Overview of the current data trend and market
In today’s digital economy, not only in the Web2 world but also in the Web3 world, data and information are the most essential resources for businesses and society. New technological developments, such as artificial intelligence (AI) and Decentralized Physical Infrastructure Networks (DePIN), continuously increase centralized or decentralized applications that create enormous potential for companies’ added value by generating unstructured data. As the raw material for acquiring information, data is considered to be of great importance for the economic success of a company.
They represent the foundation that leverages the creation of new digital services and even new business models. Data is considered an asset that, like any other material good, has a financial value and whose management generates costs.
Data created, collected, or used on or off the chain can be sold to other organizations or individuals as raw or processed data so that it no longer serves as an enabler of products but is the product itself. This leads to the idea that data assets can be monetized by exchanging and trading data between organizations and individuals.
Against this background, numerous centralized or decentralized platforms have emerged in recent years whose primary business model comprises the trading of raw and processed data(also the visualization and insights generated from the data) and the provision of data-related services.
Data analysis, including tools that provide data analysis capabilities and platforms that directly provide analysis results, has very mature business models in the Web2 world. The biggest reason is that in the Web2 industry, data is mostly not public, and data ownership can create a very high barrier. In the Web3 world, due to the transparency and trustlessness of blockchain, everyone can easily obtain on-chain data, and data acquisition is no longer a problem. However, due to the strong professionalism of blockchain, platforms that provide interpretations of on-chain data to the public have emerged and have produced several potential unicorns.
On the other hand, data markets are becoming increasingly popular in theory and practice. Generally speaking, these platforms provide the infrastructure for data exchange by acting as intermediaries that create connections between data providers and buyers. Especially with the development of AI technology, a large amount of raw data is required for training AI models. Data, mainly processed and cleaned data, has become extremely valuable. Also, many unicorn companies have been born in data collection and exchange.
However, data has different characteristics compared to physical products, hindering the direct transfer of established processes and rules for traded goods, particularly about pricing mechanisms. In terms of transaction data, willingness to pay is low.
For example, data or data service buyers often do not recognize the potential value of it because it cannot be fully disclosed before purchase. Additionally, people often do not realize that the creation, processing, storage, and analysis of high-quality data are major cost factors for data and data service providers. Another obstacle is a lack of trust and security, leading potential data providers to worry that competitors could benefit from disclosing internal data.
The following table lists representative data projects covering different areas, from indexing and data exchange to analysis.
| Project | Value proposition | Data transformation | Architecture |
|---|---|---|---|
Indexing | Raw Data -> Formated Data | Decentralized | |
Indexing | Raw Data -> Formated Data | Decentralized | |
Analysis | Raw Data -> Analysis | Centralized | |
Analysis | Raw Data -> Analysis | Centralized | |
Marketplace | Dataset <> Dataset | Decentralized | |
Marketplace | Dataset <> Dataset | Decentralized | |
RPC and API | Raw Data -> Formated Data | Decentralized | |
Analysis | Raw Data -> Analysis | Centralized |
Many excellent projects have emerged in the data infrastructure field, which means that the opportunities for latecomers are relatively reduced.
However, at the same time, the provision of value-adding data-related services beyond the core functions of a data infrastructure appears to be a critical success factor.
Some examples are listed as follows:
| Project | Architecture | Description |
|---|---|---|
Centralized | Data labelling | |
Centralized | Dataset exchange | |
Centralized | On-chain data analysis | |
Decentralized | On-chain DID and reputation |
The decisive factor is how such synergies drive the markets and how data providers are encouraged to offer their data.
Finally, data is becoming more and more critical. Data will serve as an essential production material to provide the impetus for the development of AI technology. At the same time, as the core technology leading the next industrial revolution, AI is firmly moving toward the direction of general-purpose large-model technology. Against this background, there will be more infrastructure to build essential services for data collection, organization, confirmation, and transactions. We determined that whether in the Web2 or web3 fields, there will still be a lot of opportunities in the data track.