Five Misconceptions about Personal Data: Why We Need a People-Centred Approach to “Big” Data

INTRODUCTION: BIG DATA GOES BOOM

We produce vast amounts of data in our daily life. Worldwide, we send 144 billion emails daily. Every second, we post 700 Facebook updates, write 600 tweets and initiate 35,000 Google searches. We transact $6.5 trillion yearly on Visa cards. These activities—plus texting, checking-in, pinning, Instagraming, signing-in, making mobile payments—all create a digital by-product. Data is superabundant. Figuring out how to extract value from all the digital exhaust is driving the current big data boom: a corporate-sponsored, frenzied and competitive race to gather and mine our personal data.

Data analysts use the term “big data” to convey the enormity and complexity of our digital output. For them, the data is very big indeed—and expanding. Ninety percent of all data created in history has been created in just the past two years. Worldwide storage is expected to increase from 329 exabytes in 2011 to 4.1 zettabytes in 2016. It is difficult to even translate what this metric means, but according to a 2010 Economist article, one zettabyte is equivalent to all the information in existence that year. One zettabyte would take something like eleven billion years to download using today’s broadband (Franks, 2012 p. 89). Big Data is, in fact, immense.

Analysts look at this immense aggregation of bytes, and see an unwieldy data set unlike anything they’ve crunched or made sense of before. Our data already exceeds current storage capacity, and of that vast amount of data, only about five percent is structured. Yet, despite the difficulty of harnessing its value, many are convinced that big data will become the world’s most important resource; the fuel for the next economy. In a 2011 report, the World Economic Forum declared our personal data a “new asset class” equivalent to oil and money in potential economic importance:

“Personal data will be the new ‘oil’ of the 21st century… It will emerge as a new asset class, and a person’s data will be equivalent to their ‘money’. It will reside in an account to be controlled, managed, exchanged and accounted for, just like banking services operate today.”

Conferences about big data are now being held all over the world, from Minsk to Malta. Chief Information Officers are regularly joining the executive suite and one HBR article even proclaimed the job of data scientist to be the sexiest of the 21st century. Merely the promise of personal data’s potential value has sparked the current boom. As Tricia Wang writes, “Big data can have enormous appeal. Who wants to be thought of as a small thinker when there is an opportunity to go BIG?”

As the hype grows, nearly every company wants a part of big data, but this gold rush potentially obscures much of the real value of personal data. First of all, it often fetishizes the data (and the data scientist), positioning it as the prize itself rather than the enabler of possibilities. Next, it can misplace the business opportunity: only a few companies have the capacity and resources to analyse data at this large scale. Lastly it risks forgetting the most fundamental rule of innovation – start with the person. We already know that successful product, service and experience innovation starts with an understanding of real people and their real needs, so why has this basic principle been largely absent from the obsession with big data?

This paper draws on research that looked to discover new value in the data boom through a global investigation of people’s experiences with and the new emerging business opportunities around personal data. It proposes a new perspective on personal data— one that shifts our attention away from what is technically possible and tantalisingly profitable to focus first on what people need.

We are not alone in calling for a more human-based approach to the big data discussion. However, we are advocating a people-centred perspective that runs deeper than simply putting data tools and privacy into people’s own hands (cp. Green 2012). We also want to move the discussion further than arguing for a more qualitative approach to the data itself (although this is an important point raised by others: see Crawford 2012, Rasmussen and Madsbjerg 2013, Wang 2013). Instead, our key point is that in the midst of a data boom, it is also imperative for businesses to think about how they can use the personal data to provide value back to the individuals who are both its source and consumer. This paper argues that the best opportunity in the emerging personal data economy may not be the mining, processing and selling of data. Rather, there is equal opportunity to discover the benefits that data can provide directly to customers and to deliver that value back through compelling services that address their specific needs, desires and frustrations.

METHODS: A PEOPLE-CENTRED APPROACH TO THE PERSONAL DATA ECONOMY

In 2012, at Claro Partners, we conducted a six month global consortium project investigating the emerging “Personal Data Economy”. It began with the observation that personal data is fundamentally changing the way we work, live and play. Data is the output but, more importantly, it’s also the input of a digitally-networked society. We could see a shift in everyday behaviour, noticing how people use personal data to make everyday decisions on things like what to eat, where to go, and how to get there. We saw new products and services, from the fitness tracker Fitbit and social app Highlight, to the adaptable thermostat Nest and the new banking interface Simple, transforming the way in which people interact with their data – and the value they receive from it. We call the collection of services that enable these new experiences the “Personal Data Economy” or PDE. The PDE is not built by solely mining data; instead it is built by using data as another resource in the creation of services that are valuable to people and delivered through experiences they want. Meaning, the PDE is an emerging economy of services, outside of data aggregation and targeted advertising, which focuses instead on meeting people’s needs in new ways. We believe it provides business opportunities as big as, or greater than big data.

