of data scientists?

…and GO!

Big Data produces so much information that it needs something more to bridge and/or reveal knowledge gaps. That’s why ethnographic work holds such enormous value in the era of Big Data.

Lacking the conceptual words to quickly position the value of ethnographic work in the context of Big Data, I have begun, over the last year, to employ the term Thick Data (with a nod to Clifford Geertz!) to advocate for integrative approaches to research. Thick Data uncovers the meaning behind Big Data visualization and analysis.

Thick Data: ethnographic approaches that uncover the meaning behind Big Data visualization and analysis.

Thick Data analysis primarily relies on human brain power to process a small “N” while big data analysis requires computational power (of course with humans writing the algorithms) to process a large “N”. Big Data reveals insights with a particular range of data points, while Thick Data reveals the social context of and connections between data points. Big Data delivers numbers; thick data delivers stories. Big data relies on machine learning; thick data relies on human learning.

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May 2013: Persuasive Formats

I wanted to focus my own contribution to this month’s special edition (about “how to talk to companies about ethnography”) on presentation formats. That research findings will ultimately be delivered or presented is a given, but the particular format varies and seems often to be a matter of the conventions within particular organizational or research cultures. I’ve participated in ethnographic projects within the corporate sector. I’ve done a bit of consulting work for an NGO. The bulk of my career I’ve spent in Academia doing ethnographic work as most conventionally defined – culminating in the writing of an 80,000 word ethnographic monograph (which was text by-and-large with just a few black and white photos). On this basis, I’ve passed through a few different micro-worlds where different presentation practices prevailed.

In our interview with Steve Portigal this month I asked him about the hierarchy of formality he describes in his new book. For delivering the late-breaking or unprocessed findings (to communicate their informality) he uses e-mail, then Word documents, and finally polished results are delivered in PowerPoint. The ascendence of PowerPoint (not as an accompaniment to a project report, but as the report itself) in corporate settings and consultancy work I find really fascinating. Maybe because of the way it seems to prioritize communicating with as few words as possible, the pressure to edit down to the essentials, to consider what to omit just as much as what to include, how daunting! It seems obvious that this is reflection of the particularly intensive pressures of productivity, of delivering on the short project cycles of the private sector.


The Office suite of applications does not, by any means, encompass the full range of formats that are our options for communicating about ethnographic research. For example, my first job title when I worked in industry (at Intel Corp) was “Application Concept Developer.” My task was to translate research findings from our team of social scientists (who used interviews, observation, diary studies, copious photographs, etc) into interactive design concepts. These were not prototypes, but rather interactive demonstrations showing how insights from fieldwork fed into novel designs for computing systems. This was an attempt to communicate between social scientists and engineers…using the language of building and by engaging through interactivity.

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Interviewing Users by Steve Portigal

Steve Headshot B (Small)

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Editor’s Note: This post for May’s Special Edition on ‘Talking to Companies about ethnography’ comes from Steve Portigal who has a new book out this month titled Interviewing Users. As someone who’s been in the trenches for decades now running his own successful consultancy, Steve has done a great deal of both ‘interviewing users’ and ‘talking to companies about ethnography.’ Below we take the opportunity to interview him! We at Ethnography Matters are also big fans of the ‘War Stories‘ series on his blog where interviewers report on the unexpected things that happen to them in the field.

Steve Portigal is the founder of Portigal Consulting, a bite-sized firm that helps clients to discover and act on new insights about themselves and their customers. Over the course of his career, he has interviewed hundreds of people, including families eating breakfast, hotel maintenance staff, architects, rock musicians, home-automation enthusiasts, credit-default swap traders, and radiologists. His work has informed the development of mobile devices, medical information systems, music gear, wine packaging, financial services, corporate intranets, videoconferencing systems, and iPod accessories. He blogs at and tweets at @steveportigal.

