Big Data Needs Thick Data

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.

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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.

CAUTION

As the concept of “Big Data” has become mainstream, many qualitative researchers from Genevieve Bell (Big Data as a person) to  Kate Crawford (algorithmic illusion, data fundamentalism), and danah boyd (privacy concerns) have written essays on the limitations of Big Data. Journalists have also added to the conversation. Caribou Honig defends small data, Gary Marcus cautions about the limitations of inferring correlations, Samuel Arbesman calls for us to move on to long data. Our very own Jenna Burrell has produced a guide for ethnographers to understand big data.

Inside organizations Big Data can be dangerous. Steven Maxwell points out that “People are getting caught up on the quantity side of the equation rather than the quality of the business insights that analytics can unearth.” More numbers do not necessarily produce more insights.

Another problem is that Big Data tends to place a huge value on quantitative results, while devaluing the importance of qualitative results. This leads to the dangerous idea that statistically normalized and standardized data is more useful and objective than qualitative data, reinforcing the notion that qualitative data is small data.

These two problems, in combination, reinforce and empower decades of corporate management decision-making based on quantitative data alone. Corporate management consultants have long been working with quantitative data to create more efficient and profitable companies.

With statistically sound analysis, consultants advise companies to downsize, hire, expand, merge, sell, acquire, shutdown, and outsource all based on numbers (e.g.Mckinsey, Bain & Company, BCG, and Deloitte).

The risk in a Big Data world is that organizations and individuals start making decisions and optimizing performance for metrics—metrics that are derived from algorithms..

Without a counterbalance the risk in a Big Data world is that organizations and individuals start making decisions and optimizing performance for metrics—metrics that are derived from algorithms. And in this whole optimization process, people, stories, actual experiences, are all but forgotten. The danger, writes Clive Thompson, is that “by taking human decision-making out of the equation, we’re slowly stripping away deliberation—moments where we reflect on the morality of our actions.”

INSPIRATION and EMOTION

Thick Data is the best method for mapping unknown territory. When organizations want to know what they do not already know, they need Thick Data because it gives something that Big Data explicitly does not—inspiration. The act of collecting and analyzing stories produces insights.

When organizations want to know what they do not already know, they need Thick Data because it gives something that Big Data explicitly does not—inspiration. The act of collecting and analyzing stories produces insights.

Stories can inspire organizations to figure out different ways to get to the destination—the insight. If you were going to drive, Thick Data is going to inspire you to teleport. Thick Data often reveals the unexpected. It will frustrate. It will surprise. But no matter what, it will inspire. Innovation needs to be in the company of imagination.

When organizations want to build stronger ties with stakeholders, they need stories. Stories contain emotions, something that no scrubbed and normalized dataset can ever deliver. Numbers alone do not respond to the emotions of everyday life: trust, vulnerability, fear, greed, lust, security, love, and intimacy. It’s hard to algorithmically represent the strength of an individual’s service/product affiliation and how the meaning of the affiliation changes over time. Thick Data approaches reach deep into people’s hearts. Ultimately, a relationship between a stakeholder and an organization/brand is emotional, not rational.

Some people are uncomfortable with the use of the term “stories” to describe ethnographic work. There’s a lot of confusion that stories are the equivalent to anecdotes. Market researchers question if it is a “fad.”  Even in academia, many sociologists shun the use of “stories” because it makes their qualitative work appear less scientific. I was often told to use “cases” instead of “stories.”

There’s a big difference between anecdotes and stories, however. Anecdotes are casually gathered stories that are casually shared. Within a research context, stories are intentionally gathered and systematically sampled, shared, debriefed, and analyzed, which produces insights (analysis in academia).  Great insights inspire design, strategy, and innovation.

NPR has a segment that illustrates the power of Thick Data, featuring Frans de Waal, a primatologist and biologist who just published “The Bonobo and the Atheist: In Search of Humanism Among the Primates”. Through his experiments, de Waal provides evidence to support his theory that a sense of fairness—the groundwork for morality—is not unique to humans. In the above video, de Waal shows two Capuchin monkeys receiving different rewards for performing the same action. The monkey that gets a cucumber becomes very upset when she sees the monkey next to her given a grape as a reward for performing a similar task. In the monkey world, grapes are crack and cucumbers are stale bread.

“If I show the data, which are graphs and stuff like that, people are not really convinced, if you show the emotional reaction, the amount of emotion that goes in there, then people are convinced.” Frans de Waal

In his research statement, de Waal makes a captivating case for the principles of Thick Data: “I show that video often, because if I show the data, which are graphs and stuff like that, people are not really convinced, if you show the emotional reaction, the amount of emotion that goes in there, then people are convinced.”

As de Waal makes clear, sometimes the quantitative data alone will not make a compelling argument. Even scientists need stories to make their point.

OPPORTUNITIES

Roger Magoulas, who coined the term Big Data, emphasizes the need for stories: “stories tend to spread quickly, helping spread the lessons from the analysis throughout an organization.”

