Web 3 Data Analyst

V&IA new Occupation

Numerous start-ups have established themselves in the blockchain world in recent years. Their goal: to develop a new web based on blockchain technology. This revolutionary type of internet is commonly referred to as Web 3.0. While there are now numerous ways to develop different types of decentralized applications, one key aspect should not be ignored: The blockchain is, at its core, a comprehensive, public database that provides permanent, immutable records of digital transactions and information.

With an increase in the number of users of the blockchain technology and the associated growth in network activity, the amount of on-chain data is also increasing. This can provide insight into the use of blockchain and give indications of future developments.

In this blog article, we highlight why the spread of blockchain technology will give rise to a new profession: The Web 3 Data Analyst.

Introduction to the analysis of blockchain data

We are living in an era in which data is increasingly seen as a valuable commodity. Large corporations such as Google and Spotify have even based their business models entirely on data. This enables many digital companies to generate personalized product recommendations, make data-based decisions and optimize their offerings based on user feedback and statistics.

The blockchain area also offers the possibility of analyzing data. The data used for this is referred to as On-Chain Data. In contrast to user data from platforms such as Google or Amazon, the on-chain data of the blockchain is pseudonymized. This means that (at least currently) no direct conclusions can be drawn about individual user behaviour, unlike with centralized services.

Nevertheless, the blockchain offers valuable data sets that can also be used for business purposes:

Transaction data: Transaction data recorded on the blockchain shows when and through which wallet a transaction took place. This data can be used to analyze payment and financial streams.

Public keys: The blockchain stores a user’s public key. By monitoring certain wallets, conclusions can be drawn about market activities. For example, the public keys of exchanges can be used to track the activity of money inflows and outflows.

Smart contract data: Blockchains that support the creation of smart contracts also provide access to data on these contracts. This information can be used to analyze Web 3 products in terms of their frequency of use and derive user behavior.

Blockchain Data Structures

When analyzing blockchain data, the first thing to understand is that different blockchain systems have different data structures. This is due to the respective design and consensus-building mechanism of the network. Furthermore, the data structures of blockchains are fundamentally different from traditional data sets as they consist of transaction blocks that are chained together. Special techniques and dedicated tools are required to analyze such non-tabular and distributed data sets.

Not only do Bitcoin and Ethereum, the two largest and best-known blockchains, vary in terms of data processing, but also the data structures of other blockchains differ.

Ripple operates as both a digital payment protocol and a cryptocurrency known as XRP. Ripple uses a data structure called XRP Ledger, which is based on the Ripple Protocol Consensus Algorithm (RPCA). Ripple transactions are anonymous and are summarized in so-called Ledger Versions and then arranged in a chain. Each of these ledgers consists of a header and a transaction list. The header provides information about the hash of the previous ledger and the associated metadata.

Zcash is a cryptocurrency with a special focus on privacy using the Zerocash protocol. The Zcash network offers users the opportunity to execute protected transactions that are verified in the network but are kept anonymous. This means that the identities of Zcash users remain hidden. The Zerocash protocol uses zero-knowledge techniques and in particular zk-SNARKs (Zero-knowledge succinct non-interactive arguments of knowledge) to guarantee the confidentiality of the transaction.

Iota is a cryptocurrency designed specifically for the Internet of Things (IoT). Instead of a conventional blockchain, Iota relies on a Directed Acyclic Graph structure, also known as DAG, which is known as the Tangle. Within the Tangle, each transaction refers to two previous transactions and confirms them. In this way, it creates a network of interlinked transactions.

Ethereum On-Chain Analysis - Evaluation of Web 3 Projects

The two largest blockchain systems, Bitcoin and Ethereum, are the most suitable for on-chain analysis. This is mainly due to their large number of users and the fact that various API providers provide data for these blockchains.

