Recommendations Data | Yarken Help

Recommendations Data helps teams analyze optimization opportunities, savings initiatives, recommendation status, and recommendation performance across technology spend.

Use this Analytics cube to investigate potential savings, realized savings, recommendation categories, implementation progress, recommendation sources, and operational optimization activity.

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

What Recommendations Data helps you answer

Recommendations Data helps teams answer questions such as:

Which recommendations have the highest savings potential?
Which optimization opportunities have already been implemented?
Which providers or cloud platforms generate the most recommendations?
Which recommendation categories remain unresolved?
Which assets or cloud resources require attention?
Which recommendations are high value but low complexity?
Which savings opportunities are recurring across months?
Which recommendation sources are producing the most realized savings?

The cube gives teams a structured way to analyze optimization opportunities across financial, operational, cloud, and FinOps workflows.

Access Recommendations Data

To open Recommendations Data:

Navigate to Analytics
Select Recommendations

The page opens the Recommendations Data analytics cube.

Recommendation sources

Recommendations may originate from:

Manual optimization reviews
Cloud provider recommendations
FinOps analysis
Infrastructure reviews
Asset analysis
Vendor optimization initiatives
Sustainability reviews
AI-assisted optimization workflows

Recommendations can be created manually or sourced from integrated cloud and optimization platforms.

Recommendations fields

The Recommendations group contains the primary optimization and recommendation fields.

Common fields include:

Field	Use it to understand
Recommendation ID	Unique recommendation reference
Source	Where the recommendation originated
Asset Type	Technology or asset category associated with the recommendation
Asset Code	Asset identifier
Asset Name	Asset or resource name
Category	Recommendation classification
Recommendation Details	Description of the recommendation
Status	Current implementation or review status
Cloud Resource	Cloud service or resource associated with the recommendation
Complexity	Relative implementation complexity
Value	Recommendation priority or business value
Recommendation Date	Date associated with the recommendation
Potential Savings	Estimated savings opportunity
Realized Savings	Savings achieved after implementation

Use these fields to track recommendation lifecycle, implementation progress, savings performance, and operational prioritization.

Timeline fields

The Timeline group supports time-based recommendation analysis.

Common timeline fields include:

Year
Quarter
Month

Use timeline dimensions to:

Track recommendations over time
Review savings trends by month or quarter
Compare realized savings against historical recommendations
Analyze implementation progress across reporting periods

Recommendation categories

Recommendation categories help organize optimization opportunities into operational themes.

Categories may vary depending on integrations and customer workflows, but commonly include:

Cost optimization
Resource rightsizing
Reserved instance recommendations
Savings plan opportunities
Underutilized resources
Idle resources
License optimization
Sustainability improvements
Vendor optimization
Operational improvements

These categories help teams prioritize work and organize optimization programs.

Potential savings vs realized savings

Recommendations Data separates identified savings opportunities from implemented savings.

Measure	Meaning
Potential Savings	Estimated savings available if the recommendation is implemented
Realized Savings	Savings already achieved after implementation

This distinction helps teams understand:

The size of the optimization pipeline
Delivered optimization value
Remaining savings opportunities
Recommendation implementation effectiveness

Use both measures together when reporting optimization maturity and FinOps outcomes.

Complexity and prioritization

Recommendations often include implementation complexity and value indicators.

These fields help teams prioritize recommendations based on:

Estimated effort
Operational risk
Financial impact
Business value
Implementation feasibility

For example:

High-value, low-complexity recommendations are often prioritized first
High-complexity recommendations may require architecture review or governance approval
Low-value recommendations may be deferred or grouped into larger optimization initiatives

Relationship with Recommendations admin

Recommendations Data in Analytics depends on recommendation records managed within Yarken.

Use the Recommendations admin area to:

Create recommendations
Update recommendation status
Track implementation progress
Capture optimization opportunities
Document savings initiatives
Store recommendation details

Use Recommendations Data in Analytics to analyze and report on those recommendations at scale.

Relationship with FinOps and cloud optimization

Recommendations Data supports FinOps and cloud optimization workflows.

Use it to:

Identify optimization opportunities
Review provider-generated recommendations
Prioritize cloud savings initiatives
Track implementation outcomes
Analyze recurring optimization patterns
Measure realized cloud savings

Recommendations may be associated with:

AWS recommendations
Azure recommendations
Cloud optimization tooling
FinOps operational reviews
Internal optimization programs

This helps connect optimization work directly to financial outcomes.

Relationship with Analytics investigations

Recommendations Data works alongside other Analytics cubes.

Typical workflow:

Detect unusual spend or usage in Analytics
Investigate consumption or provider trends
Create or review optimization recommendations
Track recommendation implementation
Measure realized savings outcomes

For example:

Use Consumption Data to identify abnormal cloud spend
Use Recommendations Data to track remediation opportunities
Use Spend or Chargeback reporting to measure downstream financial impact

Relationship with sustainability optimization

Recommendations can also support sustainability initiatives.

Optimization opportunities may include:

Reducing underutilized infrastructure
Lowering unnecessary compute consumption
Consolidating services
Removing idle resources
Improving resource efficiency

These activities can help reduce both financial cost and environmental impact.

Recommended report structures

Start with a clear optimization question, then build the report around recommendation status, value, savings, and ownership.

Optimization question	Suggested report structure
Which recommendations have the highest savings opportunity?	Category, Recommendation Details, Potential Savings, Complexity, Status
Which providers generate the most optimization activity?	Source, Provider, Potential Savings, Realized Savings, Month
Which recommendations remain unresolved?	Recommendation ID, Asset Name, Status, Recommendation Date
Which optimization work has already delivered value?	Category, Realized Savings, Recommendation Date, Month
Which cloud resources require review?	Cloud Resource, Asset Type, Recommendation Details, Potential Savings
Which recommendation categories deliver the most savings?	Category, Potential Savings, Realized Savings
Which months had the highest optimization activity?	Month, Recommendation Count, Potential Savings

Save recurring reports for FinOps reviews, optimization governance meetings, and executive savings reporting.

Common use cases

Recommendations Data is commonly used for:

FinOps optimization tracking
Cloud savings reviews
Recommendation lifecycle reporting
Provider optimization analysis
Rightsizing initiatives
Operational improvement tracking
Recommendation backlog management
Executive savings reporting
Recommendation prioritization
Optimization governance reviews

Recommended practices

Use these practices when working with Recommendations Data:

Review both potential and realized savings together
Use recommendation status to separate active and completed work
Filter by month or quarter when reviewing optimization trends
Prioritize reports around value and complexity
Standardize recommendation categories where possible
Save recurring optimization reports for governance reviews
Link recommendation reporting with spend and consumption analysis
Review unresolved recommendations regularly

Consistent recommendation tracking improves optimization accountability and helps teams measure delivered value.

Troubleshooting Recommendations Data results

If Recommendations Data does not show expected values, check:

The active Month, Quarter, or Year filters
Whether recommendation records exist for the selected period
Whether recommendation status values were updated correctly
Whether savings values were loaded or entered correctly
Whether the selected field belongs to the Recommendations group
Whether cloud recommendation integrations are configured correctly
Whether recommendation categories and sources were mapped consistently

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

Next step

Business Case

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