The Land Beyond Transactions - InformationWeek

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11:09 AM

The Land Beyond Transactions

New streams of information, such as RFID and metadata, are creating interest in two key BI advances: "location" analytics and metadata mining.

Analytics add business value by leveraging corporate data streams. Until recently, the focus of business analytics has been the transaction data stream. The bulk of business analytics today is wedded to corporate transaction systems managing financial, demand, and supply chain transactions. These transactions reflect what used to be paper documents — orders, invoices, payments, and so forth. And there's still a long way to go to optimize and extend the scope and reach of transaction analytics. But as new data streams are expanding the domain of analytics, they're driving new directions in analytic technology. Two such directions are location and metadata analytics.

Look at any business today and the traditional transaction data stream is just one of many. The use of the Web for e-commerce — online sales, sourcing, and marketing — has generated new clickstreams to mine to better understand customer buying behavior and brand preferences. Digital voice traffic, email, and now instant messaging are also creating equally voluminous message streams that literally encapsulate much of the day-to-day conversations of doing business. Web site log files and call-center or email archive files are the basis for clickstream and message-stream data analysis. But what's interesting about the new directions in analytics is that the drivers are less the data streams themselves but new hardware and new ways of describing data.

Location Analytics

Location analytics is about creating business value gained from data derived from location awareness, the movement of people and items between locations, and location context. Location analytics is being driven by the proliferation of synergistic hardware, including global positioning satellites (GPS), mobile/cell phone networks, and radio frequency identification (RFID) tags. Broadly speaking, people-centric location analytics will depend on mobile phone and GPS networks, while item-centric location analytics will depend on RFID tags.

Locating people is already relatively accurate. With services such as FollowUs a user of a standard mobile phone can be located within 100 meters in an inner city area in the United Kingdom. The latest cell/mobile phones enabled with Assisted GPS (A-GPS) can bring that down to less than 40 meters. (For examples of the capabilities location-based services can deliver, see the Qualcomm SnapTrack link in Resources.)

But it's not just people that can be tracked. When a vehicle is fitted with a GPS-enabled device such as the AsItMoves locator, the location of the vehicle can be pinpointed to within tens of meters. And there will soon be literally billions of items (and people) that can be located once they're fitted with a RFID tag and come within range of an RFID scanner. A RFID tag stores data that allows a scanner to record the location of an object (and other contextual information) when it comes within the scanner's range by reading and writing data from and to the tag (if required). And these aren't the only devices generating location data. Closed circuit TV cameras in stores, streets, and highway tollbooths are also collecting location data in the form of timestamped images of people or vehicle traffic.

The use of RFID data is particularly interesting because of the potential and scope of RFID use in a business context. RFID tags have already found their way not just into "things" — such as delivery trucks and the items, cartons, or pallets transported along a supply chain — but also into animals and even humans. RFID technology is not only used for asset location tracking but also for identification and recording contextual data at a point in time. VeriChip is a supplier of RFID tags that have been implanted in humans to allow specific individuals entry into secure areas or provide medical information to doctors. Passive RFID tags carry data that RFID scanners can read — for example, a product identifier, a patient's medical status, or the origin of a cow in the food chain. Active RFID tags allow contextual data to be written to the tag as part of a read and write scanning process including location identifiers, timestamps, and temperature.

Currently, the business focus of RFID is in supply chain optimization. According to Jonathan Byrnes, a senior lecturer at MIT (see Resources): "Analytical applications improve supply chain coordination, ensuring that the right amounts of the right products are in the right places at the right times. An example of this is using RFID to get an early read on demand trends, and transmitting this information throughout the supply chain to align production and inventory levels." However, improving demand forecasting isn't the limit of RFID's analytic potential. RFID will become a key part of the "real-time enterprise" by providing new data streams about the location, movement, and context of both animate and inanimate objects within an organization.

But location analytics based on RFID faces a number of challenges. The volume of data collected could be enormous. With the potential for thousands of scanning devices operating in a large organization scanning at rates much faster than humans can create and post "transactions," we could move toward RFID databases that might reach multiple terabytes — maybe even a petabyte — dwarfing the largest data warehouses of today. Also, standards are required to oil the flow of data, like the use of globally accepted Electronic Product Codes (EPCs) and data interchange metadata such as the XML-based Physical Markup Language (PML). And there are important privacy and security implications when RFID is embedded in humans that go way beyond concerns about who knows when and where you bought a candy bar.

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