No, this isn’t a joke. Rather, it is a comment on the centrality of transdisciplinary collaboration for informed and exciting data-driven futures.
Data Analytics, Wearables & Fashion Tech’s Elemental component
When I think about these exciting opportunities around mass customization, improved well-being, better in-person interactions and sustainability—whether in fashion, fitness items, smart homes, warehouses, or wearables–the underlying source of the “magic” is the same: to make the invisible visible or to characterize or transform it in some way (here I note the shared language between the fashion and tech worlds—magic, transformation, etc).
In other words, to sense and “data-fy” our world. This data-fication is truly the elemental component of our future and we need to use powers of observation and understanding of states of data as matter to derive insight. People like Sonia Sousa at Kenzen are transforming liquid sweat into actionable insights about hydration (based on sensing and measuring things like sodium, metabolites, and glucose).
People like Christian Dalsgaard in companies like Ohmatex are proving that the distance between washable electronics that blend seamlessly into fashion in service of on-body data capture is not that distant of a reality. Others like StretchSense are able to transform physical motion in a fabric into data that characterizes that motion for usage as a point of interaction (we’ve done a number of examples of this sort of fabric-based interaction at Intel, I was directly involved in one for Chromat where a simple gesture made garments glow). In Stockholm I shared the stage with Niall Murphy from Evrything who has created a platform such that all clothing could be manufactured with a digital presence.
More and more companies are delivering heart rate and basic activity as “table stakes” in our wearable world. I foresee a few efforts here that are nascent but necessary. Bear with me, I’ve not fully composed thoughts on this, so I’m throwing these out based on a stream-of-consciousness thought! First, standardization of the “table stakes” forms of data. Recent research has suggested that while heartrate is reasonably consistent among devices (both wrist, chest, and beyond), activity characterization is a little less reliable, and the derived values for the actual “so what”–the meaning or insight— like calories burned is wildly inconsistent—rendering what should be the most impactful part of the insight not useful. Second, a broad expansion in other useful areas of data driven by unique sensors to understand the world around us. Smaller devices for sensing gasses, understanding elements of performance, and so forth. Third, improved ways of making sense of the data.
Machine Learning, Artificial Intelligence/Contextual Intelligence will go a long way to helping us understand the patterns in our data, in particular when we have increasingly complex data sets. Fourth, interaction with data. I like to think that there are some increasingly natural (to average humans) ways of interacting with data right on the horizon. In some ways, the Oakley Radar Pace which we worked to deliver with Oakley last year took multiple data sets and combined them with contextual intelligence (in this case running and cycling coaching) and importantly, natural language interaction (Intel’s Real Speech) to make interaction with the device the way people already interact—spoken conversation: “What’s my workout today?” “How am I doing?” I think we’ll continue to see ways that disparate data is combined and we’ll see increasingly human ways of interacting with it.
Perhaps the most important of these efforts is to begin serious attempts to both protect and empower people with their personal data. For now, data control, access, and ownership typically favor larger companies. I poked at this in our panel discussion at the Stockholm Fashion Tech Talks. After the panel Cristina Stenbeck (whose investment company Kinnevik has significant investments in retail fashion platform Zalando) showed interest in the topic in light of the upcoming General Data Protection Regulation that will supersede the EU’s previous Data Protection Directive in mid 2018. These new regulations build on the strong OECD framework for personal data that included notice, purpose, informed consent, security, disclosure, access and accountability.
While these started as recommendations, the new directive will go farther to regulate the processing of personal data. When you begin to interrogate the ways in which personal data might be exploited, it’s not hard to see how there are both economic and human right underpinnings. At the same time, I’ve seen little effort to empower people to leverage the fruits of their “labor”–their personal data–in ways that monetarily benefit them. This is something I personally believe we will see in the not-so-distant future (I’ll have to write something up on that).
All of these efforts require transdisciplinary collaboration and perhaps a bit of alchemy to transform data into an increasingly personal form of exchange that extends well beyond the engineers who do the technical work in enabling these capabilities—social science, engineering, ethics, data analytics, material science, fashion tech design, and beyond. Interestingly, few organizations are designed to pull these disparate personalities together. How will we proceed?