Case Study: Is there still a market for dynamic-priced datasets on Ocean Protocol?

Data Whale
4 min readApr 10, 2023
Futuristic Representation of a Dynamic Data Market (by Midjourney)

Ocean Protocol enables users to publish, share, and consume data through its blockchain data marketplace. Previously, Ocean Protocol’s dynamic pricing system, which enabled data providers to determine prices for their data assets based on market demand and supply, was one of its primary characteristics. This case study will examine the advantages and drawbacks of dynamic pricing and compare it with Ocean’s current infrastructure of fixed pricing.

With dynamic pricing, a dataset’s price is constantly changed in response to supply and demand in the Ocean Marketplace and according to how much liquidity is in the pool that enables price fluctuations through an AMM (Automated Market Maker). Dynamic pricing ,in the context of Ocean Protocol, enables data providers to benefit from changing prices of their data assets in accordance with market supply and demand, which can result in more effective pricing, better resource allocation and most importantly: value exploration (because, what is data really worth!?)

The advantages of dynamic pricing

  1. Increases Data Publisher Revenue: Changing prices in response to shifts in market supply and demand enables data providers to increase their revenue. As a result, data providers charge higher for their data during periods of high demand and could also lower their rates during periods of low demand. This guarantees that data suppliers can generate the most money from their data assets.
  2. Promotes Data Sharing: By making it more profitable for data providers to share their data assets, dynamic pricing promotes data sharing. This is so that data providers benefit from changing rates in response to supply and demand in the market, increasing their revenue by making their data assets available to more customers.
  3. Improves Market Efficiency: By ensuring that data assets are valued according to their genuine worth (decided by market participants), dynamic pricing improves market effectiveness. As a result, high demand data assets will command greater prices, while low demand data assets will change appropriately.

The advantages of fixed pricing

  1. Predictability: Because buyers and sellers are aware of the dataset’s specific price up front, fixed-priced datasets offer predictability. Making knowledgeable decisions about the purchase or sale of data can be aided by this. Also, fixed price assets are somewhat protected from secondary fluctuations of token prices, as here participants need to watch two pricing (one for the dataset and one of the token vs. US$).
  2. Liquidity: As buyers and sellers may readily deal at a given price, fixed-priced datasets can aid in generating more competitiveness in the market. This may contribute to a rise in market activity and transaction volume. It also protects the data assets from unwanted speculation, making them unprofitable for real consumers.
  3. Simplicity: Fixed-priced datasets don’t require complicated liquidity pool infrastructure and AMMs, making them easy to comprehend and utilize. This can assist in lowering the entrance barriers for new consumers and sellers and increase market accessibility for a larger audience, as well as market participants. They also protects user assets against malicious actors that take advantage of the AMMs weaknesses.

Generally and although more risky, dynamic pricing is somewhat superior to fixed price in a number of ways. By balancing supply and demand, dynamic pricing can result in a more effective distribution of resources, which may result in better use of data assets may result from this. Also, by taking market conditions and trends into consideration, dynamic pricing can result in more precise pricing. This could result in data providers and customers making more informed decisions.

Dynamic pricing, however, also has significant drawbacks. First, because automated market makers are needed, it is more difficult to create and operate than fixed pricing (AMMs). In the area of decentralized finance (DeFi), concepts like liquidity pools and automated market makers (AMMs) are still risky and are regarded as experimental, since they are open to several attack vectors.

Impermanent loss is one of the key risks associated with liquidity pools. As the value of the assets in the pool fluctuates in relation to one another, impermanent loss happens and user funds may be impacted. When one token in the pool has a significant price gain or reduction, the pool can become unbalanced and IL occurs. Liquidity suppliers therefore lose some of their investment.

Another risks associated with AMMs is slippage, which happens when an asset’s price changes while a trade is being conducted. Due to this, traders may not receive the full amount of the item they are purchasing or selling.

Last but not least, abrupt changes in market conditions can create major price swings and it is challenging to foresee how supply and demand will change over time, impacting data publishers, consumers and market participants altogether.

These implications are especially relevant to assets with low liquidity that were available on earlier iterations of the Ocean Marketplace.

Ocean Protocol solely deployed fixed pricing on their latest version of their data marketplace as a result, which offers participants more consistency and a much safer environment to experiment with their OCEAN Tokens. For rewards, Ocean Protocol established a community-curated “Data Farming” initiative, in which OCEAN Tokens are distributed to those datasets with the highest inherent value, in order to curate fixed-priced assets. Participants can receive incentives in exchange.

In conclusion, we believe that the natural price discovery of dynamic pricing still plays a major role in the creation of a new data economy, despite the risks that current solutions like AMMs entail. Considering the benefits of dynamic pricing, we also believe that there is a market willing to take risks of IL or slippage, in order to experiment with natural data price discovery, benefits of supply and demand, as well as increased incentive to participate in the Web 3 Data Economy!

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Data Whale

A community-powered project that is passionate about Artificial Intelligence, Content Creation, Outreach and Ocean Protocol.