docs/data-science/data-challenges/hosting-a-data-challenge.md

Hosting a data challenge

Hosting a data challenge can be an exciting way for data publishers to seed use cases and bring attention to their data assets. Hosting a challenge can also be a good way to tap a community of data scientists to build products on top of your data to gain insights and useful models for your business without needing to bring an in-house data science team. To host a data challenge, the steps can be found below.

1. Establish the business problem you want to solve. The first step in building a data solution is understanding what you want to solve. For example, you may want to be able to predict the drought risk in an area to help price parametric insurance, or predict the price of ETH to optimize Uniswap LPing.
2. Curate the dataset for the challenge. The key to hosting a good data challenge is to provide an exciting and through dataset that participants can use to build their solutions. Do your research to understand what data is available, whether it be free from an API, available for download, require any transformations, etc. For the first challenge, it is alright if the created dataset is a static file. However, it is best to ensure there is a path to making the data available from a dynamic endpoint so that entires can eventually be applied to real-world use cases
3. Decide how the judging process will occur. This includes how long to make review period, how to score submissions, and how to decide any prizes will be divided among participants
4. Work with OPF to gather participants for your data challenge. Creating blog posts and hosting twitter spaces is a good way to spread the word about your data challenge