The economics of data — Part III — Supply

We covered the premise of data being a valuable good in Part I, then we discussed a bit more on the demand side in Part II. This post will focus on the supply side, and will hopefully present some “out there” ideas.

Let’s first start by talking a bit more about data and information. Most people think that data is a by-product of an activity or a phenomenon. The phenomenons can be naturally occurring, for example weather systems, circadian clocks and so on. Activities are more deliberate: walking to the bus stop, making a phone call, buying something online or in a store. Data will flow around and have ripple effects. Some data will flow faster than others, for example the traffic information in NYC would be a fast flowing stream(or not, depending on your perception of what fast is, which is indeed a very important point).

Data is generated through agents, or actors, and although most of the time we think about “autonomous agent” like people and organizations, data leaves a trail, a footprint in many other “passive” objects. Things can get complicated very quickly. There were some experiments done on trying to extract signals from plants for example.

With the progress in technology, we have been able to capture more of that data.All kinds of sensors have been invented to measure things from humidity, to seat pressure, to social interactions. How much data we can capture,transmit and store is limited only by what channels and mediums we discover(and the laws of physics and all that).

In an essence, our world, and the universe itself, is a data producing machine. And as far as we know, humans do most of the information processing. It is overwhelming, and it is only going to get worse(or better, if we prepare for it!)

What does this mean? it means every minute, every second, of every day, people and things are producing data. Whether you are telling your family you just booked a flight and coming to visit, to going on a hunt with your Pokemon Go app. A lot of data, and this data is sitting and flowing nudging and screaming for someone or something to extract the information out of it.

Brandenburger Gate — Wikipedia CC-BY 4.0

Does anyone care about the pictures tourists are taking of the brandenburger gate? What is the _use_case_? Let me tell you, the value of the data is relative. The one picture of The Brandenburger gate might not seem like much, but imagine a few of these pictures, from different angles. Imagine this happening at different times, on different days of the year. We can extract information to build a panorama, we could measure the level of attraction, we can learn about trends in fashion, and what kinda crowds are attracted to this site.

Now think about all the beautiful things you could do with Pokemon Go, in addition to all the enterainment.

This type of data however is fairly abundant, or low cost to acquire. People often give that data away for free

There are other kinds of data which is far harder to acquire. The difficulty could arise because of the nature of the source, for example requiring special kinds of sensors, or because that data is proprietary in some ways. Personal information for example are subject to certain privacy regulations. Even more so, data pretaining to health , or financial standing is subject to even more scrutinized processes.

Things become even more interesting when the data involves a transaction. For example when someone visits a store, or a hotel. The establishment has a record of the transaction and many businesses or government agencies will use this data to forecast or optimize different processes. The thing is , this has been around for a long time and it has proved limited. So now you can see how stores are trying to collect more data by embedding or installing more sensors to capture the data using beacons for example.

What seems to be really missing is transparency. We know many companies are trying to acquire more data. Sometimes they go directly to the sources and lure them with free services or apps, sometimes they go to companies sitting on goldmines of data either offering complimentary services , or just simply acquiring them and their data assets.

One thing we should remember is that digital data is not exclusive. You could sell the same data over and over. So if someone wants the data exclusively, they better price that benefit.

Now if we go back to our amateur photographers example, would they take better pictures if they were getting paid for them?maybe more pictures from different angles?maybe there is a gap and the first one to supply a picture from a specific angle gets paid more?

Another good example I think is one about jobs: when you make your linkedin profile public, or upload your resume on a recruiting site. You are making some of your data available for free. The reasoning is that you perceive the exposure and the opportunity to be worth some value.You are probably thinking about how to attract new employment opportunities, or you actively searching for one. At the same time, you will be helping someone create a potentially great business.So, is this a fair exchange of value?

Is money the right exchange of value?What if you were to supply your health data, in exchange for getting a personalized diet and exercise program just for you. Would that be of enough value? if not, what is the right value? What if this cool service could alert you ahead of time when you are at risk of a heart attack? Is the “free” service enough for you?

We can ask the question differently: Would you rather have the services of Facebook, or would you rather have a $100 a year?If twitter were about to go bankrupt, and asked me to pay $5/year, I would probably pay it.If they are generating less money off showing me ads, that is even better, Twitter can survive for less than $5/year :)

It is true, a lot of people aren’t paying for services, or software anymore. They have implicitly agreed to leasing their eyeballs, and soon their earlobes. It is gonna be hard to build ad-blockers for audio, so we might as well ask the question:

How much do you want for your data?

We will cover pricing in the next post.

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