How We Automated BI Content Cleanup Across Multiple Analytics Servers

The challenge of managing enterprise-scale analytics platforms involves dealing with an ever-growing amount of content that far outpaces removal. Inefficient use of disk space, unnecessary running of scheduled refreshes, and escalating backup resource consumption can degrade performance over time. Attempting to resolve this issue through either manual reviews or fully automated deletion proved impractical and risky.

Manual processes were too slow and error-prone, while automated deletion posed the threat of accidentally removing critical data. The solution the engineering team devised combined careful manual validation with automated processes to ensure safety while still delivering operational benefits.

The key challenge was identifying content that had become stale or unused without mistakenly deleting important assets. Traditional automated cleanup approaches either failed due to the scale of the problem or introduced unacceptable risks. The team needed a system that could reliably flag and quarantine content for safe removal without permanently deleting it.

This approach allowed them to preserve the option to restore any mistakenly quarantined content, as well as intervene if an asset turned out to be needed after all. In essence, they designed a system whose default failure mode was inconvenience, not data loss.

To accomplish this, the content discovery engine was built to evaluate multiple signals simultaneously to compute a staleness score for each asset. Instead of relying on a single metric like last access date, the composite score combined factors such as access frequency, refresh activity, embedded usage patterns, and ownership status.

Assets with lower scores remained active, while those meeting certain thresholds were moved to quarantine. Retention policies could be customized for different environments and projects, with thresholds adjusted based on business needs and compliance requirements. This flexible, risk-aware system enabled the platform to automatically clean out unnecessary content while preserving the ability to recover from accidental deletions.