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 ...
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.
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 ethnographer’s, 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.
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?
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 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.
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 portigal.com/blog and tweets at @steveportigal.
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.
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.
Editor’s note: This post for the April ‘Ethnomining‘ edition comes from Rachel Shadoan and Alicia Dudek. Following on the past posts about hybrid methods, this one features another interesting case study involving an on-line role-playing game. Their work correspond to a different approach, based on visualizations, than what we saw in the two previous posts.
Rachel Shadoan @RachelShadoan likes to find answers to interesting questions, and build interesting things using those answers. Currently she is answering interesting questions in the Intel Labs using a combination of data visualization, data mining, and ethnographic techniques.
Alicia Dudek @aliciadudek is a design ethnographer and user experience consultant. Her passion is finding unusual solutions to the usual problems. Currently, she is finding unusual solutions for Deloitte Digital, where she specializes in engaging stakeholders in research insights through participatory design workshops.
A few weeks into our study of Plant Wars, an online text-based fighting RPG developed by Jon Evans of Artful Dodger Software, we encountered a mystery. We had visualized the server log data that records the players’ in-game activities, and discovered a pattern as obvious as it was inexplicable: in June 2009, the top Plant Wars players began slowly shifting the time of day in which they were playing. Over a period of six months, the time that the top players started playing each day shifted by nearly six hours. We poured over the server log data, checking the processing code for errors, for time zone issues, for any possible explanation of this shift in play pattern. Using only the server log data, we came up empty-handed. What was going on?
Editor’s note: This post for the April ‘Ethnomining‘ edition comes from Fabien Girardin @fabiengirardin who describes his work with networked/sensor data at the Louvre Museum in Paris. Based on this inspiring case study, he discusses the overall process, how mixed-methods are relevant in his work, and what kind lessons he learnt doing this.
Fabien Girardin is Partner at the Near Future Laboratory, a research agency. He is active in the domains of user experience, data science and urban informatics.
At the Near Future Laboratory we like to experiment and to go in different directions from the typical technology consultancy. We thrive on the involvement of multiple practices, and bet on the unordinary when it comes to question formulation, data collection and solution creation. After completing my PhD in Computer Science, I left the bounded disciplines of academia to embrace learning and connecting to the other “fields”, the other ways of knowing and seeing the world. Along with partners Julian Bleecker, Nicolas Nova and a network of tactical scouts, we formed a technology-based practice that combines insight and analysis, design and research, and rapid prototyping to transform ideas into material form.
Over the past 5 years, I have led investigations that aim to extract knowledge from the byproducts of people’s digital activities (i.e. network data, also often called digital shadows or digital footprints). That intangible material can take the form of logs of cellular network activity, aggregated credit card transactions, real-time traffic information, user-generated content or social network updates. Over time my contributions have evolved into helping transform this type of big data into insights, products and services. Whether applied for a client or as part of our self-started initiatives, this practice requires the basic skills of a “data scientist” (data analysis, information architecture, software engineering and creativity) along with a capacity to engage at the intersections with a wide variety of professionals, from physicists and engineers to lawyers, strategists and designers. The transversal incline of investigations on network data requires understanding the different languages that shape technologies, reporting on the context of their use, and describing people’s practices. The model of inquiry blends qualitative field observations with quantitative evidence often extracted from logs.
Past projects have led us to exploit untapped data sources, uncover opportunities to transform data into insights, and materialize new services or products. Our method first contemplates datasets and techniques to approach our objectives. Then we develop tangible solutions that engage the project stakeholders in exploring different scenarios and solutions. It is through the experiences of people with knowledge of the project domain that we are able to extract possible near-future changes and opportunities.
Editor’s note: This post for the April ‘Ethnomining‘ edition comes from Rebekah Rousi, @rebekahrousi who describes how the combination of qualitative and quantitative data collection was fruitful in her analysis of elevator usage. The post highlights the lessons she uncovered using both approaches.
Rebekah is a researcher of user psychology and PhD candidate of Cognitive Science at the University of Jyväskylä, Finland. With a background in visual arts and cultural studies, she is particularly interested in the psychology of user experience, affective human-technology interactions and the mental factors of design encounters.
