Understanding the Current Status and Future Development Direction of Blockchain Data Business with One Article

Exploring the Present and Future of Blockchain Data Business

Original Title: How to Interpret On-Chain Data with Abstract Thinking

After 14 years of development, the blockchain industry has gradually transitioned from speculation to practical applications. Blockchain data analysis can be conducted at three levels: on-chain macro, project protocols, and address analysis. On-chain macro analysis compares different blockchain indicators. Project protocols require a deep understanding of business logic. Address analysis can be used to label multiple dimensions. Several areas worth paying attention to in the future are Bitcoin Layer 2 scaling solutions, Ethereum staking data, and abstract multi-signature addresses. Overall, there is tremendous potential for the development of the blockchain data market.

Introduction

If we consider the official deployment of Bitcoin as the birth year of the industry, after 14 years of development in the blockchain industry, it has gradually evolved from pure speculation and speculative trading to a technology concept with practical application scenarios. In particular, the concept of Decentralized Finance (DeFi) has been recognized and accepted by users, bringing value back to the blockchain and making on-chain data an increasingly important focus for investors and developers.

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The front page article of The Times on January 3, 2009 titled “Chancellor on Brink of Second Bailout for Banks”

Although the data scale of blockchain is relatively limited compared to the big data volume in the current Internet, it is also relatively singular in terms of raw data. However, in the process of actual analysis and interpretation, due to the relatively free input of data and the inclusion of a large amount of bytecode that is not easy to understand, many analysts and developers often spend a lot of time deciphering and using it. Based on work experience, I believe that blockchain data can be classified from a business perspective to better understand it:

1. On-chain macro

2. Project protocols

3. Address analysis

The blockchain network can be divided into three levels from macro to micro: the network level consists of multiple protocols, and each protocol is composed of activities from multiple addresses. Currently, most blockchain data analysis products for consumers are deeply involved in a specific scenario within these three levels. Next, I will explain the business logic and application forms corresponding to each level.

On-chain macro

From the perspective of the network level, it can be further subdivided into:

• Bitcoin (UTXO model)

• Ethereum-based Ethereum Virtual Machine (EVM)

• Other public chains with non-EVM architectures (such as Solana developed in Rust language, modular public chain Cosmos ecosystem, and the Move language system inherited from Libra).

Usually, for comparison, we can examine four indicators: the number of users, the number of transactions, the transaction value, and the transaction fee. Based on these indicators, we can conduct further analysis. Here are a few simple examples:

• Evaluate the level of developer activity on the network based on the number of users and the number of transactions deploying smart contracts;

• Calculate the transactions per second (TPS) based on the time intervals between transactions to determine the network’s transaction processing performance;

• Calculate the ratio of transaction amount to the number of transactions to obtain the average transaction amount, as excessive low-value transactions actually burden the network;

• Observe the total amount of transaction fees over a period of time to evaluate the popularity of the network. Unlike the number of transactions, a low point in transaction fees represents lower urgency for user transactions.

Data source: Dune

For data users, network-level data can provide assistance when choosing from many public chains, enabling them to develop or use the most suitable public chain according to their own situation and seize the best opportunity to participate.

Project Protocols

The classification of project protocols is very broad, including DeFi, Game, Non-Fungible Token (NFT), Decentralized Identity (DID), and so on. New categories are constantly emerging, so it is not specific to discuss a certain category here, but to talk about a few points of experience in analyzing project protocol data:

• Usually, a complete protocol consists of multiple business contracts, and most of them need to be read in depth (it is important to have clear and timely updated documents) and combined with one’s own use to better understand the project.

• The business logic of products in the same field tends to be similar. For example, the core business of all DEXs is trading and liquidity. It is relatively easy to understand other projects in the entire field after understanding the leading products. Alternatively, considering the project itself, they are more familiar with their own data but always want to know more about competitors and the industry. In this case, vertical field data is valuable.

• Currently, most projects contain a lot of off-chain data, such as team and financing information, social media data, user website operation data, internal order information, etc. Some of them are public, and some are non-public, which will limit the analysis of projects. However, as the industry develops, more business data will gradually go on-chain because one of the purposes of users using blockchain is to be more open and transparent.

Data source: Dune

A typical example is during DeFi Summer, SushiSwap challenged UniSwap, and the on-chain transaction volume and number of transactions of both were once similar. However, in-depth analysis can reveal that the number of independent users on UniSwap is much higher than that of SushiSwap, which means that most of SushiSwap’s transactions and liquidity come from a small number of users. The reason for this is that Sushi Token’s issuance mechanism stimulated the inflow of funds, but subsequently, due to the unsustainable economic model, the funds flowed back to Uniswap. A similar situation is currently reflected in the data of OpenSea and Blur, with the former being dominated by retail transactions and the latter being dominated by professional user transactions. (Note! There is no value judgment on the projects here, but it is explained that user behavior differences can be reflected from the data.)

