Managing information lifecycles and considering the impact of device used in consuming IoT data…

The nature of IoT devices interests me. I’ve talked about the types of data produced in an IoT system. Where the device may be left somewhere and you interact with it, the device may be worn by someone or you may be carrying the device with you. IoT devices have two types of communication active and passive.

So I started thinking about a matrix and what that would look like and got the table below.

Device

Stayable

Wearable

Portable

Passive

Active

Proximity of connection

Proximity of the data

Nature of data time

Cellular phone

   

X

X

X

X

X

Limited by screen size

Security system

X

   

X

X

X

X

Limited by receiving device screen size.

We could spend days moving things into the table but I realized that the interesting thing for me (at least today) were the last three columns. The proximity of the connection has changed in the last 20 years radically. The connection could be as simple as your cellular device to the carrier you use and from that to the Internet to the information. The data may be collected by the device locally and stored locally or the data may also be collected from remote sources and then stored locally to be shared. Finally there is the concept of data time.

clip_image002Data time is something I’ve made up to try to begin explain the relevance of time to data. Father time (sorry for the sexist image there) actually applies TTL’s (time to life) differently to data than to identity packets. Data’s life has two distinct dimensions. The first is the time you need the information in. Think of Jeopardy. The question is revealed and you have limited time to produce an answer. You either have it in the end or you don’t. I’ll take IoT devices for 200 please.

The other dimension in which data operates has to do with its ability to solve the problem. The further you are from right time, right data the less relevant the data is. That makes the last piece the nature of that data critical. Cloud changed the parameters of the information sourcing radically. It can be anywhere. It still has to solve the problem. In the book Transitional Services I showed a system called DLM© that was designed as a KM system to pull information quickly from the napkin to the board room. The stated tenant of that solution was that people access KM systems to solve problems.

The other side of data is its time to live. A weather warning only lives as long as its issued time, or if extended its modified time. Knowing, as you dig your car out of the snow, that 10 inches or more of snow was predicted for the night before doesn’t help you as you shovel. Knowing what is coming, knowing when it is coming and getting that information quickly is of course critical. The other side of the problem is the reality of the device you are using. A complex financial model shared on a cellular devices screen is less than useful. In fact it is dangerously close to a waste of time. Information lives in both time, and device space. It has to be relevant to the user consuming it as well as timely in its nature. Late data presented on a screen that is too small doesn’t help. I believe the old saying is trying to cram 20 pounds of tomatoes into a ten pound bag. You can do it, if your goal for the tomatoes was making tomato sauce.

Hence the last three columns. Where is your connect? Where is the data? And in the end do you have a device capable of consuming the data as it exists without modification. As the universe represented by IoT devices becomes more and more prevalent, the reality of the last three questions will change a lot of things.

Not just the screen as a service. But the information as a function of knowing what screen you have available.

.doc

Scott Andersen

IASA Fellow!