Feb 15, 2019
Data Agents — 00010
Examples — An [I]nventory
In our first article (this is number two for those a little rusty on their binary), we made the following statement:
The purpose of a data agent is to input data then develop it into a higher value resource.
But what exactly does that mean? Let’s consider a few real word examples.
The Parking Sensor
One most people are now familiar with is the parking or backup sensor. In fact, sensors in general are pretty solid examples of data agents. The one in your new car or truck was even nice enough to go through a very typical evolution over the last few years.
It began simply enough. The sensor scanned for objects within a certain range of the sensor. If the data collected by the sensor suggested that an object was within a certain range, it would trigger an alert (typically a beep). A simple enough idea allowing the car to inform you to something you otherwise might not have been aware of. Perfect data agency… well, not quite.
Pure data agency arrived with the eventual evolution. After a few years of testing beeps, cars today will often apply the brake. Now that is true agency. Data says something is close. => Vehicle is automatically stopped. But of course, things didn’t stop there. Many cars today can actually park themselves. That is not a data agent per se, so much as an array of data agents working together.
The Traffic Sensor
Traffic sensors have been common a little bit longer. They too have evolved and have had discreet steps of data agency along the way. The simplest forms include those which allow rural side road traffic to get onto busy highways. You may have seen these on your last trip to the shore. The light is green unless a car rolls up an “trips” the sensor. Some are camera based — some still use trip wires in the asphalt.
These sensors were soon linked to timers and other processes to create more complicated traffic controls. Driving on a highway where these have been well engineered can be very satisfying. Sadly, only a few geographies have these sorts of systems. Many more geographies linked sensors to traffic cameras. Perhaps proving that ticket revenue is a higher incentive than convenience?
Real-world Data Agents Abound
A little thought and you realize just how prevalent this stuff really is, from the automatic doors at the local supermarket to your home thermostat.They have been widely known as sensors for decades. Assembly lines are full of them. They are beginning to make in roads into the supply chain as well. But three things should stand out:
- they are not readily associated with data
- they have failed to make strong in roads into many, many areas of the business world where they easily could have
- none of these feels half as complicated as “artificial intelligence”
And yet — they should be. They should be associated with data. They should be used in far more areas of business and production. And, to stretch this play a little, people should stop mystifying AI so that the complexity level stops complicating its adoption.
Data Agents should be utilized in nearly every facet of the business world.
So why aren’t they?
Education and complexity feel like two really strong answers. Data Agents are simple when processes are, too. If the process is poorly understood, if it is layered with unnecessary complexity — Data Agents don’t materialize.
This process is even worse when others (think sales guys) opt to inflate and sensationalize the complexity. Artificial Intelligence is full of it… literally. Or is that figuratively…? Either way, the “mystique” is not helpful. It delays adoption, increases costs, and confuses the value prop.
These days sensors are everywhere, or at least it feels that way. But they are not in the places where a name like Data Agent would apply. They are not generating the actions and the value that would improve so many businesses and organizations. But for that, we need to dig in further.
In our next article, we will start to pull apart the processes where Data Agents could add value. We will also break down some of the key concepts and challenges for most businesses. So stay tuned. And as always — thanks for reading!
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