I have been writing about big data for over three years now. In all that I wrote and many articles that I read, there is an underlying assumption that people naturally accept the huge economic value associated with big data. It turns out that this is a bad assumption. They don’t!
There are many people that see big data as worthless and they are totally correct. Data is actually worthless until it is transformed into information. In this example, McKinsey states that only 1% of the data collected from 30,000 active sensors is actually used, the rest is wasted. I believe that the same applies to many other deployments. Industry is generating terabytes of data and we have only just started to process that data to extract meaningful information. Big data is truly worthless, the economic value is actually in the extracted structured information. People talk about big data but they actually mean structured information.
Linking this subject to my continuous effort to push for a horizontal #IoT, I can add that for big data to yield meaningful information it has to come from a diversity of sensors. If I collect a million data points from a temperature sensor on a motor then the information that I extract will be linear and of little value. Now, if I was to collect data from ten different sensors on that same motor then the value of the information I extract will be multiplied by many factors of ten.
This is the power of horizontal #IoT. The mash-up of diverse unstructured data streams to generate valuable structured information. This technique has been proven over the past few years by application to crowd analysis. Various diverse streams of data are inter-processed to extract, for example, “sentiment”, an item in fashion these days. There is really no difference in the mash-up operation, be it user data or motor data. The same concept applies to both. Simply take in a huge volume of diverse, unstructured, real time data and extract structured information. Same concept, different algorithm.
Most people really want structured information, not big data, since it can be used to make decisions. This is where the real economic value lies. The most famous example is the airline company willing to pay GE a few million dollars in order to know in advance when an engine is about to fail. If it wasn’t for the expert data scientists at GE that know how to extract meaningful information, then the terabyte of data generated by the engine during flight would have remained worthless. Based on that structured information, a decision is made about the impeding failure of the engine. The airline is willing to pay serious amounts of money to avoid the liabilities associated with a plane falling from the sky due to engine failure. Structured information used to make a decision of much higher economic value, probably by multiple orders of magnitude.
Any #IoT or big data discussion, since they are so intertwined, would be more fruitful if both parties can agree to the value of what they are talking about. To facilitate that value estimate I am in the process of creating a formula to indicate the dollar value of information relative to that of big data. Something like :
Market $ value of information = Ʊm x ∑ ( ∑ Data(i)(k) )
Data(i) is the $ value of each sensor sample. i is the number of data points of one specific type of sensor while k represents the number of diverse data streams. Ʊm would be the fudge factor that represents the multiplier value of the extracted information for a specific market. This formula, when complete, would help two parties agree on the value of the data streams and of the information extracted. Most of all it would hopefully push people to deploy diverse sensors to do mash-ups in order to charge more for the extracted structured information. It is an excellent solution for both parties and for #IoT. People interested in deploying #IoT nodes will see where the revenue stream would come from and the information traders would see how they will create their revenue. Everyone wins.
If we are able to quantify the value then people would invest and deploy #IoT nodes and the resulting information would be traded at it’s fair value. Once that formula is complete then we would move on to the next level where we would need to quantify the value of the decisions made based on that structured information. This is a much harder problem.
A business professional that I work with and that I highly respect always drives to the point by saying: “show me the money”. I hope with this formula I can answer his question regarding big data for #IoT.
All feedback from experts who may have already figured this out is highly appreciated.
Also read: What’s Really Going to Limit the IoT?