MrNetwork

What Blockchain Analytics Actually Is, and How to Do It we have covered data and analysis theory. now we apply it specifically to the blockchain. What are we analyzing, what does the data look like, and what process do analysts actually follow? blockchain analytics is not a special mysterious skill, It is the same data analysis process we have been discussing, applied to a specific type of structured, public, permanent data. once you understand the data source, the process becomes straightforward. What is Blockchain Analytics? Blockchain analytics is the process of analyzing data that lives on a public ledger, to understand what is happening across a network, a protocol, or a group of wallets. you are trying to understand things like: - network activity — how much is happening and in which direction - user behavior — who is doing what, how often, and with how much - token movement — where value is flowing and where it is accumulating - volume — is the trading activity real, or is it manufactured? every blockchain transaction leaves a permanent, public, unalterable record with these fields: - txn_hash - sender_address - receiver_address - amount - timestamp - and fee example: when you send money through opay, you get a receipt who sent it, who received it, the amount, the time. now imagine every single opay transaction ever made was public, permanent, searchable by anyone in the world, and impossible to alter or delete. that is essentially what a blockchain is. blockchain analytics is the skill of reading those receipts at scale across millions of transactions, and finding the stories hiding inside. The 6 step data analysis process Good analysis does not just happen randomly. It follows a structured process and getting each step right determines how useful the final output actually is. 1. define the question what exactly are you trying to find out? "Is this token being wash traded?" is a proper question. "show me all transactions" is not. A vague question produces useless analysis always. 2. collect the relevant data pull only what you need to answer that specific question. onchain, this might be wallet history, dex trade logs, LP deposits, or contract events depending entirely on what you're investigating. 3. clean and rearrange the data raw data is messy. duplicates, missing values, inconsistent formats. you clean it until it is structured, consistent, and ready to analyze without producing misleading results. rushing this step is how people draw wrong conclusions from correct data. 4. analyze the data now you actually look for patterns, outliers, and relationships. on dune analytics, this is your SQL query. on Arkham, this is following the money trail across wallet clusters. on Bubblemaps, this is visualizing holder connections. 5. visualize and present the findings turn your analysis into charts, graphs, and dashboards that other people can read and understand. your insight means nothing if the person reading it cannot follow what you found. presentation is part of the analysis. 6. act on the findings analysis is not the end goal, action is. whether that's a trade decision, a public investigation thread, a protocol recommendation, or a grant report you use what you found to do something. otherwise, what was the point? the majority of people who call themselves analysts stop at step 4 or 5. they run the query, screenshot the chart, and post it. Step 6 actually acting on findings and communicating a clear recommendation is where real analysis value is created. and it is also the step that gets you paid. stay tuned for the next.

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