How to break through the high wall and realize data value sharing

The theme of the first half of the roundtable was "Data Empowering Scenarios, How to Drive Industrial Innovation?" It was chaired by Zhu Feida, a tenured professor of Singapore Management University, and Tan Chang, executive director of iFLYTEK Big Data Research Institute of HKUST, and the big data product line of Puyuan Information were invited. Zang Yichao, manager, Du Yu, deputy general manager of Shanghai Wanxiang Blockchain Co., Ltd., head of Wanxiang Blockchain Laboratory, and Wang Shuang, founder of Nuowei Technology, conducted in-depth discussions.

Professor Zhu Feida said that the data economy has gone through three stages. In the first stage, data is only a by-product of commercial activities, and people use data more to understand the past. The second stage is the rise of big data and data economy, where data is monopolized by a small number of companies to benefit. The third stage is the data-driven economy, where data becomes a shared asset that drives all organizations.

From federated learning, transfer learning to blockchain privacy computing, algorithms have never slowed down. So, is data first or algorithm first?

At the practical level, Tan Chang said that algorithms and data are equally important. The acquisition of raw data is the basis for effective algorithms, and reverse data relies on high-quality algorithm processing to obtain results before it can be implemented in application scenarios.

Zang Yichao believes that data and algorithms go hand in hand. Based on the importance of data quality, we will manage the full life cycle of data applications. In the process, a data service platform is built through data governance combined with algorithms to form data productivity within the enterprise.

From the perspective of the enterprise itself, Du Yu believes that data is more important in the industrial sector, and various factories are also actively undergoing digital transformation; from the perspective of financial investment, algorithms are more important, and artificial intelligence plus human intelligence can make good investments. Investing in innovative companies with a weak data base is based on product and service capabilities that solve practical problems based on algorithms.

Wang Shuang holds the view that data and algorithms are equally important, but the importance of medical scenarios varies in different scenarios. For example, in the field of new drug research and development, the US FDA has approved the use of existing real-world evidence research data to support new drug research and development. The accuracy of the data is inseparable from the support of data, and privacy computing technology is required to open up data sources from all parties, and the two are indispensable.

At the same time, the algorithmic advantages of innovative enterprises in the future will be impacted by data sharing and open source tool codes of major manufacturers. Open source code enables developers to stand on the shoulders of giants, and start-up companies may lose their market advantages in terms of their original technical capabilities. The guests at the meeting held dialogues on the trend of open source and its impact on the data ecology and future development of technology.

The open source of core capabilities of major manufacturers is a very good means to promote industrial innovation. If there is competition, it is also a very healthy competition. Zang Yichao said that the company itself introduced a large number of open source architectures and made independent innovations based on the open source architecture, and joined open source organizations after forming standard products. Commercial companies use open source to make up for technical shortcomings, and commercial packaging industry solutions give back to the open source community to play a role in mutual promotion.

Professionals do professional things. Tan Chang said frankly that open source itself is a good thing for professionals, and practitioners should actively contribute code to the open source community. As an IT and AI enterprise, it originates from open source and is higher than open source. Processing data into professional and commercial software and putting it on the market can build a good business ecology.

Wang Shuang is very supportive of open source. He believes that open source can build trust, the internal structure is transparent to the outside world, and the outside world can fully trust the origin of data protection in the privacy protection framework, which is the process of building trust. In addition, open source can help popularize technology and reach customers faster. It involves specific application scenarios and requires targeted optimization support for commercial operations or professional teams.

Participants join together to grow the community, and open source code is most in line with the ethos of the blockchain. Du Yu actively embraces open source. He said that the blockchain has been open source since the beginning of Bitcoin, which has greatly promoted the development cycle of the industry, and the essence of building trust through technical means is derived from the foundation of open source, which is welcomed by professionals. From a business perspective, open source eliminates industry technical barriers, and companies can shift their energy to solving business problems and exploring business models.

