Data in the digital age has been heralded as digital oil.

With more data being produced daily than has been collectively produced over the last 20 years (compounded by the technological advancements in computation and global connectivity) data has become an intrinsically valuable, easily accessible commodity. Being so valuable, accessible, & abundant, Data has become the center of attention from the Big Brothers (Big Tech, Big Data, Big Government)

Data, by its nature, is very broad; covering everything from the seemingly irrelevant to the extremely sensitive. Weather is data, product stock supply is data, your vitals (heart rate, blood pressure) are data. Of all data the most valuable is PII (Personally Identifiable Information); information that exclusively belongs to individuals.

One of the leading human laws is the right to privacy and with the hyperconnectivity of digital communication, it is a law that has been violated time and time again.

Data Breaches worldwide have caused an uproar, billions of people have had their rights to privacy of information violated & the violators have been held minimally (if at all) accountable.

While individuals/owners of data are trying to protect their privacy, Big tech companies are vying to capture as much data as possible at whatever the cost to the data owners. In response to the data race, Governments have rolled out regulations such as the GDPR in Europe & SHIELD + CCPA in the United States to protect individuals from the misuse & abuse of their private data (also known as PII [personally identifiable information]).

But, the data race is only beginning.

New models have been adapted to the collection, storage & digestion of data. These are models built using metadata.

Metadata is data about data. Data is the direct information that is subject to scrutiny from regulatory & metadata is abstract information about the original data (which is not subject to regulation).

Every time a person takes two points of information and deduces an outcome based on those two points, that outcome classifies as meta-data.

Crypto Metadata

One of cryptocurrencies' innate wonder-properties is that it is open. The Distributed ledger technology upon which crypto assets are built (blockchain) is transparent, in the sense that it allows all activity conducted on-chain to be audited at an extremely granular level. From open & close price to volume, to on-chain account balances, to emission schedules and beyond; due to its global financial presence, cryptocurrency is rich with data.

The data found in crypto can be broken down into three general categories; economic, technical & social. The three categories have aspects that overlap, however, their applications to their appropriate domains allows the same data point to be used for different forms of deduction.

The table above is by no means complete or exhaustive; there are so many points of information to use that listing them all here would distract us from the focal point & purpose of this conversation; when modeling the metadata created from cryptocurrency, deep existential behavioral laws of human interaction can be observed → in turn allowing individuals to profit & society to progress at an exponential rate.

In order to fully understand & appreciate the malleability of open data & its implications, we can simulate the digestion of crypto data. Here we random sample 5 data points, inspecting each one individually & carve a thesis based on the aggregate of each outcome.

Simulation parameters:
Crypto Asset: XYZData point 1: Price Change (course of one Year)
Data point 2: Total Supply / Circulating Supply / Emission
Data point 3: On-Chain Balances / Duration of Holding (course of one year)1) Price Change (over the course of one Year)Coin XYZ started the year at $100 {1/1/2030}
Ended the Year at $400 {12/31/2030}
High — $530 {3/16/2030}
Low — $63 {8/8/2030}Largest Daily Gain — 33%
Largest Weekly Gain — 172%
Longest Consecutive Gain Streak — 14 daysLargest Daily Loss — 16%
Larget Weekly Loss — 65%
Longest Consecutive Loss Streak — 19 daysAccording to Price Action we draw the following possible conclusion:
Price Depreciations cycles of this asset are longer than price appreciation cycles.
While the Largest Daily loss is greater than the largest daily gain, signals huge short term volatility. 2) Total Supply / Circulating Supply / Emission ScheduleCoin XYZ has a total supply of 100,000,000
At the start of the year 50,000,000 units were circulating {1/1/2030}
At the end of the Year 60,000,000 units were circulating {12/31/2030}Daily Emission— ~27,400 xyz tokens
Weekly Emission — ~192,300 xyz tokens
Monthly Emission — ~833,333 xyz tokens
Yearly Emission — 10,000,000 xyz tokens3) On-Chain Account Balances (over the course of one year)At the start of the year {1/1/2030} there were
- 50,000 active addresses
- 1,000 addresses with a balance over 0.025% of total supply 25,000  xyz tokens
The average holding time for tokens at an address was 7 daysAt the end of the Year {12/31/2030} there were
- 75,000 active addresses
- 1,250 addresses with a balance over 0.025% of total supply 25,000 xyz tokens
Average holding time for tokens at an address was 14 days

Any single data point on its own does not present a robust enough set of information for the development of a thesis on macro human behavior; however, when we synthesize data across data points the magic starts to happen.

Starting point:
50,000,000 supply @ $100 @ 50,000 addresses 
$5,000,000,000mcap - average address balance 100,000End Point:
60,000,000 supply @ $300 @ 75,000 addresses
$18,000,000,000mcap - average address balance 240,000Over the course of 365 days:
 — Unit Price changed +300% (+$300) 
 — Mcap change +260% (+$13,000,000,000)
— Supply increase +20% (+10,000,000)
 — New Addresses +50% (25,000)

This means that $13,000,000,000 of the value entered with inflation in the supply of 10,000,000 tokens. 25,000 new addresses/people felt confident enough in a project to deposit serious money.

The next year can guarantee a supply increase of 10,000,000 again (16.67%); If in the next year 25,000 new addresses are created & $13,000,000,000 of value is flooded into the project, the MCAP will become $31b an increase of 72.2% & the unit price will rise to $442 (+47.33%).

Profit Thesis:
In the event of radical divergence in any data point (such as a spike in new addresses or collapse of price) in this model signals positivity; likewise, in the event of extreme price movement upwards in a short span of time (+100% in price 3 months into the year) a minimization of exposure will preserve capital.

Existential Thesis:
$13,000,000,000 of effort has been committed to this project regardless of the extreme inflation targets. There are more people involved, people are now paying more for this asset, holding this asset longer. It seems as though wealthier individuals find the risk::reward of this project worthwhile. Maybe adjacent systems will collapse.

Unlike the case with their closed counterparts, open crypto data has privacy/pseudonymity baked into the data structure & does not suffer the violations constantly found in central systems. The access to information is equal to an individual as it is to a government.

Cryptocurrency is an absolute paradigm shift in Big Data.

There are new rules, new players, & new ideas.

There are no single ruling enterprises leaking information.
There are no governments to sanction activities.

Let's go Build our future, on our terms, with our data.