While using Big Data in isolation can be problematic, it is definitely critical to continue exploring how Big Data and Thick Data can complement each other. This is a great opportunity for qualitative researchers to position our work in the context of Big Data’s quantitative results. Companies like Claro Partners are even reframing the way we ask questions about Big Data. In their Personal Data Economy research, instead of asking what Big Data tells us about human behavior, they asked what human behavior tells us about the role of Big Data in everyday life. They created a toolkit for clients that helps them shift their “perspective from a data-centric one to a human-centred one.”

Here are some areas where I see opportunity for collaboration between the two methods within organizations (this is not meant to an exhaustive or comprehensive list):

• Health Care – As individuals have become more empowered to monitor their own health, Quantified Self values are going mainstream. Health providers will have increased access to collectively sourced, anonymized data. Projects such as Asthma Files provide a glimpse into the future of Thick and Big Data partnerships to solve global health problems.
• Repurposed anonymized data from mobile operators – Mobile companies around the world are starting to repackage and sell their customer data. Marketers are not the only buyers. City planners who want better location-based data to understand transportation are using Air Sage’s cellular network data.  To protect user privacy, the data is either anonymized or deliberately scrubbed of personal communication. And yet in the absence of key personal details, the data loses key contextual information. Without Thick Data, it will be difficult for organizations to understand the personal and social context of data that has been scrubbed of personal information.
• Social network analysis – Social media produces droves of data that can enrich social network analysis. Research scientists such as Hilary Mason, Gilad Lotan, Duncan Watts, and Ethan Zuckerman (and his lab at MIT Media Lab) are exploring how information spreads on social networks and, at the same time, are creating more questions that can only be answered by using Thick Data methods. As more companies make use of social media metrics, organizations have to be careful not to mistakenly believe that data alone will reveal “influencers.” An example of misinterpreting a signal from Big Data network analysis is the media’s write up of  Cesar Hildalgo’s work, suggesting that Wikipedia could serve as a proxy for culture. Read Heather Ford’s correction.
• Brand Strategy and Generating Insight – Companies have long relied on market analysis to dictate corporate strategy and insight generation. They are now turning to a more user-centered approach that relies on Thick Data. Fast Company’s recent feature of Jcrew makes clear that where Big Data driven management consultants failed, the heroes that led a brand’s turnaround were employees who understood what consumers wanted. One employee, Jenna Lyons was given the opportunity to implement iterative, experimental, and real-time testing of products with consumers. Her approach resonated with consumers, transforming Jcrew into a cult brand and tripling its revenues.
• Product/Service Design – Algorithms alone do not solve problems, and yet many organizations rely on them for product and service design. Xerox uses Big Data to solve problems for the government, but they also use ethnographic methods alongside analytics. Ellen Issacs, a Xerox PARC ethnographer speaks to the importance of Thick Data in design: “[e]ven when you have a clear concept for a technology, you still need to design it so that it’s consistent with the way people think about their activities . . . you have to watch them doing what they do.”
• Implementing organizational stratgy – Thick Data can be used as a counterbalance to Big Data to mitigate the disruptiveness of planned organizational change. Quantitative data may suggest that a change is needed, but disruption inside organizations can be costly. When organizational charts are rearranged, job descriptions are rewritten, job functions shift, and measures of success are reframed—the changes can cause a costly disruption that may not show up in the Big Data plan. Organizations need Thick Data experts to work alongside business leaders to understand the impact and context of changes to from a cultural perspective to determine which changes are advisable and how to navigate the process. Grant McCracken calls this the Chief Cultural Officer, the “corporation’s eyes and ears, allowing it to detect coming changes, even when they exist only as the weakest of signals.” The CCO is the go to Thick Data person, responsible for collecting,  telling, and circulating stories to keep an organization inspired and agile. Roger Magoulas, who coined the term Big Data, emphasizes the need for stories: “stories tend to spread quickly, helping spread the lessons from the analysis throughout an organization.”

FUTURE

We still have a lot of questions to answer for Thick Data inside organizations:

1. How do we report Thick Data up? Stories are effective, but stories require time, resource, and communication skills.
2. What are the indicators for successful Thick Data research?
3. How do we train teams in integrative Big Data and Thick Data approaches? There is greater demand for ethnographers as suppliers/providers than as employees inside organizations. There are not enough ethnographers working inside companies to internalize ethnographic research and to explore different ways to extend the insights of Big and Thick Data.

This is the time for ethnographic work to really shine. We’re in a great position to show the value-add we bring to a mixed-method project. Producing “thick descriptions” (a term used by Clifford Geertz to describe ethnographic methodology) of a social context compliments Big Data findings. People and organizations pioneering Big Data and Thick Data projects, such as Fabien Giradin from the Near Future Laboratory or Wendy Hsu, are giving us glimpses into this world.

So the next time someone talks to you about Big Data, try out your elevator pitch for Thick Data.  I’d love to hear about your experience–does this term resonate?  How would you improve the pitch for Thick Data? Or do you have a more effective or alternative pitch to share? What are other opportunity areas for Big Data and Thick Data to play together?

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In the upcoming months, I will be curating a follow up edition to Nicolas Nova’s Ethnomining theme on how ethnographers are positioning ethnographic work in the context of Big Data. I’m looking for guest contributors, please reach out if you’d like to participate.

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