The Internet of Things (IOT) has expanded incredibly quickly. Believe it or not there are over 25 billion IOT devices on the planet right now. That’s right, billion with a ‘b’. More than three times the number of people. These devices include obvious things like fitbits, smartwatches, smart thermostats and things that aren’t so obvious like refrigerators and cordless drills. All these are constantly generating data, mountains of it actually. Information like your heartrate when jogging or how quickly you drain the battery in your drill you got last Father’s Day is being collected and stored somewhere. The challenge isn’t figuring out how to collect more data, it’s figuring out how to make the best use of the data we already have.
There are three basic levels of data. One of course is just the raw data, everything getting collected by all of these devices. Another is what is called meta-data, the data about the data. The best and most well-known example of meta-data is all the information that gets attached to your digital photos. That includes things like the time and place where it was taken and even (sometimes) the people in the photo. Finally, there is the transformed or processed data, data that has gone through some sort of process to make the information usable on a larger scale.
To help with that last part, artificial intelligence/machine learning (AI/ML) has been rushing to keep up with the growth of IOT. Lots of progress has already been made with over 5 billion of the 25 billion IOT devices having onboard AI/ML. What’s the advantage? Onboard AI/ML helps get things processed faster, rather than waiting for a server to collate and analyze the data. Also, if the device detects signs of trouble, the user can be made aware of it sooner. Of course, processing data from groups still requires that the data be collected elsewhere for processing, but for the individual, this could be a literal life saver.
Not that AI/ML is always used as it should be. Too often it’s used as an infallible prediction tool, a way of generalizing individuals. There are uses for that, but the problem lies in how these computer models are often treated as infallible oracles, even when broken down to the individual level. You can obviously get away with that with groups. However, when applied specifically to me or you, these things tend to break down. Just because we’ve always gone along with a certain group to by the latest smartphone, doesn’t mean you will when the next model comes out. If the AI/ML software detects a break from the pattern like that, it will either ignore it or spend time and resources reanalyzing data or gathering more in order to explain the deviation.
TARTLE has a novel approach to this kind of situation. Instead of spending a lot of time, energy and resources creating software to figure out why you didn’t go to the latest Marvel movie on opening day like you normally do, we thought we just might (brace yourself), ask you. That’s right, we, or the client purchasing your data through TARTLE would simply say, “The data you sell us shows that you normally get together with your friends and go see superhero movies on opening day. This time, you waited three weeks and apparently went by yourself. Any particular reason?” We don’t have to guess, and even better, you don’t have to answer. If you decide you are just fine keeping your reasons to yourself, you are free to do so. That’s the beauty of TARTLE, you get to decide what and when to share and why. Yes, that even applies to IOT devices, just include compatible devices on your TARTLE account and you then have control over the data those devices are sending out. Not so much as a byte goes out without your say so. TARTLE puts you back in control.
What’s your data worth?
