Revolutionary tech industry concepts such as Lean Startup and Agile Software development are underpinned by the age-old scientific methodology.
I spent 25 years in the “Ivory Towers” of academia leading my own brain science of human learning and performance laboratory. I am a scientist by trade, and when I first started working in the private sector, a colleague suggested that I read The Lean Startup by Eric Ries, saying that the “build-measure-learn” approach advocated in this book was revolutionizing product development.
The idea is that you build a minimum viable product (MVP), release it to users, measure their interactions and perceptions, and learn from the data. Once this cycle is complete you use what you have learned to improve the product and repeat the process. Ultimately, a high-quality product emerges. This is very similar to the equally revolutionary agile approach to software development that emphasizes interaction, collaboration and responding to change.The idea behind the Lean Startup concept is that you build a minimum viable product (MVP), release it to users, measure their interactions and perceptions, and learn from the data Click To Tweet
After reading about these approaches, I immediately saw that they both derived directly from the scientific method that has been in existence for thousands of years and which I had always applied in my own work.
As a cognitive neuroscientist with an emphasis on training, I was drawn immediately to xR (Cross Reality) technologies. As I have written in other articles, I believe that these technologies have the potential to revolutionize training. They reduce the cognitive load associated with the problem of mental visualization and representation, and can facilitate extensive behavioral skills practice, such as soft skills in situations when this might be difficult outside of a simulated environment.As a cognitive neuroscientist with an emphasis on training, I was drawn immediately to xR (Cross Reality) technologies Click To Tweet
Although there are clear examples of xR entrepreneurs who embrace build-measure-learn and are developing high-quality products, my experience is that the more common situation is one in which entrepreneurs are excellent at building the product, but fall short on the measure and learn components. What are usually lacking are high-quality measurement tools that provide actionable insights. With poor measures, learning will be poor as well, and the build-measure learn cycle will therefore be broken.
It is the beliefs, preferences, and – more importantly – the interactions, of the relevant customer base with the product that matters. Unfortunately, however, instead of implementing objective measures, all too often the product is a reflection of the subjective beliefs and preferences of the builders.Without implementing a rigorous, scientifically-grounded approach to development, product iterations are random, subjective and could make a product worse, not better Click To Tweet
As scientists-in-training learn on Day 1, subjective beliefs are limited by an individual’s experiences and preferences, and are therefore ineffective at driving product development. Because the measure and learn phases of build-measure-learn are broken, the result is often an underwhelming and underachieving product.
This does a disservice to the emerging technology sector, as it is difficult for high-quality offerings to break through when so many low-quality offerings exist. I have seen this first-hand in my role as an industry analyst in the Learning & Development sector.
I have spoken with numerous thought leaders and product development experts in the corporate training sector who focus exclusively on computer-based training. Some of the more forward-thinking of these individual are intrigued by the potential of xR technologies in corporate training, yet the majority of them are underwhelmed. This is worrisome because corporate training platforms represent a huge potential market for xR technologies to train important skills.When I talk to xR companies about implementing objective measures of preference and interaction a common response is that there is no money in the development budget for this Click To Tweet
When I talk to xR companies about implementing objective measures of preference and interaction in the service of learning more about their product, however, a common response is that “there is no money in the development budget for this.” The implication being that measuring and learning are not necessary to success. My response is simple: “You can’t afford NOT to have money in the development budget for this.” Without implementing a rigorous, scientifically-grounded approach to development, product iterations are random, subjective and could make a product worse, not better.
The xR technologies have the potential to disrupt a number of commercial sectors, and new xR startups are being created on a daily basis. Some will succeed and most will fail, and although I can’t predict the future, the odds of success will be significantly increased for those companies who embrace build-measure-learn and agile—that is, who fully embrace the age-old method called science.