What data is valuable?
How to research where supply meets demand... 💰🧑🏫
Simple Truths
A lot of people miss the mark on tokenizing data that actually sells. If your goal is to make money, then you have to research target audiences, who's currently buying data, and correctly price your data to meet that demand.
To figure out which market segments are paying for data, then it may help you to go to the Ocean Market and sort by Sales.
But even then, it's not enough to just publish useful data on Ocean. You need to market your data assets to close sales.
If you're still encountering challenges in generating income, don't worry! You can enter one of the data challenges to make sweet OCEAN rewards and build your data science skills.
But what if you're a well-heeled company looking to create dApps or source data predictions? You can kickstart the value creation loop by working with Ocean Protocol to sponsor a data challenge.
What data could be useful for dApp builders?
Government Open Data: Governments serve as a rich and reliable source of data. However, this data often lacks proper documentation or poses challenges for data scientists to work with effectively. One idea is to clean and organize this data in a way that others can tap into this wealth of information with ease. For example, in one of the data challenges we leveraged public real estate data from Dubai to build use cases for understanding and predicting valuations and rents. Local, state, and federal governments around the world provide access to valuable data. So make consuming that data easier to help consumers build useful products and help your local community.
Public APIs: Data scientists can use free, public APIs to tokenize data in such a way that consumers can easily access it. This is a repository of some public APIs for a wide range of topics, from weather to gaming to finance.
On-Chain Data: There is consistent demand for good decentralized finance (DeFi) data and an emerging need for decentralized social data. Thus, data scientists can query blockchain data to build and sell valuable datasets for consumers.
Datasets for training AI and foundation models: Much of the uniqueness and value in your data consists of aggregating and cleaning data from different sources. You can scrape the web or source data from other sources to present to AI/ML engineers looking for data to train their models.
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