Some estimates of a Human Life peg the value as roughly $10 million USD.

And since in many real ways humans are data (at least for the purpose of quantifying value), it's a good bet that this figure is the total value that can be extracted from a single human oer a lifetime.

So what's the value of your data?

To estimate that, we need to ask a more specific question:

How much is an indiviual piece of data worth?

It could be next to nothing or it could be billions of dollars.

Estimating the value of data

Here’s the main factors that affect the value of data:

1. How accurate/true is the data?

Everyone is used to their own perception of what’s true and what’s false.

But in the modern world there are ways to mimic the truth, such as deep fakes. And as the technology improves, it gets more and more difficult to prove fakes.

Moreover, we’re used to dealing with imprecise data with imprecise descriptions. For example, is the sky blue seems simple, but really, it depends.

The sky may not be blue depending on the time of day, the weather, the location of the observer, and so on.

And there are many sources of data, and some sources are more accurate than others.

So context , including the source, can be very important to value.

2. What’s the context of the data?

In order to understand the value of a piece of data, associated data or metadata that provides context is important.

The difference between associated data and metadata is that associated data may have no direct connection to the primary data point, but there are still relationships that can enhance the perception of accuracy of the primary data point.

For examples, two photos taken of the same view, at roughly the same time, but from different points of view can give credence to each other.

Metadata, on the other hand, is data collected and directly related to the primary data point. For example, when you take a photo with your phone, additional information beyond the data in the photo is collected and incorporated into the image file.

This give context, and while it can be changed it tends to be very good at confirming some details about the content in the image.

This also points to one source of value for any blockchain-related or properly decentralized data - immutability. It’s not impossible to change data on a blockchain, but it can be very, very difficult.

But that’s a subject for another post entirely.

3. Is the data timely?

This one can be straightforward. For example, knowing you have a winning lottery ticket is great, unless you come by that information the day after the deadline to redeem it.

On the other hand, information doesn’t always come with a straightforward deadline, or precise context that tells you it could be important.

We’ve all heard stories about someone shopping in a thrift store, buying a “nice looking painting”, and finding out later that it is a very valuable piece of art.

That painting had been viewed by many people shopping, employees, and the person who gave it to the thrift shop, and none of them realized what they missed out on.

So timeliness can be a crucial factor in the value of data.

4. Does the data have a “shelf life”?

Some data essentially remains the same forever, other data gradually loses its value over time, and as just mentioned, some data loses almost all of its value suddenly when the “deadline” making it valuable has passed. 

For example, a person’s name tends to have a long life. A person’s address often has multiple values, but each tends to have a fairly long life.

But a person’s buying habits typically have a more limited life span, and change more frequently.

One of the values of data is to retailers interested in understanding the person’s current buying habits, in the hope that buying habits profile will give a clue as to what the user might be interested in buying next?

And out-of-date buying habits may not help much with that.

So data with a longer “shelf-life”, or more recent data, tends to have more value.

5. Who wants the data, and do they know they want it?

All of the above factors are important, but they are moot if no one wants the data.

In fact, we are all surrounded by data at all times.

And being human, we often do now even know what data we have a need for, or it we do, we may not know a source for the data, or may not know how to use it if we had it.

So for now, let’s say a person knows they want a specific piece of data, knows where to get it, and knows how to use it once they get it. And lastly, they have the means to pay for it, and will do so immediately.

6. If they want it, can they afford it?

The next question becomes, what will they pay for it.

There are some very rational ways to calculate how much this person should pay for the data.

For example, if the person needs to pay $10 for the data, but then can use the data to receive $11 within a month, that’s essentially a 10% gain in one month.

But few people would go to the trouble of buying and using the data for $1. So unless they could do this every month, and had the money to buy a substantial amount of these data points, and could buy 1000 of them per month, and receive $1000 with very little effort, and with an extremely low risk, then to them the data has little or no value.

On the other hand, if having the data fulfills some other desire of the person, they might be willing to pay quite a bit for it, even if it has no monetary value at all. Relaxation, entertainment, being in the know, prestige, and many other desires come into play for humans.

And just as common, people don’t always rationally calculate the value of data. Every desire (monetary or otherwise) can be calculated or reasonably estimated, but sometimes people are slightly or very irrational about things.

So the bottom line is, the value of data depends on:

1. Does anyone want the data?

A. Do they know what they want

B. Do they know what to do with it if they get it

C. Do they have the means to get it

D. When do they want it

2. Do people trust the accuracy of the data

A. Is the description precise

B. Is the source reliable

3. Does the data come with enough context to support the accuracy desired

A. What kind of content is available

B. How much context is there

C. How accurate/reliable is the context

4. Is the data timely

A. Is there a deadline at which the data becomes useless

B. Is that deadline known

C. Does the deadline affect the data’s value for everyone, most people, a few people

5. Does the data have a shelf life

A. How “fresh” is the data

B. How long does the value of the data persist

C. Does the value drop off steadily, or are there “bumps” in a graph of the dropoff rate