On the whole, for the computer industry, the common concern of everyone is how to make the use of data governance more efficient; in terms of non-computing, little attention has been paid to the cost of data usage, which has also aroused more comprehensive and in-depth thinking. Where are the real obstacles to the circulation, sharing, and leasing of data between industries or companies, or between individual users and business groups?

Du Yu said that data islands are currently the biggest problem. The blockchain advocates openness and sharing. Starting from the public chain, all transaction records are open and transparent. In practice, enterprises and financial institutions will not expose real transaction records and business secrets, and personal privacy data are unwilling to be disclosed. The pain point of erecting high data walls between different government departments and government and enterprises lies in how to get through them. Blockchain plus privacy computing technology can realize effective collaboration and sharing of upstream and downstream data in the industry.

Zang Yichao took the smart park as an example. Providing data services for small and medium-sized enterprises will inevitably involve personal privacy and data security issues such as taxation. Then, how to enrich the data service ecology based on current technical concepts is the key.

Wang Shuang believes that blockchain plus privacy computing at the technical level can realize data traceability in the medical field, and actively promote the formulation of personal privacy protection laws, food safety laws and related national standards at the legal and regulatory level, so as to jointly realize the safe and orderly flow of data.

Cross-ocean peak dialogue, closely following the trend of digital and intelligent integration

The theme of the second half of the roundtable is "Release the value of data, what are the wonderful uses of blockchain", hosted by Yan Li, founder of Hashpower Think Tank, Ingo Rübe, founder of KILT Protocol, Tong Lin, CEO of Phala Network, and co-founder of Treemap blockchain Conflux Wu Ming and Chainlink China Community Director Philip jointly participated.

Ingo Rübe, who is far away in Germany, started a transoceanic dialogue. He introduced that the team launched the Polkadot ecological project KILT Protocol three years ago, and focused on the development of the blockchain-based "verifiable digital certificate" function to achieve the transfer of trust

Tong Lin introduced that the team provides private computing services for many blockchains and parachains based on Polkadot's cross-chain protocol and integrated cross-chain protocol. In short, it helps the blockchain to be mosaic, and makes data available and invisible.

Wu Ming said that the team mainly solves the bottleneck problems of existing public chain performance and consensus algorithm by designing innovative ledger structure and consensus, improves the throughput rate of Ethereum, and shortens the transaction confirmation time; through ecological compatibility, reduces or solves the problem of open congestion in Ethereum .

Philip hopes to decentralize the oracle (middleware) part, because the data source is scattered, and the data is obtained from multiple data sources and multiple nodes and uploaded to the blockchain to ensure the safety and reliability of the decentralized application, and will not be attack.

At the level of the combination of blockchain and data, the guests elaborated on changes and cognitions from their respective fields. Specifically, what kind of internal connection may be produced between blockchain and data in the future, and how the collaborative business scenario end itself functions and connects.

Wu Ming believes that the blockchain itself is a reliable distributed ledger, which can be used as a reliable data carrier to carry data with the greatest value, such as financial credit data. Therefore, the blockchain and data are inseparable, and the data carried by the blockchain can also Achieve tokenization.

Tong Lin pointed out that the types and quantities of data on the current chain are much smaller than those off-chain. The characteristic of the data on the chain is that it is available when it is visible, and it is not available if it is invisible (encrypted data), but the use of privacy and security computing can make the data on the chain available and invisible.

Philip said that the second-generation blockchain represented by Ethereum has realized Turing's complete smart contract, which makes the blockchain change from a data structure to a world computer. The team hopes to promote the application of smart contracts and prepare for external data, which can make the blockchain industry and external data more closely integrated.

Ingo Rübe believes that the biggest challenge facing the future application of blockchain is data confidentiality. Internet giants such as Google or Facebook form big data islands, and the risk of data leakage promotes the improvement of supervision. In addition, there are many data walls, which put forward higher requirements for the service capabilities of innovative enterprises.

In fact, the blockchain faces multi-dimensional challenges in the process of processing data or extracting data value. Where are the challenges and difficulties? The guests at the meeting expressed their views.

and does not constitute investment advice

The opinions contained in the article represent only the author's own