Identify above trend storage consumption

This page describes how to use Storage Intelligence advisor to identify above-trend storage consumption. Identifying growth trends helps you determine whether you are storing data unnecessarily and optimize your storage usage.

When total bytes stored increases above trend, Storage Intelligence advisor displays a Total bytes stored increased above trend over the last 30 days finding. A finding is an anomaly detected from your metrics that needs your attention. For an overview of Storage Intelligence advisor, see About Storage Intelligence advisor. For a full list of findings, see Findings reference catalog.

Thresholds that trigger a finding

To identify above-trend growth, Storage Intelligence analyzes historical data over a 30-day period to establish a baseline for storage consumption across your project, folder, or organization. Storage Intelligence advisor generates a finding when storage consumption increases above this baseline.

Findings reflect the previous day's activity. For example, a spike occurring on Tuesday appears in Storage Intelligence advisor on Wednesday.

Respond to a finding

To respond to the finding, identify which data is causing the growth and determine the best management strategy.

  1. View the finding details.

  2. On the Finding details page, use the Buckets with largest increases table and the bucket-specific Prefixes with largest increases to identify which datasets are growing.

  3. Once you know which data is growing, decide if that data needs to be managed differently based on its access patterns or retention needs. Use the following table to select the recommended strategy for your scenario:

    Scenario Recommended action Details
    Data is not needed after a specific period Apply lifecycle rules If you no longer need data after a specific period or want to move data to a colder storage class, use Object Lifecycle Management rules to manage your storage consumption.
    Data with changing access patterns Enable Autoclass Autoclass automatically transitions objects in your bucket to appropriate storage classes based on access patterns. It moves less frequently accessed data to colder storage classes and more frequently accessed data to warmer storage classes, which helps manage costs automatically.
    Need object-level details to understand storage growth Investigate with Storage Insights datasets If you need granular information about storage growth, use Storage Insights datasets to export metadata to BigQuery. You can then query the dataset to identify the specific prefixes and objects that contribute to the growth.

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