Kenneth Snyder is the founder of the London-based start-up LifeGadget which is a platform for aggregating and analyzing one’s social, activity, health and wellness data through a contextual interface. Ken has almost two decades of experience in Information Technology through a diverse range of experience spanning from leadership positions at established players such as Sapient to founding roles at various start-ups. When Ken is not working on his next project he shares his entrepreneurial passion through various strategy workshops in the United Kingdom.
Here are my 5 questions with Ken and his answers:
1) Only a few years back there was a budding hope that simply empowering someone with their own health data, with nothing else needed, would be enough to see measurable improvement based on the attribute being measured (see Data is the Next Blockbuster Drug). The current general belief is that some sort of guidance is a necessary component for human improvement and that data, for the simple sake of data, is an ineffective intervention. What is your point of view on the importance of data and its role given this context?
The word empowerment means different things to different people. I believe data is very valuable and is allowing us to see correlations and patterns that we simply did not have access to before. For me there are four powerful aspects to tracking data:
- The ability to reflect on historic data to identify patterns, patterns which are pretty much imperceptible experientially.
- The ability to share historic artifacts about yourself with others – be it your doctor, a wellness advocate, and/or social supporter such as a family member. For many people memory is a poor substitute for the truth.
- The ability to make predictions and correlations based on multiple data sources. For instance, weight by itself can be interesting, but put that into an ecosystem with other data and it gets a lot more interesting. One can begin to derive secondary effects and make better choices.
- The ability to make data sharing frictionless and explicit. In other words, data sets are becoming rich and the integrity of the data is getting better as we move away from pen and paper.
That said there are still some universal truths that are important. For instance, sustainability is one. The four aspects I just mentioned don’t mean anything if the means to which one tracks their data is difficult. If the process/method is difficult then utility is compromised. Another truth is that people’s goals change and interest in anything tends to trend in an angulating fashion. This needs to be incorporated into the ease of use of collecting data since it’s a norm, and developers should build products around expecting data to come in ebbs and flows.
2) LifeGadget is being developed in part so that people will better understand their behavior and choices holistically. How profound do you speculate facilitating better access to biometric and behavioral data will move the needle towards allowing users to find unique correlations in aspects of changeable behavior that aren’t available today?
There is a balance between the value data provides and the effort needed to get data. We are getting better at lowering the effort threshold by reducing the friction between users and their data. We are also making data more enjoyable through various methodologies such as gamification. Regarding your question, single variables are important but it is exploring the complex relationship between those variables that ultimately will really move the needle.
For someone to really understand what is going on, they need access to a more complicated picture of their environment. As individuals we are usually not well-suited (without good tools) to explore our raw data and extrapolate meaning from that environment. Secondarily, even those that might be great at Excel modeling and have the aptitude to generate meaningful complex algorithms won’t have the time to do it just for themselves. Great tools let people bring forth observations quickly and easily by finding meaningful relationships in the data effortlessly.
3) One of major themes of this year’s Quantified Self Conference was the fact that hardware and software manufacturers have done little to standardize biometric data types. With the exception of the FIT Protocol and Open mHealth Architecture data standards for biometric data practically do not exist. What do you perceive as the implications of this, both as an advocate of the space and a product developer?
We are still in what I call a “Wild West” environment, meaning rapid change is taking place. At a personal level I naturally gravitate to these types of environments. This type of great change is very exciting. Since it is still a land grab you have players like Nike that do not want standards because it gives them a competitive advantage. Also it is important to note that not having standards creates a lower cost for start-ups and pioneers because they’re not limited by boundaries. However, from an infrastructure standpoint standards are starting to develop and that is unlikely to slow down… things like OAuth 2.0 and RESTful APIs are making data integration easier and cheaper to orchestrate.
I believe when a standard(s) do begin to take root you will see a very rapid move towards standardization. I suspect this will happen – the drawbridge will get lowered if you will – when businesses see value in collaboration. This will naturally happen as the ecosystem evolves. The industries that service this data will push for it and data producers will either fall in line or face exclusion.
4) Aside from the issue discussed in question three, what is one thing you would do today to improve the biometric tracking industry at large if you had unlimited resources?
I think one potential roadblock is the inherent cautiousness of the healthcare industry. If I had unlimited resources I would use them to influence the constructive disruption of the healthcare industry. I’m not suggesting we throw out caution, but I am suggesting we don’t let it get in the way of innovation. A great example is the stethoscope. The stethoscope was invented and it took another 30 years for it to get adopted by the healthcare industry. These types of delays as it pertains to biometric data innovation would be catastrophic. People need better access to genetic data and data generated by remote monitoring now. There is so much potential but progress is being hindered by legal and risk-based boundaries. For example the Federal Trade Commission is blocking the ability to see real-time glucose monitoring… why? Seriously, what is the harm in that? This is where I would like to see the industry improve.
5) The space is evolving rapidly, in part driven by the need for innovation to help improve healthcare. A recent article from CNN (Tracking Your Body with Technology) suggests that mind-blowing devices are on the horizon. From what you have seen and heard, what excites you the most about the future of self-tracking, biometric hardware, and/or mHealth?
I am excited about the rapid growth of this sector and the acknowledgment that it is beneficial to people (and these benefits are only getting better). I am excited to be involved in this not only professionally, but also personally. I started going to Quantified Self meet-ups because I have been tracking my own data for years. I’m excited that devices are improving with each iteration. For example, sleep tracking devices right now, in my opinion, are good but not great. I’m excited to see devices such as the Zeo get better because sleep quality is such an important piece of the puzzle… so we need better data regarding sleep. Another thing that excites me is seeing the way we track food intake improve. No one has gotten that down yet but someone will. This goes back to my answer to question two… right now the effort to track food is too high to maintain sustainability, but someone will solve this and that excites me. Lastly, it excites me to think that blood and biometric markers will likely be available to consumers without having to puncture the skin. Things like cholesterol, reactive protein counts, and other standard measures will be easily obtained without a blood draw. Once this happens we are going to be able to do some really great correlative analysis and really empower people with a clear, unique picture of their data and what their data means.