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Data Literacy for Family Caregivers: A Social Process

Anthropologist at Intel

“Data literacy is a social process, and the only way we strengthen that process is by building the institutional capacity. Frankly, I think Atlas is at the cutting edge of this.” – Dawn Nafus, Anthropologist at Intel

Much of Atlas of Caregiving’s strength as an organization comes from having assembled a team of industry professionals, thought leaders, and researchers to work together to think creatively about problems and explore solutions. Members of Atlas’ leadership team bring expertise and perspectives from a wide range of fields and professional backgrounds, resulting in a truly innovative and interdisciplinary approach to improving outcomes for family caregivers. Pulling methods and insights from anthropology, data science, and policy, to name only a few, has been instrumental in the development of the Atlas CareMap and other Core Research.

I recently sat down with anthropologist and Atlas advisor Dawn Nafus to talk about data science, the promises and challenges of a data saturated world, and how Atlas offers an innovative perspective to thinking about topics like these.

Dawn has been an anthropologist at Intel for 12 years and is a long-time advisor to Atlas. She has played a crucial part in designing the methodological framework that has informed Atlas’ research and that has enabled Atlas to better understand, in fine detail, the daily activities of family caregiving.

Trained in applied anthropology, Dawn cares deeply about using insights, perspectives and theories from the field of anthropology to solve real-world social problems. Applied anthropology is anthropology—not merely ethnography—applied to various situations. Regardless of the context, the discipline is highly scientific, steeped in a rich history that is riddled with dynamic debates meant to call into question possible methods for understanding humans, relationships, culture and traditions.

Dawn’s journey out of typical academic posts was due a lot to happenstance. “  I was interning at the R&D department at British Telecom, where they were looking at ‘big data’ alongside ethnography,” she explained to me. “It was through those networks that I ended up at Intel.”

Over the course of her time at Intel, her position has changed significantly. “Intel realized early on that a huge part of their revenue would come from emerging markets and so anthropologists became a valuable resource. However, as the language of UX changed and took hold of the technology industry, our role became really understanding the day in the life of the end user and how technology fit into that context,” said Dawn.

At Intel, Dawn works on product innovation by exploring how consumers experience products. To do this, she uses ethnographic research to understand, more generally, how consumers interact with wearables, experience time and self-tracking, and how they interpret data. Dawn works in the US and Europe and has been focusing on projects in community-based environmental health, and software for supporting data literacy.

Data Science and Anthropology

Dawn has been engaged in the Quantified Self organization, an interdisciplinary group of people using various forms of data collection to better understand themselves. “One reason I got into data was because [of] the need to unpack and understand what was going on,” said Dawn.

Wearables and biosensors, mobile phones and the Internet of Things (IoT) means data is being produced and captured everywhere always. Understanding these data sets can give us rich insight into how we move through our worlds, but that requires consideration about what it means to work with that data—and to consider contexts and perspectives regarding what the data is and what it can tell us.

Dawn’s work has been largely centered upon this. She has been interested in how people are going to encounter new data types—how will people come at the data? From what positions? “Data literacy is a social process,” Dawn says. “And the only way we strengthen that process is by building the institutional capacity. Frankly, I think Atlas is at the cutting edge of this.”

Interacting with data in a meaningful way is an ongoing challenge for both individuals and organizations. “Data literacy for consumers, therefore, is an important component to the project of using data productively and to do good,” says Dawn. “Organizations like Atlas, that introduce accessible methods for compiling and interpreting data to consumers, play an important role in promoting the type of data literacy that can empower people to identify their own solutions and improve their own lives.”

Why is Atlas of Caregiving Significant?

Atlas is unique because it does not subscribe to a mere commercial or medical perspective on the usefulness—or use—of data. As she explains:

Commercial use of data means dumbing it down, adding simple goals, like 10,000 steps on the Fitbit, that may be strictly superficial and not add any real value to an individual’s health and wellness. Conversely, medical perspectives on data focus on research objectives and the management of patients. These perspectives are often either overly complex and technical for consumers, or too generalized to large populations, not accounting for important context of different patient situations.

Instead, Atlas is helping people collect and interpret their own data. In this way, Atlas is similar to, or possibly a facet of, the Quantified Self movement which attracts people who are able to reflect on the data they collect about themselves and their day-to-day lives.

“Raj is picking up on this idea that people can, if given the space, reflect and ponder their world—but it’s not always obvious what exactly to keep track of,” says Dawn. “Is it the steps in one day or the trends that happen over an extended time?”

Built into Atlas’ CareMap and the CareMap Workshop is an intentionally designed environment to encourage teaching and learning about the data represented in the CareMap. The space is flexible, responsive to where people are at, and encourages them to consider things from different angles.

According to Dawn, part of the challenge is that interpreting data is part of a culture that says the data speaks for itself—that the data is a prompt to do other things. “If we don’t teach people to think about what the data means—if we keep just making more data without thinking about how to process and question and read and interpret, then we’ll keep on having these booms and busts,” she says.

To Dawn, the transformative thing about Atlas is that it doesn’t make those assumptions. “In a way, Atlas is a stake in the ground and a clear claim that the way we should be handling information is not to make assumptions about what anyone should ‘optimize’ – it’s more about helping people to understand how to think about that data,” she says.

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