In partnership with three international clients from the banking, telecom and technology sectors, our team interviewed more than sixty individuals, experts and start-ups in cities around the world, including San Francisco, London, Berlin, Tokyo, São Paulo, New York and Boston. Our research was grounded in the belief that in order to understand how to create value from personal data, we need to start with the person, rather than the data. So, instead of looking at data to try and understand what it might tell us about human behaviour, we started by looking into human behaviour to see what it could tell us about the role of data in people’s everyday lives.

We designed the research around human stories relevant to each location. For example, we talked with members of the flourishing Quantified Self (QS) movement in San Francisco. This is a community of “self-quantifiers” who track, share and make use of their personal data, with the intention of changing their own or others’ behaviour. They track a variety of behaviours around finance, health, diet, life planning, mood and mental acuity, in pursuit of new forms of self- and human knowledge. For them, data is like a sixth sense. In London, we interviewed urbanites to gain insight into how they use digital technologies and connected devices to engage with and navigate their city, both physically and emotionally. We observed how they use and create real-time data to orient themselves to their surroundings, fulfil immediate needs, participate in their community and to make decisions about things like utilities, transportation, health, shopping, public services and entertainment choices. We wanted to better understand these experiences. In Berlin, we explored data unrest, going from hacker dens to parliament to discuss digital rights with people who have political ideas and investment in personal data, including members of the Pirate Party. In Tokyo, we focused on NFC technology-enabled experiences and in São Paulo, on social networks and self-made business opportunities. All of these stories offered unique insights into the new experiences and behaviours emerging around personal data. In total, we conducted thirty-one 1:1 interviews.

In our research, we also interviewed twelve start-ups who are defining new offers built on personal data. For example, we met with the creator of Chromaroma (a game built on top of London Transport data and played by thousands of commuters), Kitakore (a Japanese recommendation engine), Gravity Eight (a site for collecting a broad variety of self-tracking information), and other start-ups looking to create entertainment, recommendation, tracking, discovery or other types of services built on top of personal data. Lastly, we met with twenty experts like Wired’s Ben Hammersley, Urbanscale’s Adam Greenfield, and MIT’s Sandy Pentland to discuss the role of personal data in disrupting both business and society.

Our interviews were more than mere conversations. We used collaging, follow-alongs, workshops, participant observation and other creative means to engage, be shown and told, and dig deep into people’s data-driven experiences. Below are some of the insights and patterns we synthesized.

KEY INSIGHTS

We categorized insights developed from our research into three areas: the consumer, the personal data experience and the misconceptions about personal data.

BUSINESS EXAMPLES AND OPPORTUNITIES

Businesses and start-ups are beginning to emerge with new business models and propositions that treat data as most valuable to users themselves. As part of our research we created a landscape to visualise the vast scope of these services in the Personal Data Economy. The landscape is an interactive tool that allows viewers to explore new propositions that deliver the value of personal data directly to the user. It includes offers around discovery, recommendation, social interactions, prediction, identity and access management, and the Internet of Things, amongst others.

FIGURE 1. Personal data economy landscape

One of the major discoveries in looking at this landscape was that while we included over 250 services, the majority of them are fulfilling a single human need: the need for mastery. These mastery services focus on using personal data to measure things, make one more efficient, help optimise a behaviour or achieve a goal. Location services like AroundMe exemplify this space. They use your location data and Google Maps to help you efficiently locate a restaurant, museum or shop. This is an obvious place to start, as it is a relatively straightforward application of the data. However, our research revealed other opportunity areas based on connecting more deeply to people’s emotional needs, like for example, the human need for intimacy. In a world flooded with data where all our relationships seem to merge into a single platform or interface of shallow interactions, people we spoke to expressed both a sense of loss and a craving for more intimacy. People were rediscovering email attachments as a means of directly exchanging with a single person, using more closed social networks like Path, or dividing social groups across platforms as a way to create the kind of intimacy they preferred. But can’t we imagine and create better services to enable intimacy?

Our research highlighted Snapchat as one company to watch in this space, and now we believe it’s incredible and rapid growth can be in part explained by its ability to respond to the need for intimacy in a way that many other services don’t. Snapchat is a real-time picture and video sharing service that allows users to decide for how many seconds others can see a photo they share. After the viewing period, the photo or video disappears. By letting people impose this time constraint, Snapchat gives context and a better experience to personal data sharing; it provides precisely the type of social-based privacy that many people crave. Furthermore, the photo’s transience makes the very act of sharing more meaningful and valuable to users themselves. It creates a moment of intimacy between two people, rather than burying that content within a larger data stream. That is, it feels more like sharing in the traditional sense.

