The concept of connections and the flow of data has dominated the internet for the past couple of years. No one calls it that, they call it IoT discussions. But in the end connecting to things via the Internet and having those connections is all about data. Visual, logical, structural and all other forms of data. Produced by a series of sensors or devices. Relayed to a services of devices, all at once or one at a time.
The concept of connecting, or connections is a huge topic. It is a two-way process you have to connect to the device and then have a way to interpret the data. Data has multiple formats but the reality of data is that you need it in various time frames. There is data you need right now. There is data you need to evaluate over time. In fact you can break most data acquisition into a four quadrant chart.Or a chart like this one where we see the data as a process moving around the circle. At any point the data flow can reverse and data I will need soon can shoot up to data I need right now or down to archival data. Systems are designed to accommodate data moving in these paths. Critical data is provided real time. Data I may need soon is provided just-in-time and data I may need but not currently is provided as needed. The final data, archival is assumed to take longer and if you need it you don’t care that it takes longer anyway.
All of this crammed into a tiny device. Frankly most of them were deployed and people used those years ago. We didn’t have connected weather stations or connected video feeds around the country in the past. The video feeds went back into a room in a single facility and they considered that data. Now you get those feeds everywhere. I had a weather station at the school where I taught. We checked it every single day and provided a forecast for the student body. Now I can simply launch weather bug and find 24 weather stations within 5 miles of where I am. Or launch Netatmo and get a reading from outside my back window and 25 stations within 5 miles of me. The amount of data is in the end staggering.
Weather data is critical if you are going outdoors. If you are simply hopping in your car and driving to work weather data is less critical but traffic data is critical. You need to know what the traffic is like before you get in the car. So the function of the data exists in a bucket. What am I doing now? Going to mow the lawn, weather is critical traffic not so much. If you have a traffic issue on your front lawn there are other factors you should be considering like why are you mowing a median?
The nature of information, the relevance of that information and finally the presentation of that information become the factors of consideration. Looking at the jumble of words on the right it’s easy to identify the words that are larger and bold. It harder to provide a rhyme or reason for the jumble however. The presentation of these words is focused on identifying the words presented differently than the others. Based on that model you can quickly grab the words and report back that you’ve found them. It’s like the old adage when you are up to your neck in alligators it’s hard to remember your original job was drain the swamp. Find the word Technology in the jumble – it’s there but it isn’t called out.
Get me the right information, in a format I can consume so that I can make the right decisions. If we consider John Boyd’s OODA loops here, we can consider that proper data orientation allows for cleaner observation and good fast decisions.