7 lessons Web3 data markets can learn from the Web2 data economy
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Market research by Dr. Mark Siebert & Scott Milat
Data markets built on Ocean Protocol are at the bleeding edge of the Web3 data economy. In fact these markets are so new that many of the potential benefits are yet to be realised.
We looked to the Web2 data economy for learnings that could be taken by those building data markets in the Web3 ecosystem. While the Web2 data economy is still in the early stages of development itself, there’s still a lot we can learn from taking a deeper look at those in the Web2 environment.
1. Data for business intelligence and data for improving sales are valuable use cases, in the eyes of Web2
We analysed 238 data use cases from datarade and found that over half of them could fit into two broad categories, business intelligence and sales.
Business Intelligence (31%) — Helping businesses make more well informed business decisions. This includes use cases such as industry benchmarks, competitor analysis, due diligence and macro economic analysis.
Sales (26%) — Helping businesses more effectively target, segment and ultimately sell goods and services to customers. This included use cases such as demographic segmentation, email marketing, location based targeting, prospecting and retargeting.
The remaining sectors were Industry specific data e.g. real estate, stock market etc (16%), Improving Data e.g. transaction data enrichment, location verification (16%), Data enabled functions e.g. natural language processing, geofencing (9%) and AI e.g. machine learning (2%).
While this isn’t a perfect measure of the market, it does reveal the types of use cases that are in demand right now and could be of value to you.
2. Solving for data standardisation will be critical
Web3 helps people work together without a ‘trusted third-party’ but that doesn’t translate to agreed upon data standards.
For distributed datasets to work together at scale, some form of data standardisation is required. A centralised entity can achieve this relatively easily through top down management. Solving this in a more complex bottom up environment like that of Web3 will present some interesting challenges.
Data Unions are one approach that is picking up traction within the Ocean Protocol ecosystem. Time will tell whether or not this approach will be capable of solving the data standardisation problem for Web3.
3. Web3 Data Markets need to focus on attracting High Quality Datasets
Web3 data markets like the ones built using Ocean Protocol need to focus on attracting high quality datasets.
While there are many potential benefits to building Web3 data markets, if these markets are unable to attract high quality datasets users will simply go elsewhere.
While this poses a threat it also provides Web3 data markets with an opportunity. Attract high quality datasets and you can begin to attract new customers.
If Web3 data markets can leverage the uniqueness of the Web3 ecosystem (i.e. tokenised incentives, data unions etc) to build high quality datasets, they may be on to a winning formula.
4. Most of the value for data markets will flow through legal entities
While the Web3 world may propose a number of benefits by being open and permissionless, most of the value for Web3 data markets will be captured by registered, legal entities.
Whether it’s for compliance reasons (e.g. GDPR) or to keep personal data anonymised and non-traceable. Data buyers put a lot of value into buying from a trusted source. This often means having a legal entity to which they can hold accountable when things inevitably go wrong.
That said, a lot of effort and energy goes into contract negotiations today ensuring the right security and compliance protocols are in place. Automating or at least making part of this process more streamlined could be a major unique benefit for Web3.
5. Corporate buyers like convenience
Data buyers are unlikely to care whether or not they are purchasing data from a centralised or decentralised source. What they do care about however is convenience.
Your customers may want to pay in crypto or they may want to pay in fiat. Depending on your target audience you may choose to go for one over the other, or you may choose to accept both. Either way, it’s worth noting that very few companies have their own crypto wallets today and the crypto learning curve itself may be enough to put them off.
Data buyers are also unlikely to spend hours trawling through multiple resources looking for what they need. The closer they can get to a one stop shop the better. This is where treating other aggregators or intermediaries with an established customer base as a target customer can be a useful strategy.
6. Data readiness — The closer to plug’n play the better
Different buyers have differing needs and oftentimes require data in different formats.
Understanding the format which speaks to your buyers and requires the least effort to use on their side will attract more buyers to you.
Whether this means your data market should cater towards a specific niche, or you offer data in multiple formats will depend on your use case. But gaining a deeper understanding of your buyer’s needs and giving them as close to a ‘plug‘n play’ experience as you can, will position you as a more attractive option.
7. Consultancies offering bespoke data services will continue thriving
The more relevant a dataset is to a buyer the more they are likely to pay for it. This doesn’t mean that more ‘generic’ datasets don’t have value, they are just less likely to fetch as high a price (all things being equal).
A lot of data buyers use consultancies today to acquire the specific data (and insights) they need.
While data markets will capture their fair share of value on their own, offering bespoke consultancy services should be considered as an additional income source for your data market, either now or into the future.
Further Research Findings
This is one of 5 articles that was published from our research. Links to the remaining articles can be found below.
- Go To Market Analysis for Data Markets & Data Brokers on Ocean Protocol
- How to identify buyers for your datasets using Ocean’s Data Markets
- 4 New Ocean DAO Projects that Build Upon our Research Findings
- Useful resources for future Ocean Data Brokers & Markets
This work was made possible thanks to the Ocean DAO and created as part of our research proposal looking into which buyers are most likely to purchase data from Ocean markets rather than going anywhere else?
