Chainalysis Launches No-Code Automation Workflows For Blockchain Intelligence

Operational Impact: The tool aims to democratize data science, allowing broader teams to replicate high-level investigations like Coinbase’s recent fentanyl sprint, which generated 41 intelligence packages globally.

Blockchain intelligence and data analytics firm Chainalysis announced the launch of Workflows, a no-code solution for automating data analytics functions.

The new automation tool allows users to implement the firm’s Data Solutions threat monitoring service, a platform enabling data scientists to run queries using SQL and Python programming scripts to conduct deep data analysis, without writing any code.

Instead, Workflows users have the option to select from one of the available automated workflows and fill in the necessary information through plain language text boxes. The tool itself then runs the requested analytics.

According to a Jan. 20 blog post from Chainalysis, current workflow modules include Timing and Amount Analysis, Threat Actor Network Expansion via Mutual Counterparty Analysis, and Targeted Wallet & Cluster Search. The firm plans to add “hundreds of no-code workflows over time.”

Democratizing data analytics

Chainalysis’ Data Solutions tool is designed to accelerate workflows for power users. As an example use case, the firm cites a Coinbase investigation that resulted in the distribution of 41 intelligence packages to twelve countries providing a detailed view into the digital infrastructure underpinning illicit fentanyl distribution at the global scale.

Data analysis is a complex endeavor, even for seasoned developers, and one of the major bottlenecks is automation. In most cases, data scientists aren’t typically looking for a single data signal like a needle in a haystack. Instead, it’s more like they’re looking for all the needles in all the haystacks and they’re trying to determine where each one originated from.

This usually involves repeating the same query thousands or even millions of times. Automation allows users to set up workflows that handle this on their behalf. Traditionally, data scientists have accomplished this by writing their own SQL or Python instructions. This allows them to define specific parameters relative to their own in-house data and the specific onchain data they’re analyzing.

With the launch of Workflows, ostensibly any user with access to Chainalysis’ tools can conduct deep data analysis with automation. The company noted that “technical users” who still wish to write their own automation instructions in SQL or Python will still have the option to do so.

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