Instrumenting Innovation with Thinking Things

Of Blobemes and Blurbemes– The Future of Experience Research and the Internet of Everything

Professors: John Cain, TJ McLeish, Peter Binggeser, Laura Mast 

Team members: Katie Kowaloff and Keta Patel

Twenty years ago, the business world began a shift akin to the industrial or the informational revolutions: a shift that placed users and their experiences at the center of the value chain, as the driver for innovation. As with any successful technology or process, the market has eventually turned “user- centered” into table stakes—a required, but no longer differentiating approach. On the horizon is another shift. Whether you call this new landscape “sensor-driven” (McKinsey), “smart systems” (The Economist), or The Internet of Things doesn’t matter so much as its implications for design and innovation:

1  Sensor technology has things talking. Everyday consumer products; homes, offices, and retail spaces; civil infrastructure, and even the natural environment—all have the capacity to communicate. To deliver huge volumes of real-time data at a level of detail never before possible. Data that can be used alone, or correlated with other quantitative or qualitative sources, to deliver a powerful new kind of intelligence that fuels creativity.

2  The world-as-information-system will give us new ways to measure and assess the effectiveness of design and innovation itself. The goal of the class is as follows:

• Start by building thinking things—objects and environments instrumented with a readymade sensor. Example sensors include Twine, MeMoto, Fitbit, FuelBand, RescueTime, the Android or iPhone platforms, and so on.

• Instrumentation methods will provide remote access to communities and contexts of use that allow you to engage with them without direct interaction or observation.

• To combine our instrument data with secondary research to place the data gathered within a larger body of knowledge.

• The final goal of the course was to develop or use existing algorithmic models to compare and interpret data; dynamic models of consumer understanding that shed light on the initial hypothesis through a database, visualization and analytic tools to make sense of it all.