This interview is available en Español – Habitantes Experiencia Diseño Innovación


Image courtesy of Rosenfeld Media

Ethnography Matters: First all Steve, congrats! We are so excited to have a copy of your book. Before diving into the specific questions, we want to know what motivated you to write this book?

Steve Portigal: Thanks! I’ve wanted to write a book from the time I was a little kid. I didn’t imagine it would be non-fiction, though! A lot of folks in the user experience and design worlds were feeling the need for a good book about this and my name came up as the author they’d want to see something from. I had been talking with Rosenfeld Media for a while about writing something, but it seemed like a daunting commitment. But when your peers are asking for it, it’s pretty compelling!

EM: So which part of the book was the most fun to write? Which part was the hardest?

SP: There were creative and intellectual challenges and rewards all the way along. A lot of the writing process was taking topics I had been speaking about for years and crafting the kind of text that is appropriate for a practitioner book. It was fun to revisit familiar points and find a better way to convey them. And then once in a while I’d hit on something that I maybe would typically gloss over in a presentation and realize I’d better dig a little deeper into myself and find away to explain something. The details of some of those moments are lost to memory, but the part of the process where I was discovering something by articulating it was pretty wonderful.

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Tweeting Minarets: A personal perspective of joining methodologies

David Ayman Shamma

Editor’s note: In the last post of the Ethnomining‘ edition, David Ayman Shamma @ayman gives a personal perspective on mixed methods. Based on the example of data produced by people of Egypt who stood up against then Egyptian president and his party in 2011, he advocates for a comprehensive approach for data analysis beyond the “Big Data vs the World” situation we seem to have reached. In doing so, his perspective complements the previous posts by showing the richness of ethnographic data in order to deepen quantitative findings.
David Ayman Shamma is a research scientist in the Internet Experiences group at Yahoo! Research for which he designs and evaluate systems for multimedia-mediated communication.


There’s a problem we face now; the so called Big Data world created an overshadowing world of numerical data analysis leaving everyone else to try to find a coined niche like “small data” or “long data” or “sideways data” or the like. The silos and fragmentation is overwhelming. But really, it’s just all data. Regardless of the its form or flavor, there are people who are experts at number crunching data and people who are experts at field work data. Unfortunately, the speed at which data science moves is attractive and that’s part of the problem; we don’t get the full picture at speed and everyone is racing to produce answers first.

A few months ago, in a conversation with a colleague, he told me “you don’t know what you don’t know, especially when it’s not there.” We were looking for a way to automatically surface a community of photographers on Flickr who didn’t annotate their photos. They didn’t use any titles or tags or any annotations what so ever. But they were clearly a strong and prolific community. If there was some way to automatically identify them, then we could help connect them.

Now, finding metrics for social engagement in unannotated data is not an impossible task when provided with some signal in the data that has some correlation, statistical or otherwise, to the effect you’re trying to surface. But in some cases, it’s just not possible. What you need is just not there; therein is a problem. In other cases, it’s much harder to surface features when you don’t know what they look like.

When you have a lot of data, finding that unexplainable prediction through algorithmic statistics becomes easier. It doesn’t explain why and it doesn’t always work.

Enter Ethnography to answer the why and find out what things might look like—surfacing findings in the age of big data. When I was invited to write a post on Ethnography Matters, I decided to illustrate this through a personally motivated example.

In the late January of 2011, the people of Egypt stood up against then President Hosni Mubarak and his National Democratic Party. They wanted employment, a fair government, and an end to the 30 year long emergency law which had removed most of their civilian rights. Undoubtedly, you read about it somewhere. At the time, my mother was in Cairo visiting her 100+ year old mother. So this left me glued to the only source of news I could find—a rather buggy Al Jazeera video stream. U.S. news agencies were slow to start some sparse coverage. Somewhere in-between, it was burning up on Twitter.

Tharir tweets

A visualization of Twitter activity directed towards Tahrir by aymanshamma

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