Data source: Dune

Address Analysis

From the perspective of popular EVM architecture public chains, addresses are currently divided into two types: Externally Owned Accounts (EOA) and Contact Account (CA). Regarding the existing business forms of data products for addresses, the author believes that the main ones are:

• Asset dashboard (mostly used to display asset status in wallets)

• Transaction records (mostly used to display badges and proof of rewards, such as airdrops or DID)

• Tag system (multi-dimensional tags for recommendation or risk control)

Data Source: DeBank

Here we mainly talk about the dimension of tags. Tags are very important in consumer data products. For example, for users, they may not understand the meaning of 0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045 at first glance, but they can immediately recognize it as vitalik.eth (the founder of Ethereum). Of course, this is just one of the many dimensions of tags. The author has summarized several dimensions of address tags:

• Entity tags (representing who)

• Behavior tags (what they have done)

• Status tags (current or past status)

• Predictive tags (what they may do in the future)

• Other tags (user-defined and difficult-to-classify tags)

Data Source: OKLink

Currently, most data products only display entity tags, and then display fund flows through behavior and status tags. The deep mining is not enough. For example, when initiating a transaction, displaying the counterparty’s address age, assets, and number of transaction objects can alert users to risks. Or, based on a user’s past transaction behavior, recommend similar projects. For example, an address that has participated in the minting of multiple NFTs can be recommended what NFTs are being minted the most today, saving users’ search time. Rich data support can provide more powerful algorithm services for products.

Personal opinion

Finally, the author would like to talk about three directions that I personally pay more attention to in terms of business data in the next 1-2 years:

• Bitcoin Layer 2 (including data generated by other scaling solutions)

• Ethereum Staking (Beacon Chain data)

• Account Abstraction (account abstraction and multi-signature address data based on the ERC-4337 proposal)

Bitcoin Layer 2

Regarding schemes like Ordinals that assign numbers to the smallest unit “sat” of the Bitcoin network, the Bitcoin community has different opinions, but its popularity has added imagination space and miner income (transaction fees) to the Bitcoin ecosystem. From the perspective of block space and transaction quantity, Ordinals once made transaction fees exceed block rewards, but the Bitcoin network obviously cannot accommodate more users to complete asset transactions. Even though the peer-to-peer payment story of Bitcoin has been replaced by digital gold consensus, with the halving of block rewards, the Bitcoin network’s computing power will also face huge challenges. Reduced income and intensified competition will inevitably eliminate a portion of the computing power. When block rewards can be almost ignored, transaction fees will become the main source of income for miners. If network transaction volume and fees do not grow steadily, the projection into reality is that miner income is unstable, which will affect the diversity and robustness of the network. In this case, trusted scalability in the future becomes particularly important, and the Lightning Network is currently the solution that has gained more consensus recognition from the community.

Ethereum Staking

As the underlying value storage of the entire Ethereum ecosystem, the Beacon Chain’s data can be said to carry one of the largest amounts of funds. However, due to the different structures of the consensus layer and the execution layer, existing data platforms have not presented the capital flow relationship between the two layers very well. Currently, the staking rate of Ethereum is around 20%, which is relatively low in the POS consensus mechanism. Especially since the upgrade of Shanghai’s open staking withdrawal, the net inflow of staking has been gradually increasing. Therefore, the author believes that this part of the market is expected to absorb and accumulate funds for a long time, and it has great development potential.

Data source: beaconcha.in

Account Abstraction

From the current perspective of data analysis, most project protocols only consider EOA addresses as user accounts. However, with the improvement of asset security and usability, programmable accounts have been proposed for abstraction. From a business perspective, the analysis logic generated by CA as a user account has changed. CA cannot initiate transactions in the EVM, so an EOA is needed to call CA and then call other CAs. This EOA can be a different address and may not be one of the CA’s multi-signature addresses. For these transactions, the analysis logic will change. Of course, ERC-4337 is still in the draft stage, so most developers have only heard about it in articles and conferences and have not really started using it. In the on-chain data business, this is also a relatively early vertical track.

Data source: Dune

Finally, I would like to make a not very rigorous analogy. If the data market of an industry will eventually account for 8% of the total size of the industry, then the current market value of 1 trillion (we experienced a 10-fold increase from the low point of 200 billion to 2 trillion from the beginning of 2020 to the end of 2021) of the cryptocurrency industry can accommodate about 80 billion. This still leaves a lot of room for user and capital growth in the future. The data track has currently only achieved decentralization of data storage, and there are still many stages such as data computation, data verification, and data processing that require more creativity.

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