Ethereum offers the most comprehensive analytics options. In addition to public keys and transaction data, Ethereum-based smart contracts can also be investigated. This expands the analysis spectrum by examining smart contract functionalities such as DApps and DAOs, as well as account interactions and token transfers. Ethereum is therefore particularly suitable for gaining insights into trends and developments on the Web 3. Data can either be collected by synchronizing with a local Ethereum node or obtained via data providers such as Etherscan.io.

On-Chain Metrics for analyzing Web 3 Projects

Depending on the specific question, various metrics can be used to analyze Web 3 projects on Ethereum. The metrics can be viewed from different perspectives based on the desired level of detail:

The financial statements section includes all data that offers the possibility of making statements about the profitability of a Web 3 project. The metrics that can be used for this are Fees, Revenue, Token Price and Market Capitalization, as well as the amount of Treasury (DAOs). In particular, the Fees and the Revenue make it possible to gain insights into the usage and profit creation of a protocol and thus draw conclusions about its profitability.

The Daily Active Users indicator provides information on how frequently the respective protocol is being used. To determine the number of daily active users, factors such as the number of token holders and the trading volume of the protocol is taken into account. The metric can be counted as part of the wallet analysis and also offers the opportunity to track the development of the project over time.

The Total Value Locked (TVL) indicates how much capital is tied up within a smart contract or project. This metric can be used as an indicator of user confidence in the project.

It should be noted that on-chain data alone is not sufficient to fully evaluate a Web 3 project. In principle, a combination of on-chain data and a fundamental analysis should be used in order to gain a comprehensive understanding of the project and its general development.

Skills of the Web 3 Data Analyst

There are therefore certainly opportunities to carry out well-founded blockchain data analysis. The main challenge here is collecting, providing and processing the necessary data sets. At the same time, the analysis of blockchain data involves the risk of misjudgements due to inadequate data quality.

Nevertheless, it is foreseeable that the breadth of the field and the specialized knowledge will lead to the establishment of a new profession in the future. The Web 3 Data Analyst. But what skills are required for this role?

Blockchain Fundamentales:

To be able to analyze on-chain data, future blockchain data analysts need a rudimentary understanding of blockchain technology.

Web 3 Business Understanding:

As with conventional data analysis activities, it is not enough to simply deal with the data. Understanding the underlying business Web 3 problems and the resulting metrics is the foundation for performing valuable analyses.

Tool Selection:

Although many solutions already exist on the market, you have to choose the right tools depending on the task and the selected metrics in order to obtain meaningful results. If you want to rely on low-code tools, you have to live with the fact that the data is provided in a proprietary form. If you want to define your own metrics, you often have to rely on complex API providers to obtain the data and prepare it for your purposes. Both methods offer advantages and disadvantages.

Data Cleansing:

If you do not opt for a third-party tool, the most time-consuming part of carrying out analyses is data cleansing. In order to carry out valuable analyses, the necessary data must first be prepared, structured and accumulated. A general rule here is that the analyses can only be as good as the quality of the data.

Data Storytelling:

As data analysis is used to make data-based decisions, web 3 data analysts are required to demonstrate a high level of communication skills. In addition to the basic ability to present the processed data visually, they also need to be able to convey its meaning in order to attract the attention of decision-makers.

The rise of Web 3 Analytics

With the emergence of various blockchain systems and their increasing usage, more and more opportunities are opening up for analyses in the web 3 sector. Due to the large number of providers and services, Ethereum offers platform for examining on-chain data and drawing conclusions about the current status and user behavior. One of the main challenges is the adequate processing of this blockchain data and the determination of suitable metrics to answer specific questions. However, it is already foreseeable that a new specialist field of the Web 3 Data Analyst will emerge in the future. Such an analyst should not only have an in-depth understanding of blockchain, but also have advanced skills in processing and analyzing complex data.

In general, the Web 3 Data Analyst could be seen as an in-depth specialization that involves an understanding of on-chain data as well as a broad interest in business related questions.