I don’t know who was more moved by the experience of elevator design, me or the 50 people I interviewed. A few years ago a leading elevator design and manufacturing company gave me the task of examining how people experienced and interacted with elevators. The scope included everything from hall call buttons, to cabin interior design and perception of technical design. When given the brief, the artistic director noted country specific design features (or omissions) and even mentioned that there may be observable elevator habits I would want to take note of. Then, on our bidding a corporate-academic farewell she added that I might want to consider the psychology of the surrounding architectural environment. With that, I was left with a long list of to-do’s and only one method I could think of that would be capable of incorporating so many factors – ethnography. Ethnographic inquiry provides a framework in which the researcher’s own observations and experiences of the phenomenon under study – in this case elevator users’ behaviour in relation to the elevators, other users and the surrounding architectural environment – can be combined with “insiders’” opinions and insights.
So, I undertook the study in two of Adelaide’s (Australia) tallest office buildings (see the building entrances above). I chose these buildings for several reasons: 1) they are both highrises in which elevator usage is a necessity; 2) they are both non-residential office buildings in which factors such as occupational well-being, health and safety, and socio-cultural dimensions including power relations and hierarchies come into play. In order to gauge and explain user behaviour in relation to the tangible and non-tangible dynamics of the spaces, it is necessary to study sites which are similar in purpose. Further, both buildings housed the same brand of elevators. And both had only recently undergone elevator upgrades.
The data collection consisted of two separate parts: the mini-interviews (or verbal questionnaires) which lasted two to five minutes; and the field observations. The mini-interviews comprised the following topics: background information; mental factors such as current mood and personality type loosely based on the Myers-Briggs Type Indicator (social, organised, intuitive and analytical); Likert-scale opinion rating of elevator design elements; design suggestions; preferences (elevators or stairs?); security and safety; and habits. The components I was looking at in the field observation were: waiting and operating habits; interaction with design; interpersonal interaction; and movement flow.
While ethnography generally draws on qualitative data, it does not not mean that quantitative approaches shouldn’t be employed in the research process. Combining the two leads to a “mixed-method approach” that can take various forms: data collection and analysis can be either separated or addressed together, and each of them can be used in service of the other. Of course, this isn’t new in academic circles and corporate ethnography but there seems to be a renewed interest lately in this topic.
One of the driving forces of this renewed interest is the huge amount of information produced by people, things, space and their interactions — what some have called “Big Data“. The large data sets created by people’s activity on digital devices has indeed led to a surge of “traces” from smartphone apps, computer programs and environmental sensors. Such information is currently expected to transform how we study human behavior and culture, with, as usual, utopian hopes, dystopian fears and *critical sighs* from pundits.
Although most of the work of Big Data has focused on quantitative analysis, it is interesting to observe how ethnographers relate to it. Some offer a critical perspective, but others see it as an opportunity to create innovative methodologies to benefit from this situation. See for instance the notion of “Ethnomining” described by Aipperspach et al. (2006) in their insightful paper Ethno-Mining: Integrating Numbers and Words from the Ground Up:
Ethno-mining, as the name suggests, combines techniques from ethnography and data mining. Specifically, the integration of ethnographic and data mining techniques in ethno-mining includes a blending of their perspectives (on what interpretations are valid and interesting and how they should be characterized) and their processes (what selections and transformations are applied to the data to find and validate the interpretations).
Editor’s note: In the last post of the Stories to Action edition, urban research designer Adriana Valdez Young @thepublicagency tells us how she used stories gathered from ethnographic research to design a game for architects and planners. Her “action,” a game called Arrivalocity, allowed users to access stories from her fieldwork. Although not all “actions” turn out as we expect. Adriana shares with us how she would approach this process if she were to do this again. All designers and researchers can learn from her very open and honest reflection.
Adriana Valdez Young makes creative learning and research platforms that engage people with their city. She is the co-founder of English for Action in Rhode Island, helped launch KARAJ in Beruit and is the co-editor of Betta, an architecture zine on lifestyle and conflict.
One day in Eindhoven for a lighting workshop. The next day, back to London for a one-hour walk down the street, followed by six hours drawing plans for a boutique hotel, art cinema, and food market to present to city officials. This is how a group of architecture firms spent two days in the spring of 2012 shaping a gentrified vision for Rye Lane (Olcayto 2012).
Designers, planners and developers shape our cities, yet they can spend little to no time in the field before delving into decision making. In the context of culturally-complex and rapidly changing streets, the results can be generic and damaging characterizations, leading to bland and detrimental designs.
