A lot of data is produced on a daily basis in the crypto markets. Each transfer of a token, wallet interaction, smart contract call, and deposit to an exchange that has occurred on a public blockchain adds another piece to the puzzle.
The issue is that raw blockchain information may be tough to comprehend. A transaction’s source and destination addresses may be thousands of ETH. A block explorer may show that thousands of ETH were sent between these addresses, but it is not necessarily the case that a market expert explains what was happening in these addresses, what these addresses are in control of, or where the funds came from.
This is where blockchain analytics platforms come in handy. Glassnode, Nansen, Dune, DFLLama, Token Terminal, TRM Labs, and Arkham are the tools that convert blockchain records into information that can be more easily analyzed by researchers, institutions, compliance teams, and investigators.
Blockchain Analytics: What is it and what does it do?
Blockchain analytics involves the analysis, clustering, attribution, modelling, and visual mapping of data stored on public distributed ledgers. It processes transactions, wallet addresses, token movements, and other on-chain patterns, providing valuable insights.
It can be considered a digital detective exercise. Analytics platforms don’t just look at individual transactions; they also look at how addresses and activities relate over time. This can be utilized in numerous ways. One investor could analyze the flow of coins on the blockchain. A researcher could study the activity on a DeFi protocol. In the interim, a compliance team might review and determine if money has been passed through high-risk addresses.
Public blockchains generate a permanent record of transactions, which, in comparison to traditional markets, represents a massive amount of financial data that can be used by researchers.
What is Blockchain Analytics?
Data collection is the first step in the process. Analytics firms gather publicly available data, including transactions, wallet addresses, dates, amounts, token transfers, and transactions with smart contracts, directly from the blockchain nodes or via APIs. The information is then broken down and normalised to be more easily analysed across millions of transactions.
Platforms can create transaction graphs of the movement of funds from one address to another. These techniques can then be used to cluster addresses which seem to be under the same control.
Common-input heuristics can detect addresses that occur in the same transaction, for instance. There are other clues, like behavioral patterns, timing analysis, and change-address detection.
Attribution is the next step. Platforms use external data to supplement blockchain data to match addresses to known entities that can be anything from an exchange to a DeFi protocol, a custodial service, or even a high-risk service.
Through heuristics, graph analysis, risk models, and increasingly machine learning, analytics systems can detect patterns and anomalies. The output is actually much more beneficial than simply a list of transaction hashes.
Glassnode: Understanding Market Behavior
Glassnode is a company most famous for its on-chain market intelligence. It creates metrics that researchers can use to look at the behavior of the participants on crypto networks.
In addition to price, researchers can analyze metrics like exchange balances, active addresses, realized profits and losses, and token supply distribution, among others.
These indicators can provide additional context around market movements.
Nansen: Following Wallets and Capital
Nansen takes a more wallet-focused approach to blockchain research. One key feature is wallet labeling, which helps turn anonymous-looking blockchain addresses into more understandable categories.
The platform empowers researchers to analyze token movement, portfolio activity, DeFi engagement, and wallet transactions and capital flow.
This is helpful because blockchain addresses aren’t contextual. Once a researcher can determine if an address is likely a transaction that was connected to an exchange, fund, protocol or another kind of market participant, that transaction is more informative.
By using Nansen, researchers can begin to chart wallet habits, rather than just individual transactions.
Dune: Turning Blockchain Data Into Dashboards
Dune provides researchers with yet another method of on-chain analysis. Users can query the blockchain data and construct their own custom dashboards instead of depending on pre-defined indicators.
These dashboards can monitor volumes on decentralized exchanges, stablecoin activity, protocol usage, and so much more.
It is also organized around a community, allowing researchers to peruse dashboards developed by others.
Customizable queries offer a lot of flexibility for the more advanced user. Researchers can create analyses based on their own questions, rather than following the metrics offered by a particular platform.
DeFiLlama and Token Terminal: Researching Crypto Fundamentals
DeFi research requires more than watching token prices.
