This article originally appeared on the BeyeNETWORK.
Sometimes technology developments seem to be unconnected. With additional thought they can be seen as sharing properties relevant within a business intelligence
An interesting confluence of wireless and miniaturization technologies has helped to create “smart dust”—tiny wireless devices designed to be sensitive to various measurable phenomena such as light, vibrations, humidity, temperature, etc. Each device, containing its own processing unit, power supply and two-way mesh-radio communication capabilities, would be able to communicate with other devices within a reasonable distance, forming what is called a “sensor network.” The concept is that these miniscule devices could be “sprinkled” across an area to help register and log measurements of various sensations and then propagate those measurements to a corresponding business application.
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For example, smart dust sensors could be distributed across farmland to measure acidity or chemical levels in soil. Other examples might include sensing abnormal vibrations that precede earthquakes, detection of chemical poison molecules as a hazard detection application or used in a hospital to capture numerous body function measurements in the critical care unit. This small list of examples is just scratching the surface. Smart dust is intriguing because of its potential for widespread use—any place where there is a need for multiple measurements of multiple sensory aspects to be communicated and analyzed, smart dust can fill the bill.
Radio frequency identification, or RFID, technology, employs a different strategy for communication. In the RFID model, a unique identifier is stored on a device (called a transponder or a “tag”) containing a tiny processor attached to an antenna. When an RFID tag is within reasonable proximity to an RFID reader, which is a device that emits a signal activating the tag, the unique identifier is captured and can be communicated to other computers within an application system. The kinds of applications suitable to an RFID system are those that need to track objects as they move from one location to another. An example is a package delivery application, where an RFID tag is attached to a package at the point of acceptance, and the associated identifier is captured at any one of a series of readers within the operations chain, such as when it is loaded onto a plane, or taken past the door of a delivery truck. There is interest in using RFID technology for personalization, so for example, giving a retail organization’s best customers a courtesy card with an embedded RFID tag will allow in-store readers to recognize those customers and supply them with special content or offers in real time.
On the business intelligence front, both of these technologies pose some interesting opportunities. For sensor networks, each measurement captured by any of the devices within the mesh can be seen as a transaction, and the scale of data that can be captured is almost limitless. This provides mountains of fodder for analysis, whether it be sliced and diced across the measurement dimensions, or subjected to data mining methods in search of relevant patterns. And for the personalization aspect alone, the value of an in-store RFID framework is that it can capture qualitative data about more than just transactions, but can also log movement patterns that might help in understanding how different people shop. In turn, individuals can be classified into segments based on their similar shopping patterns. And, by providing a real-time feedback loop between the individual and an analytical engine, not only can offers be generated in reaction to customer behavior; one might see ways to influence customer behavior via that same feedback loop!
But one other aspect of both sensor networks and RFID systems is the concept of collaboration. With the sensor networks, each device interacts with some subset of the other devices with the ultimate goal of propagating data to its final destination. In an RFID system, we can start to see how objects move in relation to other objects in the space, which provides insight into how the objects under observation collaborate.
And that brings me to the last item on my list this month. The proliferation of high-speed internet connectivity, coupled with the ubiquity of in-home wireless access points means that practically anywhere I walk in my neighborhood, I am likely to be able to connect to at least one, if not three or four unguarded wireless networks connected to the Internet. And in a recent conversation, the concept of a “neighborhood mesh” was brought up. The intention of this mesh was to provide a means for neighborly communication, so in case you wanted to borrow a cup of sugar, you could broadcast across your local mesh to see who could supply you with that sugar. Of course, this is ridiculous—if you want to borrow a cup of sugar, you can walk out your front door and knock on your neighbor’s door and ask him directly.
So what is the value of a neighborhood mesh? In the context of intelligence, one could start to look at the similarities between that kind of system and sensor networks or RFID technologies. I see the focal point being collaboration. Not in the sugar sense, but more in the social connectivity sense—how people interact, what value does that interaction bring to the participants, what are the rules for interaction, etc. More abstractly, how are the policies and procedures of social collaboration defined, agreed to and institutionalized? How does one join a mesh? Are there specific hardware and software needs? Do you have to join a special club?
In turn, I think the interesting aspect of the neighborhood mesh is self-organization. The essence of self organizing systems is that order appears seemingly out of nowhere, with participants abiding by a set of rules that have evolved, but might not have been explicitly stated. Yet the rules exist, and identifying and documenting those rules is a challenge in its own right. I hope to explore self organizing systems in greater depth in a future article, and look at its applicability to customer pools and customer networks.
David is the President of Knowledge Integrity, Inc., a consulting and development company focusing on customized information management solutions including information quality solutions consulting, information quality training and business rules solutions. Loshin is the author of Master Data Management, Enterprise Knowledge Management – The Data Quality Approach and Business Intelligence – The Savvy Manager's Guide and is a frequent speaker on maximizing the value of information. David can be reached at email@example.com or at (301) 754-6350.
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