- **Descriptive **analytics asks “what happened?” and summarizes historical data, like tracking daily transaction volumes or identifying the largest token holders.
- **Diagnostic **analytics asks “why did it happen?” and examines patterns to understand causes, such as exploring why a protocol’s total value locked suddenly dropped.
- **Predictive **analytics asks “what might happen?” and uses statistical models to forecast outcomes, like estimating future network congestion based on historical patterns.
- **Prescriptive **analytics asks “what should we do?” and recommends specific actions, such as suggesting optimal times to execute large trades or advising on protocol parameter adjustments.
Introduction
Introduction
Blockchain technology promised transparency from its inception, a public ledger where every transaction would be visible to anyone who cared to look. Onchain analytics emerged as the discipline of extracting meaningful insights from this wealth of public blockchain data. At its core, it involves collecting, processing, and interpreting the transaction records, smart contract interactions, and state changes that occur on public blockchains. What began as simple block explorers has evolved into a sophisticated field combining data science, financial analysis, and behavioral economics.
At its core, onchain analytics is simply data analytics applied to blockchain data. Like any data-driven field, it builds upon the foundational principles of data analytics, also known as business analytics, which has evolved over decades across industries from retail to healthcare to finance.
Data analytics traditionally encompasses four main types or levels of sophistication, each answering a different type of question: