[PROPOSAL] Go to Market Analysis

  • Name of project: Project Plankton - Go to Market Analysis
  • Team Website (if applicable): N/A
  • Proposal Wallet Address:
  • 0xB1A549920b38b2F7C80443b49B5A75DB3A4Cd5F5
  • 0x591c9C5bf95E2264cac458D02855684a4A3bF32c
  • The proposal in one sentence: We want to understand which businesses want to purchase data from Ocean and WHY they wouldn’t go elsewhere.
  • Which category best describes your project? Pick one or more.
    • Outreach / community / spread awareness (grants don’t need to be technical in nature)
    • Improvements to OceanDAO

Project Overview

A slide deck for this proposal with all relevant information can be viewed here: https://docs.google.com/presentation/d/1Mq1d_Vlg_Oi0Hf76VPm6GZ7RlU9RMv7g0toW63VCVY4/edit?usp=sharing

  • Description of the project: This is a business focussed research proposal designed to identify the buyers who would prefer to purchase data from the Ocean network rather than anywhere else.

Currently, it’s unclear which businesses have the most to gain from consuming datasets from Ocean and we believe the ocean community is still not clear on why data buyers would choose Ocean Protocol for their data purchases over and above existing providers.

We want to identify prospective data consumers and speak with them to understand why they would (or wouldn’t) purchase data from Ocean. While we will have a lot to learn from those who do not wish to purchase data from Ocean (and the reasons why) our core focus is to identify those who are ready to purchase from Ocean today.

  • What problem is your project solving?
  1. Lack of Data Buyers in Ocean Markets - aside of a few pilot projects.
  2. Differentiation amongst emerging marketplaces, e.g. from consortia-driven approaches of big companies, such as Telekom, Amazon etc. or other startups.
  3. How to approach and reach data buyers - deeper understanding of what data buyers are actually looking for and their willingness to purchase data from Ocean.
  • What is the final product (e.g. App, URL, Medium, etc)?
  1. Go to market approach for others in the Ocean Community to learn from.

a. Outline which industries are most likely to purchase data from Ocean Markets and why they would be likely to purchase data from Ocean Markets and not go elsewhere.

  1. A list of 500 prospective data buyers.
  • How does this project drive value to the Ocean ecosystem?
  1. We want to identify 500 prospects that we can add to the top of the sales funnel to generate POTENTIAL future value for the OCEAN network.

  2. Identifying data purchasers and their data needs will have a number of benefits including attracting new datasets to the Ocean Network. If we assume a ratio of data purchasers to data sets of 1:1, then 100 data purchasers would equal 100 new datasets to ocean over time.

  3. We have seen that a reasonable TVL for a good dataset is around $30k (see references). Therefore we will assume TVL = $30k for this calculation.

  4. Previous work done by the Ocean community has resulted in a 20% conversion rate from prospects to data purchasers (see references) therefore converting 20% of the 500 prospects would result in 100 data purchasers.

  5. 100 datasets generating 30,000 TVL = $3m TVL

  6. We are going to assume this proposal contributes 5% to the overall sales process of converting 20% of the 500 prospects.

  7. If the potential TVL for this work is $3m and we are contributing 5% towards this, then $3m x 5% = $150k.

  8. BANG = $3m x 5% = $150k

  9. BUCK = $10k

  10. Chance of Success = 70%

ROI = $150,000 / $10,000 * 0.7 = 10.5


German AI/ML goes Ocean

Experiences from OCEAN market, e.g. EVOGRATAa dataset and overall Market info (as of April 23rd):

** 58k EUR TVL (Total Value Locked) exceeds estimated 30k EUR TVL value.*
** Average OCEAN TVL pre dataset = ca. 10kEUR (€4,184,207.92 TVL across 411 data set pools that contain 1,100,794.119 OCEAN, plus datatokens for each pool)*

Assumption for new case = 30k EUR TVL for other new datasets

** Published 3 new datasets in 25% of time (3 months) from 50 datasets prospected. This makes 12 datasets realistic for one year project time (=ca. 20% conversion rate)*

Assumption for new case: This equals ca. 20% of the estimated 50 datasets.

Project Deliverables - Category


  1. A list of 500 prospective data buyers.

Improvements for Ocean DAO

  1. Go to market approach for Ocean Markets including a summary of learnings

  2. Outline which industries are most likely to purchase data from Ocean Markets and why they would purchase data from Ocean Markets and not go elsewhere.