As a researcher with the ‘Ordinary Streets’ project at LSE Cities, I spent several months in 2012 learning about the culture of trade on Rye Lane – a dense, multicultural high street in the neighborhood of Peckham, South London. Rye Lane is a street where businesses and shoppers regularly out-maneuvered tight spaces and budgets. It is an entrepreneurial and cultural destination, where a newly arrived immigrant can rent an outdoor market stall for a daily rate of £10 – using only a mailing address and a mobile number to secure a permit; where a woman can buy exactly the same foods she cooked, hair style she wore and movies she watched in Lagos – all in the same shop; and where a refugee from Iraq manages a store that he subdivided from one to eight micro businesses – each one run by immigrants.
The Infra/Extraordinary column is devoted to zooming in on intriguing objects and practices of the 21st Century. Adopting a design-ethnography perspective, we will question informal urban bricolage, weird cameras, curious gestures and wonder about their cultural implications.
Running across this “pedibus” sign on the streets of Lausanne the other day made me think about the cultural implications for such practice.
Pedibus are commonly found in European cities such as Geneva, Lausanne or Lyon and one can see them as an intriguing type of school bus line that collects students at scheduled stops located in the city, except there’s no actual “bus”. Children are “picked-up” in accordance with a predefined and fixed timetable. They are then brought to school on foot by volunteers (parents or people from the neighborhood).
The name is a portmanteau word formed from the latin root “pedester” (which means “going on foot“) and “bus”. This semantic combination highlights the ambulatory character of the system, with the participants walking without any other mean of transport (that being said, I sometimes see kids on scooters when “in” the pedibus).
In general, pedibus systems can be created by urban institutions, or by a group of parents who are interested in a healthy and cheap way to deal with pupils’ schedules. Of course, such collective services are necessarily bound to the structure of urban environment. They are indeed more likely to be found in dense (and safe) city centers than sprawl-like suburbs, but one can also run across a pedibus in the countryside in France or Switzerland.
The pictures above have been taken in Lausanne, a Swiss city with a population of nearly 130’000 inhabitants making it the fourth largest city of the country and 41.38 square km2 (15.98 sq mi). The website about the pedibus in this town indicates that the network is 21 km/13 miles long with 40 “lines” (approximately 575 m/0.3 mile long).
These numbers are intriguing but that’s not what I’m most interested in. Looking at the picture above, several elements caught my eye:
- A very casual form of signage: it’s made of a wooden plaque with bright colors and a hand-drawn typeface, which is a bit unusual in Switzerland with its high standard of graphic design. It is also attached to existing urban infrastructures (signage, wall, etc.). This highlights the informal character of this system: disconnected from the other urban signs (which have a more structured visual identity). Pedibus stops like this one are sometimes removed during summer vacations, as if to tell us the temporary existence of this means of transport (and the rythm of the “school season”).
- Unlike other bus stops, the timetable is pretty basic and limited to certain moments of day: morning, end of morning, beginning of the afternoon and end of afternoon (based on school schedules).
- There’s a short description of what a pedibus is (with words and a drawing representing the bus): even if the system is 14 years old in Lausanne, it may tell us that it’s still important to explain what it is; probably for newcomers.
Beyond my interest in alternatives means of transports, I find pedibus systems fascinating for two reasons. First and foremost, they show the importance of bottom-up innovation as well as citizen participation. That’s probably what could be called a “Smart City” from a human perspective. Second, they also reveal how innovation can be based on “removing” elements from an existing system. In this case, and because it makes sense in terms of distance, this mean of transport corresponds with the removal of the main artifact that was involved in the process: the bus. I think that this is more than the “less is more” ethos commonly found in design circles, and which strives for minimalism. To some extent, the pedibus may be another example of “innovation through subtraction“, a sociological concept that I recently encountered in this research paper: “innovation founded on reducing a practice or ceasing to use – subtracting, detaching – a given artefact.“. From a design POV, I’m fascinated by this move: you take an existing technological system (e.g. school bus), you remove the main component (i.e. the bus), and then you try to find a workaround.
Do you see any other examples in your everyday life? Can you invent other examples of pedibus-like innovation with other technological artifacts/services?
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- interesting tidbit on @steveportigal's thoughts about ascendency of ppts for delivering ethnographic insights in orgs ow.ly/l0LBV 1 day ago
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- Contributions from @steveportigal, @triciawang, and @jennaburrell on "how to talk to companies about ethnography" - ethnographymatters.net 3 days ago
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