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. In my PhD Sociology program, professors often told us 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 his talks, de Waal shows a video of 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.

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

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 Strategy — 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

The integration of Thick Data raises new questions for organizations.

How do we report Thick Data up? Stories are effective, but stories require time, resource, and communication skills.

What are the indicators for successful Thick Data research?

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.

Even though we have a lot of questions to answer about Thick Data, it’s important to keep in mind that 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.

It’s also important to remember that there is a lot of progress to be made on Big Data. According to a Gartner study of companies who had invested in Big Data capabilities, only 8% of were doing anything significant with their Big Data. The rest were only using Big Data for incremental advances. This means that a lot of companies are talking and investing in Big Data, but they aren’t doing anything transformational with it.

For enterprise and institutions to realize the full potential of Big Data, I believe they they need Thick Data. And that is why ethnographers are more needed now than ever. We play a critical role in keeping organizations human-centered in the era of Big Data.

_________________

Since writing this updated article in 2015, I gave a talk at TEDx with a business audience in mind for why every organization needs to prioritize Thick Data and the integration of it with Big Data. If you’re struggling to get alignment for the importance for Thick Data, share this video with your leadership and management teams.