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Blockchain data analytics in a nutshell

Blockchain data analytics in a nutshell

Tue, Dec 13, 20225 min read

Category: Business-stories

As blockchain technology is still evolving, with multiple new products, chains, and start-ups established daily, blockchain data analytics is in high demand. Because of how the data on the chain is structured, analyzing what’s been going on within a blockchain platform or dApp might be a tricky and complex challenge. Blockchain analytics requires specific skills and tools and is not easy to perform. To understand it better, let’s dive into the basics of on-chain data analytics.

In today’s post, we look into the complex world of big data mixed with blockchain technology. Here’s the content breakdown:

  • What is blockchain analytics?

  • Why is blockchain analytics important?

  • What are the challenges of blockchain analytics?

  • How to execute blockchain data analysis?

What is blockchain analytics?

In the broadest of definitions, data analytics is the art of drawing conclusions from raw information sets. In today’s world, where data is king, it’s hard to imagine a company that wouldn’t take advantage of its power. The same applies to analyzing data stored on the blockchain. Blockchain data analytics is the process of identifying, analyzing, and clustering data stored on the chain. It helps in understanding information about users and also in preventing fraud and money laundering by identifying anomalies. 

Why is blockchain analytics important?

Amongst many reasons for the benefits of blockchain data analytics, safety is probably the most crucial one. As blockchain becomes more and more popular, a growing number of businesses look into trading and accepting cryptocurrencies as a form of payment and investment. Currently,the total value of all existing cryptocurrencies is estimated at around $919 billion with84 million crypto wallets registered. And we've all heard cautionary tales of Terra, Luna, or FTX and wondered if, and how, those could've been prevented. One of the ways of improving the safety of crypto transactions is to use the power of blockchain data analytics to our advantage. Crypto regulatory compliance is another field that benefits from blockchain data analysis greatly. Due to learning how users operate using blockchain financial platforms lawmakers can make accurate decisions when creating much-needed cryptocurrency policies. It's something that already exists in the world of traditional finance but is still very fresh and uncharted when it comes to blockchain transactions. Analytics is the best way of learning and predicting the shape of future cryptocurrency systems and making them safe and reliable.

Benefits of blockchain data analytics

  • fraud protection

  • criminal activities detection

  • crypto risk management

  • money laundering prevention

  • regulatory compliance support

  • a better understanding of users' behavior and needs

  • dApp improvement

What are the challenges of blockchain analytics?

While the benefits of on-chain data analysis are undeniable, the whole process is difficult and complex. There are a couple of major challenges awaiting blockchain aficionados who wish to make use of the generated data:

  1. Lack of standardization: There is no standardized data format or schema for blockchain data, which makes it difficult to analyze and compare across different blockchain networks.

  2. Data privacy: blockchain networks are designed to be secure and protect user privacy, which can make it challenging to access and analyze data.

  3. Data volume: blockchain networks generate vast amounts of data, which can make it difficult to manage and analyze efficiently.

  4. Data quality: on-chain data can be incomplete, inaccurate, or inconsistent, which can impact the accuracy and reliability of the analysis.

  5. Scalability: blockchain analytics tools and systems must be able to handle large volumes of data and transactions in real time.

  6. The decentralized nature of the blockchain: Since the blockchain is decentralized, there is no central authority or organization that owns or controls the data, making it difficult to obtain a complete and accurate picture of the network.

  7. How to execute blockchain data analysis?

    Now that we've covered the basics, let's look into the process of analysis itself, taking the Ethereum network as an example. To perform blockchain data analytics on Ethereum, developers use block explorers (Etherscan,Etherchain,Ethplorer, orBlockchair) which enable insight into real-time data on blocks, miners, accounts, transactions, etc. To be able to use the data efficiently, blockchain engineers need to index and query the data. One of the most popular indexing tools for Ethereum is The Graph, which is a decentralized protocol made for organizing on-chain data. Before using The Graph, development teams used to develop their own proprietary indexing servers, which were resource intensive and broke the basic rules of decentralization.

    The most common open-source tool for Ethereum data analytics isEthereum ELT which converts the data into more convenient formats, like CSVs. Another popular analytics tool used to convert blockchain data into relational database tables, and then to query it using SQL isDune Analytics. Its most valuable feature is that even a non-technical person after some basic learning should be able to create a custom dashboard allowing them to display the required information.

    The summary

    With the rise of the demand for blockchain analytics, the list of available tools is ever-growing. Keeping up with them is a must if you wish for your blockchain product to succeed. Without analytics, your dApp won't address the needs and requirements of its users properly. Overall, the goal of blockchain data analytics is to use it to gain insights and make informed decisions about the use of blockchain technology. It's important for everyone interested in blockchain product development, but also for lawmakers. In order for them to be able to create policies that actually accelerate blockchain business growth, they first need to analyze the data that is available. 

Agnieszka
Agnieszka

Head of Business Development

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