mirror of
https://github.com/oceanprotocol/docs.git
synced 2024-11-15 01:34:51 +01:00
88 lines
4.1 KiB
Markdown
88 lines
4.1 KiB
Markdown
---
|
||
description: Thrive in the open data economy by closing the loop towards speed and value
|
||
---
|
||
|
||
# The Data Value-Creation Loop
|
||
|
||
<figure><img src="../.gitbook/assets/data-scientists/data-value-creation-loop.png" alt=""></figure>
|
||
|
||
### Motivation
|
||
|
||
The core infrastructure is in place for an open data economy. Dozens of teams are building on it. But it’s not 100% obvious for teams how to make $.
|
||
|
||
We ask:
|
||
|
||
> How do people sustain and thrive in the emerging open data economy?
|
||
|
||
**Our answer is simple: ensure that they can make money!**
|
||
|
||
However, this isn’t enough. We need to dive deeper.
|
||
|
||
### The Data Value-Creation Loop
|
||
|
||
The next question is:
|
||
|
||
> How do people make money in the open data economy?
|
||
|
||
**Our answer is: create value from data, make money from that value, and loop back and reinvest this value creation into further growth.**
|
||
|
||
**We call this the Data Value-Creation Loop.** The figure above illustrates.
|
||
|
||
Let’s go through the steps of the loop.
|
||
|
||
- At the top, the user gets data by buying it or spending $ to create it.
|
||
- Then, they build an AI model from the data.
|
||
- Then they make predictions. E.g. “ETH will rise in next 5 minutes”
|
||
- Then, they choose actions. E.g. “buy ETH”.
|
||
- In executing these actions, they data scientist (or org) will make $ on average.
|
||
- The $ earned is put back into buying more data, and other activities. And the loop repeats.
|
||
|
||
In this loop, dapp builders can help their users make money; data scientists can earn directly; and crypto enthusiasts can catalyze the first two if incentivized properly (e.g. to curate valuable data).
|
||
|
||
### The Data Value Supply Chain
|
||
|
||
**If we unroll the loop, we get a data value supply chain.** In most supply chains, the most value creation is at the last step, right before the action is taken. Would you rather a farmer in Costa Rica selling a sack of coffee beans for $5, or Starbucks selling 5 beans’ worth of coffee for $5?
|
||
|
||
Therefore, **for data value supply chains, the most value creation in the prediction step.**
|
||
|
||
To the question “How do people make money in the open data economy?”, the “create value from data!” almost seem like a truism. Don’t fool yourself. It’s highly useful in practice: **focus only on activities that fully go through the data value-creation loop.**
|
||
|
||
However, this is still too open-ended. We need to dive deeper.
|
||
|
||
### Which Vertical? How To Compare Opportunities
|
||
|
||
There are perhaps dozens of verticals or hundreds of possible opportunities of creating and closing data value-creation loops. How to select which? We’ve found that two measuring sticks help the most.
|
||
|
||
**Key criteria:**
|
||
|
||
1. **How quickly one can go through the data value-creation loop?**
|
||
2. **What’s the $ size of the opportunity**
|
||
|
||
For (2), it’s not just “what’s the size of the market”, it’s also “can the product make an impact in the market and capture enough value to be meaningful”.
|
||
|
||
We analyzed dozens of possible verticals with according to these criteria. For any given data application, the loop should be fast with serious $ opportunity.
|
||
|
||
Here are some examples.
|
||
|
||
- **Small $, slow**. Traditional music is small $ and slow, because incumbents like Universal dominate by controlling the back catalogue.
|
||
- **Large $, slow**. Medicine is large $ but slow, due to the approval process.
|
||
Small $, fast. Decentralized music is fast but small $ (for now! Fingers crossed).
|
||
|
||
**We want: large $, fast.** Here are the standouts.
|
||
|
||
- **Decentralized Finance (DeFi)** is a great fit. One can loop at the speed of blocks (or faster), and trade volumes have serious $.
|
||
- **LLMs and modern AI** is close: one can loop quickly, and with the right application make $. The challenge is: what’s the right application?
|
||
|
||
### Project Criteria
|
||
|
||
We encourage you - as a builder - to choose projects that close the data value-creation loops. Especially loops with maximum $ and speed.
|
||
|
||
We follow our advice for internal projects too. Predictoor, Data Farming, and DeFi-oriented data challenges are standout examples.
|
||
|
||
### Summary
|
||
To sustain and thrive in the open data economy: make money!
|
||
|
||
Do this by closing the data value-creation loop, in a vertical / opportunity where you can loop quickly and the $ opportunity is large.
|
||
|
||
|