In 2009, tech ethnographer Tricia Wang spent some time in China, living with migrants, working alongside street vendors, and crashing at internet cafés. She was conducting research for Nokia, which at the time was the world’s largest cellphone company, when she discovered something that challenged the organization’s entire business model. “I saw lots of indicators that led me to conclude that low-income consumers were ready to pay for more expensive smartphones,” Wang wrote in a blog post called “Why Big Data Needs Thick Data” for the website Ethnography Matters four years later.
What is measurable isn’t the same as what is valuable.
Ethnography is the scientific study of the customs of individual peoples and cultures, and Wang describes “thick data” as “data brought to light using qualitative, ethnographic research methods that uncover people’s emotions, stories, and models of their world.” Without it, Wang argues, companies are basing important business decisions off incomplete data — like Nokia was in 2009. “Their notion of demand was a fixed quantitative model that didn’t map to how demand worked as a cultural model in China,”Wang wrote. “What is measurable isn’t the same as what is valuable.”
NOT WHAT IT SEEMS
Through her research, Wang concluded that “Nokia needed to [change] their current product-development strategy from making expensive smartphones for elite users to affordable smartphones for low-income users.” But when she reported her findings to the company, they said her sample size — about 100 people — was “weak and small” compared to their millions of data points. “In addition,” Wang wrote, “they said that there weren’t any signs of my insights in their existing datasets.”
That’s because Nokia was only looking at the numbers, while ethnography can be integral to fully understanding data. “Data is always incomplete,” Sarah Pink, professor of design and media ethnography at Australia’s RMIT University, said in an interview with Convene. “It does not tell us enough contextual information about the people involved, the environments and contingent circumstances that they are in, their feelings, or their motivations.”
Pink says incomplete data can be “problematic” when it’s used to drive design and policy decisions. “A responsible approach to data and to our technological futures requires an ethnographic awareness of where data comes from,” she said. “Data ethnography can be used to understand how digital data is produced, used, analyzed, and deployed in everyday life, society, and organizations.” Applying this to events, Pink said, “entails an ethnographer hanging out with participants, asking questions, video-recording, and collaborating with them in the research to understand what data means at the moments when people are involved with it.”
How exactly? Data ethnography “can be used to understand how people use the data produced by self-tracking technologies such as wearables and apps,” Pink said. “They often use the technologies in ways that are surprising and that show us that data does not always mean what we might at first think it does. This emphasizes data ethnography is important because it enables us to understand data and what it means through contextualized insights, and by acknowledging that data might be ‘broken,’ incomplete, or otherwise not quite what it might be assumed to be at first glance.”
INTERROGATING BIG DATA
Big data by nature strips away context, according to Wang, and with it the stories of your attendees. “Integrating big data and thick data provides organizations a more complete context of any given situation,” Wang wrote. “For businesses to form a complete picture, they need both big and thick data, because each of them produces different types of insights at varying scales and depths.”
For businesses to form a complete picture, they need both big and thick data.
Because data ethnography is such a new approach to working with digital data and emerging tech, there isn’t a traditional use for it yet, Pink says. However, there have always been studies that bring together quantitative data and qualitative ethnographic data in order to provide context. To better understand the intersection between data and behavioral patterns, Pink launched RMIT’s Data Ethnographies Lab, which includes a series of workshops on ethnographic data. “I realized that we were at a moment where it was essential to begin to interrogate the rise of big-data collection, analytics, and use through ethnographic research,” Pink said.
These are good resources to have in a time when, according to Wang, companies are relying too heavily on big data. “I encountered a lot of doubt on the value of ethnographically derived data when I started working primarily with corporations,” she wrote. “I started to hear echoes of what Nokia leadership said about my small dataset — that ethnographic data is ‘small,’ ‘petite,’ [and] ‘puny.’”
But in 2013, when Microsoft bought Nokia, the onetime industry leader had just 3 percent of the global smartphone market. “They didn’t know how to handle data that wasn’t easily measurable and that didn’t show up in existing reports,” Wang wrote. “What could’ve been their competitive intelligence ended up being their eventual downfall.