Another interesting company in this landscape is Foursquare. Unlike Snapchat, Foursquare needs a huge amount of personal data to fully function. However, Foursquare gathers that user-generated data to provide relevant information back to users themselves. Foursquare offers personalised discovery and sharing rather than aggregating all user data to target users with ads, or help them be more efficient. For example, it can suggest where you might like to eat tonight based on your friends’ preferences; and it can provide this recommendation within your personal and social context. Furthermore, rather than starting with the data, Foursquare first created a compelling service with an engaging game element that generated the personal data it later used to build its increasingly-personalised offer. It is a service that keeps the user experience at its core.

Savvy users understand that just because Snapchat erases the image, or Foursquare gives them a badge, doesn’t mean that the companies are not collecting and storing their data. In reality, most people are keenly aware that companies have access to their personal data and understand that it’s the price they pay in exchange for the platform’s value proposition. In order to function, Snapchat needs access to your contacts. Similarly, Foursquare’s Radar needs to understand your social network to push alerts when you are passing a location where your friends are gathering. In the end, Foursquare and Snapchat simply deliver a better experience, and do so in an environment of trust, transparency and control. They offer a service relevant that consumers consider an equitable value exchange for their personal data.

CONCLUSIONS

It seems that today everyone is focusing on big data; how to make money from it, mine intelligence from it, and transform business from it. To be sure, big data can yield powerful insights and greatly impact the business landscape. However, this paper argues that big data in itself is not a strategy for innovation. Furthermore, the big data hype is also distracting us from other business opportunities and experience innovations that provide value directly to potential customers. We believe the PDE provides new opportunity for organisations to deliver real value to people and advocate for a people-centred perspective to create something meaningful and new.

This paper highlights the emergence of a Personal Data Economy, in which services provide value to people by building on top of their data (shown in its vast scope and variety in the PDE landscape). The intent of this paper is not to dismiss or ignore the Big Data Economy, but to emphasize that creating value in the Personal Data Economy requires us to take a very different approach. Business ethnographers and anthrodesigners, among others, have already argued for a new approach to data, pointing out that it would be a mistake to equate data-mined discoveries with real “customer intelligence” (Lee and Sobol 2012, Wang, Crawford). These writers rightly recommend we take a more qualitative, data with depth, or thick data perspective. However, a people-centred approach to data demands more than the usual Geertzian method. At Claro Partners, here’s what we think are the key principles to taking a more people-centred approach to the Personal Data Economy:

3. Start with the person. In the Personal Data Economy, data is not the oil but rather one of the resources in the service design toolkit. In the Personal Data Economy, we don’t begin with analysing or crunching data, rather we begin with thinking about people and their everyday needs. Data then becomes one tool to help design better services and experiences for people.
4. Become observers of the role data is playing in people’s lives. Data is increasingly something we interact with in our everyday lives. We need better understanding not only of the data we generate as exhaust, but also of how we live with that data and act upon it. Services designed with the idea that personal data is an inherent part of and crucial input into the experience, rather than just the output, will create new value in the PDE.
5. Identify new unmet needs. What are both the functional and emotional needs when it comes to experiences with our personal data? Many services in the PDE are, in fact, still quite functional. It is perhaps obvious how data can help us be more efficient, precise and informed, but the power of personal data is more than functional. Our personal data connects us emotionally to who and what we care about. It is a footprint of who we are, what we do and what we aspire to. It has the capacity to meet human needs such as intimacy, belonging and recognition. As one attendee at a San Francisco Quantified-Self meetup explained: “capturing the sense of a moment is different than capturing a data point.” Even for these data-driven folks, the emotion of a moment and the context of creation are critical to the value of their personal data. In the PDE, there is vast opportunity to consider how to meet these emotional and context-based needs, especially as consumers’ functional needs for information are increasingly satiated.
6. Create Tools. Create tools to help businesses identify, design, and create business models around more relevant services with data as an enabling resource to tap into when needed. This means rethinking the tools you currently use to include data as an input, an output, and at times even as an actor in a larger network.
7. Design The Whole Data Experience. Design how and where the data is generated, volunteered or inferred; how and when the user can access it; how they have to manage the data (or ideally not at all); how they can view it and manipulate it to create meaning; how they share, contribute, exchange the data, and finally if and when they archive, delete or retire the data.

We firmly believe that there is great business potential in personal data, but it will not be fully realised by aggregating and mining it. There are huge opportunities in the Personal Data Economy; opportunities to design ways to give value back directly to users through compelling services that address their specific needs, desires and frustrations. So, rather than go big with data, we recommend starting small. And rather than big data, we recommend to focus on relevant data. To understand what is relevant to people, we need to begin with their needs. We can use these defined needs as the first filter to work out what data is needed, how to access it, how to use it, and how to deliver it back to the user.

Abby Margolis is Research Director at Claro Partners, a Barcelona-based user-centred business innovation and service design firm focused on helping clients navigate disruptive change.

Ethnographic Praxis in Industry Conference Proceedings 2013, pp. 32–43. © American Anthropological Association, some rights reserved.