Project Deliverables - Roadmap

Desk Research (4-6 weeks, 2 people part-time)

  • Build a high level overview of the data market including list of 500 prospects and begin to understand their motivations.
  • Develop hypotheses of which industries/market players would be most likely to purchase data from Ocean Markets and Why.

Outreach (4-6 weeks, 2 people part-time)

  • Reach out to target businesses/individuals/entities to test hypothesis.

Summary (2-4 weeks, 2 people part-time)

  • Summarise findings and share with Ocean Community.

Additional Proposals

  • Upon completion of this proposal we expect to have identified a handful of target data buyers and their data needs and to have generated a number of hypotheses around why certain businesses would choose to purchase data from the Ocean Network. The next step will be to validate those assumptions and identify and acquire the datasets required to meet those needs.
  • A new Data Market may be built to satisfy this segment, we may look to scale horizontally into adjacent industries, but it is too early to tell.

Team members

For each team member, give their name, role and background such as the following.

Dr. Mark Siebert

  • Data publishing (10yrs)
  • Business Development for Data Markets
  • Owning and driving global executive engagements and partnerships, Data and Open Science
  • Web3 experience: < 1 Year
  • Positioning businesses in emerging markets or innovative fields with focus on data and AI-driven solutions.
  • https://www.linkedin.com/in/dsiebert/

Scott Milat


From my point of view the key features for enterprise data buyers are not completed yet:

  • Compute-to-data to offer data without exposing it to data theft
  • Fiat/stablecoin payments for data services on the marketplace to not be exposed to the volatility of the crypto market

Would it not make more sense to wait with the outreach until those features are done to approach potential buyers?
In the Ocean roadmap it states those will be finished ~Q2 2021 (https://oceanprotocol.com/technology/roadmap).

1 Like

Hi Robin, thanks for sharing this info and you raise a good question.

I would look at outreach in this context being more about understanding the customer’s needs i.e. speaking with customers (or potential customers), rather than looking to close a deal.

The main thing we want to know here is why the data buyer would want to purchase data from the Ocean Network rather than going elsewhere.

Thank you for the answer. And don’t get me wrong. I really think this proposal is important as Mark and you have years of business experience and will be able to ask the right questions - also to help the project and the ecosystem to connect to customers.

An offer from my side would be that if you want to do a deep dive on a project that is looking for customers then I would offer DataUnion.app, my time, and knowledge to develop a use case that you can concretely take with you to clients. This will help you to have a concrete example which you can use and you of course would also help DataUnion.app as we are looking for customers and are trying to understand how to bring them in.

Here is my Calendly - I am open for a chat about this with Mark and you at any time of your convenience.

1 Like

Having followed Ocean from the start, this proposal considers what has always been and still remains my number one concern, why & how exactly will a business, because let us be clear, this is very likely a B2B product, how will a business discover, access, consume and pay for data through the Ocean protocol.

I have my hopes, but some research in this regard would be an excellent building block. Failing that, someone more familiar with the Ocean research may point us in the right direction as to what analysis has been done.

In terms of threats, this new feature from Snowflake to me is the most obvious, and perhps something that could be included in the analysis.

As a paying consumer of public and private data, Snowflake is the biggest threat. They are able to provide the services of aggregator to all other marketplaces, as they are bringing the data right to where I need it, in my data warehouse. I don’t even need to pay for storage, only compute. Serious convenience.

1 Like

Hi Robin, thanks for the offer. You’ve been working in the Ocean ecosystem for a lot longer than both of us so I’m sure we’ll be able to help each other out a lot (assuming the proposal is funded of course!).

Hi mattard, thanks for sharing. Sounds like we are very much on the same page as this too seems like the most important question (in our minds at least) when it comes to the long-term success/viability of the Ocean network currently. Hopefully we get a chance to do a deep dive and see what we can discover.

1 Like

Hi @Scotty,

I am replying to inform you that we have updated our process for Round 5.

Please submit your Proposal via the Web Form below to complete registration.

This should take less than 5 minutes.

Thank you!

Thanks @idiom-bytes, all done.

This project has been complete. Findings from our research can be found here:

If you would like to access the list of 500 prospective data buyers please reach out to one of us via Ocean DAO’s Discord.