The Value of Information

VoI meets IoT

by Sam Savage

Severn Darden, one of the founding members of Chicago’s Second City comedy troupe, had a routine in which he played a Professor of Metaphysics.

 “Now, why, you will ask me, have I chosen to speak on the Universe rather than some other topic?’” he would begin in a thick accent. “Well, it's very simple. There isn't anything else!”

In the Information Economy, the same could be said for the Value of Information (VoI).  

It started over half a century ago with a seminal 1966 article entitled Information Value Theory by Professor Ronald A. Howard of Stanford. When I sat in on Howard’s class as an Adjunct Faculty member in the mid-1990s, I was amazed that with all my years of technical education I had never been exposed to this fundamental idea. And I continue to be surprised at how few people are aware of this concept today. I believe that the Internet of Things (IoT) is about to change all that.

My epiphany came during a recent presentation by W. Allen Marr, Founder and CEO of Geocomp, a Boston area geotechnical engineering firm that determines how the earth will respond when you build a bridge or skyscraper on it or drill a tunnel through it. Marr started by pulling out his smart phone, which displayed a live map of Chesapeake Bay, with colored dots representing the recent movements of sensors embedded in a tunnel currently under construction. Then he went on to discuss the use of sensors in an earthen dam, discussed below, which for me sealed the deal on the connection between the Value of Information and the Internet of Things.

First, here is my own informal definition of VoI. In any situation in which you can imagine saying I wish I had found out such and such before I had to decide between this or that, ask what you would have been willing to pay to go from decide then find out to find out then decide.

For example, you can decide to buy a stock or not today, and then find out tomorrow if it goes up or down. How could you find out what a stock was worth in the future and then decide whether to buy it? Easy. Stock options let you do just that, so option pricing is a special case of VoI. 


Here’s another example. Suppose you like to drive your Ferrari fast over a stretch of road where you know there is a 20% chance of a radar trap with an associated $500 speeding ticket. You must decide how fast to drive, then find out if you will get a ticket. Your expected loss is 20% x $500 = $100. What is the value of information provided by a radar detector with a 90% accuracy? You get to find out if the detector goes off, then decide to slow down. Now there is only a 2% (10% x 20%) chance of getting a ticket, so your expected loss is 2% x $500 = $10. The VoI is the difference or $90.  

Note that if you are driving a clapped out 60’s vintage VW Bus on the same road, you have nothing to decide about speed. You need to keep the pedal to the metal just to keep up with traffic. Without a decision that could be changed by the information, VoI is zero. 

But let’s get back to Marr’s dam story. 

Suppose the acceptable rate of failure for an earthen dam is once in 10,000 years. And the dam in question looks pretty good until someone points out that it is upstream of a nuclear facility. Uh oh. Now the rules say you need to reinforce it to a rate of one failure in 1 million years. So get out your checkbook, because to patch it up to that strength will cost $800 million.


But here is an IoT idea. Consider a sensor network embedded in the dam that has a 99% chance of detecting a failure before it happens. And suppose that the $800 million patch job could be done quickly and would still have a 99% chance of saving the dam after the sensor network goes off. We have gone from decide to spend $800 million, then find out if we really had to, to find out if the dam will fail then decide to spend the $800 million. 

So, what is the value of the information provided by the sensor network? Of course, one must really look at the net present value over an extended period, the reliability of the sensor network, etc, etc,  but let’s start with the first year. We have an operational sensor network which reduces the likelihood of dam failure to the goal of about 1 in 1,000,000, but since we did not reinforce it, there is still about 1 chance in 10,000 that the sensors will detect that the dam is unhealthy, in which case we will need to spend the $800,000,000. So, our expected cost is roughly $80,000 for a savings (VoI) in the first year of $799,920,000. So does the sensor network cost less than that? Are you kidding? It’s $500,000. And according to Marr, doing the economics for 30 years, including monitoring and maintenance of the network, adds another $2 million. Marr calls this application of real-time monitoring to detect and respond to emerging risks “Active Risk Management.” The actual details are more complex and the statics assume an “average” dam, but you get the idea. Data from sensors can provide great value.

Marr’s presentation made me wonder about the total value of the information coming from each of the other 20 billion things on the internet. And this led to the theme of this year’s Annual Conference: Data, Decisions, and the Value of Information.  

I am happy to announce that Allen himself will be a highlighted speaker, along with other pioneers in information economics and the internet of things.


For example, Doug Hubbard, author of the popular “How to Measure Anything” series, has made a career out of VoI. He has discovered that ignorance of this subject leads to an ironic outcome, which he calls Measurement Inversion.  When he ranks the effort that firms put into measuring things next to the information value of those measurements, he finds that they go in “exactly” the wrong direction. That is, the most effort is spent collecting the least valuable information. 

Another long-time supporter of who will also be presenting is Steve Roemerman, CEO of Lone Star Analysis, a Dallas-based firm working in logistics, aerospace, and oil & gas. They have been a pioneer in IoT, with lots of practical experience. According to Steve, “In more than one of our IoT engagements, we found the customers already had all (as in 100%) of the information they needed.” The real problem was to integrate the information for making better predictions and decisions. Steve also warns that “brute force sensor deployment for its own sake is one reason we see IoT deployments fail.” This only reinforces the need to understand the concept of VOI both with the information you have already, and the information you are planning to acquire.

© Copyright 2019 Sam L Savage