This post is part of a series on nearline future technologies and how they might impact living. I hope that, in addition to being enjoyable to read, they encourage you to think about how technology will effect the people you work with and for.
Radka loved the view from office. The west-facing windows on this floor of the Avallónë Data Cloud Inc tower swept out over the Grand Park with its comprehensive trail and networked education system.
Beyond the park the city itself scrawled a pattern of intermingled old and new construction, biological trees and network trees intermingled as well. At the distant edges the cranes of deconstruction chipped away at sections of the Dropzone.
The Dropzone was the local name for the strip of land along the coast where insurance coverage had been dropped. Much of it was being turned into beach or park. But in the meanwhile it was an 80 kilometer strip of cranes, jackhammers, and drifting trash. The official name of the area was the Dynamic Littoral Potential Zone.
Radka’s assistant brought in her physical mail for the day. Several postcards from old college friends on a vacation in North Africa, The Economist Print Edition, a few premium data reports, business correspondence from the European office.
She began to prepare a report on the past week’s activities on the data services mapping project, of which she was the primary director. She and her team had nearly completed signals testing and verification on several neighborhoods.
This would give them a complete assessment of data transmission capabilities not just at the house-by-house granular level, but the entire throughput capability of that section of the city. As the importance of data continued to rise, especially at the coasts, having this map completed would be extremely valuable for Avallónë DCI.
There were business model plays in consumer, business to business and of course government segments.
At the consumer level, the reliance of many core infrastructure systems–grid-connected solar/wind, refrigeration, security, water, pretty much anything that used power used or made power–on data connections required a thorough cross-check of the neighborhood’s capabilities; a direct connection at the house wouldn’t matter if the lines of the neighborhood were already full.
In addition, families who had children in training needed clean and clear lines to ensure that the training quality came through well. For many families this could be a primary reason for locating in one location or another. Similarly, some industries like international finance and emergency services required their employees to maintain data connections above a certain grade.
Managing and knowing the capabilities of segments of town created additional “neighborhood” layers that would map over other, older graphs of the city. Since so much of the infrastructure had been laid out ad hoc at the beginning of the century there was little rhyme or reason to it. In addition, the variety of line types and corporate players kept total throughput a mystery to anyone who couldn’t get physical access.
For many years, the physical access issue had been considered insurmountable. Most of the early attempts had been disastrous for PR. They made the early century “Google Truck” look like a children’s parade.
Radka, however, had cracked this one. She’d begun the project three years ago as an experiment and now it was running globally. A steady stream of network layer data had been filling the data tanks ever since she began using her “water vapor” technique.
Basically, she had identified existing human behaviors for which the data she wanted was a by-product. Sort of like water vapor being the by product of the engines the transport pods used.
Sending someone specifically to gather the data she needed from each residence would have been problematic: too costly, too much privacy intrusion. But when it came as a piece of a larger stream, or was an incidental data acquisition, it was relatively painless.
In this case, since she knew people moved house every four years or so, she knew there would be an opportunity to gather initial data for a section in that time. The challenge was that the depth of the data she needed was beyond what most people could assess.
Through some advanced agent modeling techniques she identified the qualities required of someone who could gather her data for her, including available budget for hardware sensors. From there it was a matter of matching these characteristics with existing data sets.
Luckily, Avallónë DCI had some of the most complete data sets available. It had paid for this tower and more than twenty like it across the globe. Connecting her requirements to a population query was trivial. She’d had about 75 candidates within the first three days of polling.
The rep score system had enabled her to weed that group down. Then she simply set a watch on the final candidates and placed bids on any work they did in one of her neighborhoods.
She had been careful not to specify which data sets she was after, always bidding on the complete collection. She didn’t want to tip her hand to Avallónë DCI’s competitors. There weren’t many of them, of course. But they were extremely aggressive.
She wrapped up her report and paged through the Economist starting with the obituary, which she always did. Not that she was especially morbid, but that she often found that was where the greatest detail could be found for understanding how things worked in the world today–by following the stream of events encapsulated in a single life or a single career.
She considered the wide variety of data sources and systems she was able to assemble in order to develop the network layer.
It wouldn’t have been possible outside of Avallónë DCIs data collection. Well at least not in the private sector. Not so much because of the technology but because of the attention paid to the value of a broad data set and the connections between different sets.
One of the obituaries she was reading was about a grain king in the plains. His life developing an agribusiness empire based on harvesting large amounts of wheat, shipping it east to Minneapolis, then shipping it back to the plains as cereal or bread or pizza dough. Profits only flowed one direction in his life, costs only flowed the other direction.
Her data system was a little bit like that. Something which could only flow in one direction.