Reality Mining

Roland Piquepaille points to an article in SensorMag:

Emerging technologies are creating a real-time, high-bandwidth, global sensor network. The most visible component, the Internet, has become fundamental to 21st century business. The evolution of low-cost, networked sensors, often directly Internet-enabled, is bringing sensors out of their traditional closed-loop realms into the rest of our reality. Don’t believe me? Consider cell phones: There are 1.4 billion active cell phones in use today with more than half a billion units sold last year. As cameras become a standard cell phone feature, we’re becoming the most connected and instrumented people in history.

As sensor and communications technology continues to develop, we can envision a very different Internet than the one we use today. Rather than sending messages and browsing Web pages, we may experience new interactions such as experience sharing and browsing reality.

Data mining, defined broadly as extracting useful information and insights from data, may be the untold half of the sensor networks story. Given the potentially huge amount of data streamed by live sensors, algorithms to fuse, interpret, augment, and present information will become an increasingly important part of networked sensor applications. In this article, we’ll show examples of data integration, analysis, and visualization of sensor information.

We call the data mining of sensor streams “reality mining” to emphasize the direct mining of insight from operations-relevant sensor data streams. Reality mining provides an insight infrastructure between detection and action, allowing businesses and other organizations to use sensor data in valuable ways. For example, adding sensors to stands of trees would allow experts in a wood products company to monitor tree growth for operational efficiency and yield. Combining these sensor data with models of tree growth and projections of product markets as the trees mature could let the company make resource allocation decisions today to maximize profits later.

Managing Paradigmatic Change

HBS Working Knowledge has an article by Jonathan Byrnes:

Paradigmatic change is very important in business. It has the potential to create major new value and to renew a company, but it is very difficult to accomplish in the absence of a business crisis. Managing paradigmatic change is fundamentally different from managing incremental improvements to the existing business.

This book grew out of Kuhn’s research on the history of science. Before Kuhn’s work, the prevailing view of knowledge building in science was that it was a linear process centered on the so-called “scientific method.” According to the traditional view of this process, scientists posit hypotheses, test them, and in this way, build knowledge. However, when Kuhn looked closely at what actually happened, he found that this could not be further from the truth.

Instead, Kuhn found that knowledge building in science was a process that was marked by occasional great lurches forward. In fact, most science took place within the context of a broad, tacit, explanatory framework that he called a “paradigm.” The Aristotelian system that theorized that the sun revolved around the earth is an example of a paradigm.

What Kuhn found in science plays out in business every day. A manager seeking to create paradigmatic change, whether in market focus or vendor integration or manufacturing process, will hit a wall of “the way we do business,” that is analogous to Kuhn’s paradigm.

As in Kuhn’s process, simply showing evidence that a fundamentally different way of doing business would provide higher returns will not be sufficient to motivate paradigmatic change unless a dire crisis is clearly imminent. It will be ignored much as Kuhn’s anomalies were ignored.