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Recommendations Data

Use this article to understand the Recommendations Data cube in Analytics and how to build custom reports for recommendation status, source, savings, asset, resource, and timeline analysis.

Overview

Recommendations Data is the Analytics view for recommendation records managed in Yarken.

Use it when the standard Recommendations Overview report does not answer a detailed question, or when you need to build a custom analysis for optimization reviews, FinOps reporting, governance meetings, or executive savings updates.

For shared Analytics workspace behavior, report building, visualizations, filters, saved reports, and dashboard controls, refer to Getting Started with Analytics.

Open Recommendations Data

To open Recommendations Data:

  1. Go to Analytics.

  2. Select Recommendations.

The page opens the Recommendations Data analytics cube.

What Recommendations Data helps you answer

Use Recommendations Data to answer questions such as:

  1. Which recommendations have the highest savings potential?

  2. Which recommendations have already delivered realized savings?

  3. Which sources are generating the most recommendation activity?

  4. Which recommendation categories remain unresolved?

  5. Which assets or cloud resources need attention?

  6. Which recommendations are high value and low complexity?

  7. Which months had the highest recommendation activity?

  8. Which provider-sourced recommendations are still waiting for action?

This helps teams move from record management to analysis, reporting, and prioritization.

Data source relationship

Recommendations Data depends on recommendation records in Yarken.

Recommendation records may be:

  1. Created manually in Admin > Recommendations.

  2. Uploaded in bulk through Admin > Recommendations > Upload.

  3. Imported through configured Advanced Integrations and recommendation pipelines.

  4. Updated as teams review, action, complete, or dismiss recommendations.

Changes to recommendation status, savings values, source, category, asset, or resource information affect Analytics and dashboard outputs where those fields are used.

After recommendation records, upload rules, connections, or pipelines are updated, run Cube Refresh from the bottom of the Admin menu so internal data refreshes before validating Analytics results.

Recommendation fields

The Recommendations group contains the primary optimization and recommendation fields.

Common fields include:

Field

Use it to understand

Recommendation ID

Unique recommendation reference.

Recommendation Name

Short name or title of the recommendation.

Source

Where the recommendation originated.

Asset Type

Asset category associated with the recommendation.

Asset Code

Asset identifier linked to the recommendation.

Category

Recommendation classification.

Recommendation Details

Description of the recommendation or action.

Status

Current lifecycle status.

Cloud Account ID

Cloud account associated with the recommendation, where available.

Cloud Resource ID

Cloud resource associated with the recommendation, where available.

Resource Type

Resource category such as EC2, EBS, RDS, S3, Savings Plans, or another configured type.

Complexity

Relative implementation effort or difficulty.

Value

Relative business or financial value.

Recommendation Date

Date the recommendation was identified or recorded.

The exact fields available may vary by environment, data model, and source data.

Savings measures

Recommendations Data separates opportunity value from delivered value.

Measure

Meaning

Potential Savings

Estimated savings available if recommendations are implemented.

Realized Savings

Savings achieved after recommendations have been completed or otherwise realized.

Use both measures together to understand the size of the remaining optimization opportunity and the value already delivered.

Timeline fields

Timeline fields support trend analysis.

Common timeline fields include:

  1. Year

  2. Quarter

  3. Month

Use timeline dimensions to track recommendation activity, potential savings, and realized savings over time.

Start with a clear question, then choose fields and measures that answer it.

Question

Suggested structure

Which recommendations have the highest opportunity?

Recommendation ID, Recommendation Name, Category, Potential Savings, Complexity, Status.

Which recommendations have delivered value?

Recommendation Name, Status, Realized Savings, Month, Source.

Which sources generate the most savings opportunity?

Source, Potential Savings, Realized Savings, Month.

Which recommendations remain active?

Recommendation ID, Recommendation Name, Status, Recommendation Date, Value, Complexity.

Which cloud resources require review?

Cloud Account ID, Cloud Resource ID, Resource Type, Recommendation Details, Potential Savings.

Which categories should be prioritized?

Category, Potential Savings, Realized Savings, Value, Complexity.

How is savings changing over time?

Month, Potential Savings, Realized Savings, Recommendation Count.

Save recurring reports for monthly FinOps reviews, optimization governance, and leadership updates.

Relationship with Recommendations Overview

Use Recommendations Overview when you need the standard dashboard view of recommendation savings by month, asset type, and source.

Use Recommendations Data when you need custom reporting, deeper field-level analysis, or a saved report that answers a specific team question.

Both views depend on the same underlying recommendation records.

Relationship with integrations and uploads

Provider integrations, manual uploads, and manual entry all feed recommendation records into Yarken.

If a Recommendations Data report does not show expected values, the issue may be caused by:

  1. Missing or failed provider ingestion.

  2. A disconnected recommendation source.

  3. An inactive recommendation pipeline.

  4. Upload file errors.

  5. Incorrect field mapping.

  6. Recommendation records missing savings or status values.

  7. Filters that exclude the expected records.

  8. Cube Refresh not being run after the latest recommendation data or configuration change.

Check the Recommendations workspace, uploaded file history, pipeline run history, and Cube Refresh status before relying on the report output.

Common use cases

Recommendations Data is commonly used for:

  1. FinOps optimization tracking.

  2. Cloud savings reviews.

  3. Recommendation lifecycle reporting.

  4. Provider recommendation analysis.

  5. Rightsizing and reserved commitment reviews.

  6. Recommendation backlog management.

  7. Executive savings reporting.

  8. Optimization governance meetings.

  9. Comparing potential and realized savings.

  10. Identifying high-value, low-complexity recommendations.

  1. Review potential and realized savings together.

  2. Use status to separate active, completed, and dismissed work.

  3. Filter by month or quarter when reviewing trends.

  4. Prioritize reports around value and complexity.

  5. Standardize recommendation sources and categories where possible.

  6. Save recurring reports for governance reviews.

  7. Run Cube Refresh after recommendation records, upload rules, connections, or pipelines are updated.

  8. Compare Analytics totals with the Recommendations workspace when validating data.

  9. Review unresolved recommendations regularly.

Troubleshooting Recommendations Data results

If Recommendations Data does not show expected values, check:

  1. The active Month, Quarter, or Year filters.

  2. Whether recommendation records exist for the selected period.

  3. Whether status values were updated correctly.

  4. Whether potential and realized savings values were entered or loaded correctly.

  5. Whether the selected field belongs to the Recommendations group.

  6. Whether uploaded files show the expected loaded record counts.

  7. Whether recommendation integrations are connected and pipelines have run successfully.

  8. Whether source, category, asset, and resource fields were mapped consistently.

  9. Whether Cube Refresh was run after recommendation data, upload rules, connections, or pipelines were updated.

If savings totals still appear incorrect, compare Analytics results against the underlying recommendation records in Admin > Recommendations.

Next step

Recommendations Overview

Related content

Recommendations

Savings Recommendations

Manage Recommendations

Upload Recommendations

Set up Recommendation Integrations

Getting Started with Analytics