As I have written in detail about Unlocking the Potential of Decentralized Data, questions still exist to understand how the challenges we face in the web2 environment affect the way we transition certain business models to web3.
While there is no one-size-fits-all answer to this question, the best way to create value with selective data will vary depending on the specific data set and the organization's goals.
However, some tips for creating value with selective data include identifying key trends and patterns, developing targeted marketing campaigns, and creating custom reports or dashboards.
Especially for a large data set, where analyzing data from different demographics would affect the kind of shared data.
I’ve been thinking about how data can be used to build value at scale.
If we take these assumptions as accurate, then it follows that to build value at scale, we need to be able to structure data in ways that maximize its value.
These assumptions from my POV suggest that it makes sense to focus on the minor data necessary to meet reasonable minimum goals because token adaption within the retail segment is minimal.
The downside of this approach is that you are not releasing the full potential data to other buyers (who may want to use it differently without tokens).
Focusing on delivering data in exchange for tokens without releasing reconstructions of comparable data to other buyers would limit opportunities for value creation.
Can platforms operating in Data as a Service (DaaS) build a data marketplace on their platform, where aggregators & buyers themselves may use data? (Assuming that the platform has a native token.)
This data marketplace would enforce predefined pricing and supply rules. This turns data into becoming liquid and tradable in real time.
For example, when it comes to data used for machine learning, releasing AI-relevant data is a good idea: Several buyers could use it in more valuable ways.
Regarding the platform's native tokens, there is another aspect to consider.
The challenge, of course, is that there is no one-size-fits-all solution for data architecture. The right approach depends on the specific needs of the business and the types of data involved.
That said, some general principles can be applied in most cases:
Simplicity is key: The goal should be to design systems that are as simple as possible while still meeting the needs of the business. This will make it easier to maintain and evolve the system over time.
Flexibility is essential: The system should be designed to easily accommodate changes in the types of data being collected and processed and changes in the way the business uses data.
Scalability is crucial: The system must be able to scale gracefully as the volume of data increases. This includes both horizontal and vertical scalability.
Performance matters: The system should be designed to handle the increased volume and complexity of data without sacrificing speed or accuracy.
Security is paramount: The system must be designed with security in mind to protect sensitive data from unauthorized access and misuse.
Data quality is essential: The system should be designed to ensure that the data being collected and processed is high quality to be used effectively by the business.
Usability is essential: The system should be designed to be easy to use so that it can be used by a wide range of users, from technical experts to non-experts.
Maintenance is a necessary evil: The system will require ongoing maintenance, so it should be designed to do this quickly and efficiently.
Documentation is essential: The system should be well-documented so that it can be maintained and evolve over time.
For example, when trading industry-relevant data, a user could decide to trade their data units from others in the network only if that other user has positive feedback from the other users with whom they interact.
This is because I'm considering user privacy into context, which will definitely structure the operational process.
With this feedback, the user — by trading with that other buyer — would have something to offer to his network companies.
Any industry-relevant data enables a new kind of network effect because today, everything is tracked.
For example, data that other buyers in data analysis could use to find profitable product strategies can increase the chance that other B2B players will buy data themselves.
The more data new users generate, the more revenue is generated by the services that sell their data to other buyers. Since the platform & its users can, in essence, control the pricing and supply of data, they can decide exactly where they want to take advantage of the data distribution network.
Therefore, data distribution for multiple purposes may be viable for an initial online data economy.
In the end, it all boils down to the following:
Identifying what data is most valuable to your target audience.
Use that data to create a unique and compelling offer that your target audience will find irresistible.
Promote your offer through channels that will reach your target audience.
This creates value from selective data sharing, which is a critical factor in the success of any data-driven business model. Platforms will have an advantage over traditional data markets because it offers a more efficient way to connect buyers and sellers. In addition, it allows for development of new applications that can be used to monetize data.
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