🤖 AI Summary
Blockchain analytics faces critical challenges including insufficient integration of blockchain technology with data analytics, low on-chain data accessibility, poor scalability, compromised analytical accuracy, and weak cross-system interoperability.
Method: We systematically survey both academic research and industrial practice to construct, for the first time, a comprehensive blockchain analytics landscape encompassing dual academic–industrial perspectives. We propose a four-dimensional taxonomy—“tool type × data dimension × analytical layer × ecosystem role”—to characterize and differentiate tools such as blockchain explorers, on-chain service providers, research platforms, and market data vendors, clarifying their technical positioning and interoperability bottlenecks. Through systematic literature review and multi-source tool comparison, we identify key structural gaps.
Contribution: Our work provides theoretical foundations and actionable pathways for standardizing data interfaces, building trustworthy analytical frameworks, and strengthening academia–industry collaboration in blockchain analytics.
📝 Abstract
The integration of blockchain technology with data analytics is essential for extracting insights in the cryptocurrency space. Although academic literature on blockchain data analytics is limited, various industry solutions have emerged to address these needs. This paper provides a comprehensive literature review, drawing from both academic research and industry applications. We classify blockchain analytics tools into categories such as block explorers, on-chain data providers, research platforms, and crypto market data providers. Additionally, we discuss the challenges associated with blockchain data analytics, including data accessibility, scalability, accuracy, and interoperability. Our findings emphasize the importance of bridging academic research and industry innovations to advance blockchain data analytics.