A while back, I shared a pattern for extracting and reporting on Anaplan audit data using a Python project hosted on GitHub. I wrote that during my time on the Operational Excellence Group (OEG) at Anaplan. The Python solution still works, and plenty of teams are running it today. The problem it solves hasn't changed: Anaplan audit data is comprehensive, but it's formatted for systems rather than people. Events come back as numeric codes. Users, models, and workspaces are referenced by internal IDs. Stitching everything into a readable narrative takes effort.
What has changed is that you no longer need Python or custom scripting to do it, nor a local server to host and schedule it.
I've since co-founded Nexadata, now an Anaplan Technology Partner, and we've published a Quick Start Guide that builds the same audit log extraction pattern using Anaplan's native audit endpoints and a few AI Copilot prompts. The result is the same kind of clean, analyst-ready audit dataset, built in the browser in about 35 minutes, with the workflow scheduled directly inside the platform. No script to host, no separate scheduler to maintain.
The three stages will look familiar:
Extract. Pull native Anaplan audit data, including events, users, workspaces, models, and the action types (Imports, Exports, Processes).
Harmonize. Join the lookup datasets onto the events stream so every record carries readable user names, workspace names, model names, and action descriptions, along with a translated event message in place of a numeric code.
Analyze. Land the result in an analytics-ready shape that a BI tool, an audit report, or a security alert can consume directly.
The output answers four questions at a glance: who did what, where, and what it means. Once scheduled, the workflow runs on its own, and the audit dataset stays up to date with no further effort.
A note on AI use. AI Copilot is used only to design the transformation pipeline. Once you approve the build plan, all data operations run inside the Nexadata platform. The reasoning happens in AI; the data just passes through Nexadata.
The full Quick Start Guide is here:
Free for Anaplan customers. Nexadata is free to use for any Anaplan customer who deploys it for Anaplan audit analytics. No platform fees, no usage charges for this use case.
For teams that prefer Python and have the infrastructure to host and maintain it, the original solution is still a solid option. For teams that would rather skip the hosting altogether, Nexadata is a no-code alternative. And because it's a general-purpose integration platform, the same approach applies to other Anaplan data flows beyond audit, including pulling external data in and pushing transformed data back out.
Curious to hear what others are doing for Anaplan audit reporting today. If you're running the original Python solution, building something custom, or experimenting with this no-code approach, drop a comment. Always interested in what's working.