DeFiLlama is particularly valuable for benchmarking protocols and blockchain ecosystems by analyzing key metrics like total value locked, stablecoin usage, decentralized exchange volumes, fees, and revenue. These metrics can be used to gauge whether a protocol is successful in bringing in money and valuable economic activity.
At Token Terminal, we take another blog view into crypto research, grouping blockchain and protocol-related data based on the financial metrics. Investigators can look into topics like fees, revenue, and user activity.
These platforms illustrate the trend towards fundamental analysis in crypto research. Researchers increasingly can pose not only the question of whether a token’s price is going up, but also whether that underlying network or application is being utilized.
TRM Labs and Arkham: Following the Flow of Funds
Blockchain analytics is especially potent when researchers are interested in where money is coming from and where it’s going.
Blockchain intelligence, investigations, and risk management are a major priority for TRM Labs. It blends together transaction graphs, address clustering, entity attribution, heuristics, and risk models to enable organizations to investigate on-chain activity.
The normal workflow can start with raw transaction data. Addresses are clustered and enhanced with information about who they belong to. Then, transactions can be analyzed for specific risk indicators and further analyzed for relevant fund flows.
Arkham also prioritizes linking blockchain activities to entities and represents the movement of funds.
These features highlight the distinction between a basic blockchain explorer and a robust analytics tool. It’s one thing to know that wallet A sent assets to wallet B. The network of wallets and entities around that transaction gives a much deeper understanding.
Blockchain analytics goes beyond the scope of investment research
Blockchain analytics isn’t confined to traders.
Financial institutions can deploy analytics to keep track of digital-asset transactions and evaluate counterparts, while also aiding in compliance efforts. The technology can be utilized by crypto exchanges and custodians to monitor transactions and detect suspicious activity.
Authorities can monitor crypto transactions from wallet addresses and link wallets together. Regulators can also test their transactions and possible market abuse.
As more institutions join the fray, analytics could become more critical as well when the time comes to run operations. Institutional activity can occur on-chain with Crypto investment products, Staking operations, Custodian systems, and tokenised financial products.
The Capabilities of Blockchain Analytics
Transparency means that public blockchains are transparent but not all transactions are easy to understand.
Addresses are generally pseudonymous. One organization might have thousands of addresses and one exchange wallet can hold thousands of customers’ activity.
Clustering and attribution techniques can be useful, but can also include assumptions. What an analytics provider considers “address” and what he/she calls “metrics” could vary.
Therefore, researchers need to refrain from making assumptions about the intent of the person operating the blockchain movement based on the movement’s movement. Placing tokens on the exchange, for instance, does not mean that the tokens will be offered for sale.
It’s best to use on-chain information in conjunction with other evidence.
The future of Blockchain Analytics is here, with AI at its core.
AI could render blockchain analytics much more user-friendly.
The natural-language interfaces could enable analysts to pose complex questions to blockchain without having to sift through thousands of transactions. Machine learning can also help with pattern matching, anomaly detection and big graph analysis of transaction graphs.
Cross-chain analytics will also play an important role. The activity of cryptocurrencies is now making its way from one network to another, from one bridge to another, from one Layer 2 system to another, and from one DeFi platform to another. Research instruments must then be able to track activities beyond a single blockchain.
Despite this, the human eye plays an important role. While AI can facilitate the organisation of information and highlight unusual patterns, the interpretation of these patterns requires the input of researchers.
Conclusion
Blockchain analytics has advanced a lot further than just searching for transactions.
These platforms, including Glassnode, Nansen, Dune, DeFiLlama, Token Terminal, TRM Labs, and Arkham, transform raw blockchain data into actionable insights.
There are those who specialize in market behavior and fundamentals, and there are those who specialize in wallets, transaction flows, investigations, or compliance. Together, these tools are changing how researchers understand cryptocurrency markets and uncover what is happening across blockchain networks.
The growing adoption of blockchain in the DeFi, stablecoin, staking, tokenized assets, and institutional finance spaces will make on-chain data more useful. While blockchain analytics can’t give a sure prediction, it does provide researchers with a more transparent window into what is actually happening on-chain.