What’s on-chain data ?

On-chain data refers to data that is recorded on a blockchain. Because a blockchain is a distributed database, on-chain data is publicly available and can be accessed by anyone.

Web3 and web2 are different versions of the World Wide Web, with web3 being the most recent and advanced version. Some key differences between the two include the following:

1. Web3 is decentralized, while web2 is centralized. This means that in web3, data and services are provided by a distributed network of nodes, rather than by a single entity. This makes web3 more resilient and less vulnerable to censorship or failure, but also more complex and harder to control.

2. Web3 is built on blockchain technology, while web2 is built on traditional client-server architecture. This means that in web3, data is stored and transferred using cryptographic algorithms, rather than being stored and transferred by a central server. This makes web3 more secure and transparent, but also slower and more expensive.

3. Web3 is focused on enabling new types of applications and services, while web2 is focused on improving existing applications and services. This means that web3 is more experimental and forward-looking, while web2 is more mature and established.

These differences have implications for how data is analyzed in each environment. In web3, data analysis is more focused on understanding the behavior of decentralized networks and the underlying blockchain technology. This often involves using advanced techniques such as machine learning and network analysis to identify patterns and trends in the data. In web2, data analysis is more focused on understanding the behavior of users and the applications that they use. This often involves using traditional techniques such as statistical analysis and data visualization to understand user behavior and identify trends and insights.

To do on-chain data analysis, you will need to collect and organize the relevant data, and then use tools and techniques such as data visualization and statistical analysis to identify patterns and trends. This can help you to better understand the behavior of the blockchain network and its users, as well as to make predictions about the future direction of the market. In some cases, you may also want to use machine learning techniques to automate the analysis process and identify more complex patterns in the data.

Categories of on-chain data

There are two categories of on-chain data :

1. Raw data

1. Abstracted

We single out such categories because, in fact, all the calculated metrics are just abstractions over raw data. On-chain raw data refers to the unprocessed data that is recorded on the blockchain. This data includes information about individual transactions, such as the sender and receiver of the transaction and the amount of cryptocurrency that was transferred. Economic data, on the other hand, is derived from the raw data and includes information about the supply and demand for a particular cryptocurrency, as well as its market capitalization and trading volume.

Economic data is not just an abstraction over the raw data, but rather is calculated using a variety of techniques and metrics. For example, market capitalization is calculated by multiplying the total supply of a cryptocurrency by its current price, and trading volume is calculated by summing the total number of transactions over a given period of time. Other metrics, such as the velocity of money and the network value to transaction ratio, may be calculated using more complex formulas that take into account various factors such as the number of transactions and the overall network activity.

Overall, economic data provides a higher-level view of the cryptocurrency market and can be useful for understanding market trends and making investment decisions. However, it is important to note that economic data is not always an accurate or complete representation of the underlying market, and should be used with caution.

Different analytical solutions

Centralisation vs decentralization

There are several different solutions for indexing on-chain data, including both centralized and decentralized options. Centralized solutions typically involve a single entity that collects and organizes the data, while decentralized solutions use a distributed network of nodes to index the data. Some examples of indexing solutions include block explorers, which allow users to search and browse the blockchain, and indexing services, which provide APIs and other tools for developers to access and analyze on-chain data.

It is possible to make a decentralised analytical solution using blockchain technology, but it would depend on the specific requirements and constraints of the system. One potential benefit of using a decentralised approach is that it can help to ensure the integrity and security of the data being analysed. However, decentralised systems can also be more complex to design and implement, and may require additional resources in terms of computing power and storage. In terms of performance, a decentralised system may be slower than a centralised solution in some cases, but this will depend on a variety of factors, such as the specific algorithms and data structures being used, as well as the overall design of the system. Ultimately, the decision to use a decentralised approach will depend on the specific needs and goals of the analytical solution.

What can one do with blockchain data?

There are many different methodologies that can be applied within on-chain data analysis. Some common examples include:

Descriptive analysis

Descriptive analysis, which involves summarizing and describing the data, and can include things like calculating basic statistics and generating visualizations. This type of analysis is useful for getting an overall picture of the data, and can help to identify trends and patterns.

Exploratory analysis

Exploratory analysis, which involves more in-depth exploration of the data, and can include things like clustering and dimensionality reduction. This type of analysis is useful for uncovering hidden patterns and relationships in the data, and can help to generate hypotheses and ideas for further investigation.

Inferential analysis

Inferential analysis, which involves using statistical techniques to make inferences about a population based on a sample of the data. Different statistical methods are usually applied within this analysis type. This can include methods for calculating things like mean, median, mode, and standard deviation, as well as tools for testing hypotheses and performing regression analysis. This type of analysis is useful for making predictions and generalizations about the data, and can help to identify trends and patterns that are not immediately obvious.

Predictive analysis

Predictive analysis which involves using machine learning algorithms to make predictions about future events or outcomes based on the data. This type of analysis can be used to identify trends and patterns in the data, and can be used to make predictions or recommendations. Usually techniques like clustering, classification, and regression, which can be used to identify patterns and relationships in the data, are included.

The specific methodology used for on-chain data analysis will depend on the goals and requirements of the analysis, as well as the nature of the data itself.

Let’s talk about data visualization. It is a common analytical tool that can be used to represent complex data in a visual format. This can include tools like charts, graphs, and maps, which can help to identify trends and patterns in the data. For example, a line chart could be used to show the trend of a particular cryptocurrency’s price over time, while a bar chart could be used to compare the market capitalization of different cryptocurrencies. Data visualization tools can also be used to create interactive visualizations, which allow users to explore the data in more depth and interact with it in real time. This can be useful for identifying relationships and patterns that may not be immediately obvious from looking at the raw data.

One may ask - why should I use visualization tools when explorers already return exhaustive information? Data visualization tools and block explorers are both tools that can be used to analyse on-chain data, but they serve different purposes and provide different types of information.

Data visualization tools are focused on representing the data in a visual format, which can make it easier to understand and identify trends and patterns. By contrast, block explorers are online tools that allow users to browse the blockchain and view information about specific blocks, transactions, and addresses. They provide a user-friendly interface for accessing and interacting with the data on the blockchain, but they typically do not include advanced analysis or visualization features. In general, data visualization tools can be used in combination with block explorers to gain a more comprehensive understanding of the data on the blockchain.

Web3; data science; job opportunities

There are four things to think about while discussing the future of Web 3 and data science:

More job opportunities for data scientists and other data professionals will be made available by Web 3. This is due to the fact that organizations preparing to adopt Web 3 will have a huge need for people with extensive experience in data analysis, interpretation, and product and service creation using the data at hand while incorporating AI and ML into the equation.

Users and data scientists will benefit financially from Web 3. Companies will have the option to buy data directly from users (allowing data owners to sell their data to whoever they want), combine and blend these new data sets with existing data sets to improve learning models, and then sell the new insights on the open market.

Data scientists can apply AI to more thoroughly comprehend particular customer needs on Web 3. Data companies can create language models that bring “semantic understanding” because Web 3 is individual or user-focused, and because data is linked to user interaction, they can then create solutions that are specifically tailored to the user. Data companies can also extract insights from the raw data and then transform those insights into better product recommendations that can enhance the customer experience primarily based on customer expectations.

Data scientists will have a much bigger impact on the global economy in the Web 3 era. They will develop into the new “neurons” who can assist in creating content or AI models that can coordinate with other AI models and address more complicated problems or potential risks to businesses or organizations.