Automation dashboards and value tracking
Track execution volume, delivery performance, labor savings, cluster utilization, and cost optimization recommendations from IaCM execution data.
Every pipeline run in Harness IaCM is recorded as execution data, and that data rolls up into dashboards that show both what is running and what it is worth. Because the dashboards read the same execution records that the pipelines already produce, there is no separate tracking system to maintain and no offline spreadsheet to reconcile.
This topic walks through the four dashboard views that automation owners use most: operational volume, automation analytics, cluster utilization, and cost optimization recommendations.
What you will learn from this topic
Operational dashboards: How to review pipeline execution volume, trend, and success rate across an account.
Automation analytics: How value realized, labor savings, and DevOps Research and Assessment (DORA) metrics are reported from execution data.
Utilization optimization: How to read cluster cost, forecasted cost, and efficiency score.
Cost optimization recommendations: How to turn a recommendation into a Jira ticket and track the savings it produces.
Before you begin
Before you use this guide, ensure you have the following:
Harness account with IaCM enabled: You need Infrastructure as Code Management under Infrastructure in Harness when it is entitled on your account.
Dashboard permissions: You need View on Dashboards at the scope you want to report on. Go to Harness RBAC to review role assignments.
Execution history: The dashboards report on completed pipeline runs, so a workspace must have been provisioned at least once before its data appears. Go to Provision workspaces to run a provisioning pipeline.
Cloud cost visibility: The utilization and recommendation views require Harness Cloud Cost Management to be connected to your cloud accounts.
Operational dashboards
The out-of-the-box dashboard for cloud engineering leadership aggregates data across every IaCM workspace in the account: the total number of workspaces, how that total has grown, and the status those workspaces are in. Alongside it, execution volume, week-over-week trend, and success rate are reported for the whole estate rather than pieced together pipeline by pipeline.
Each widget supports the same set of actions:
Drill down: Select a spike in the volume chart and choose the criteria to break it down by. The result is a second visualization, or a tabular view, of the total pipeline runs in that period.
Download: Export the data for an individual widget, or schedule delivery of the whole dashboard in several formats.
Alert: Create an alert on a widget, choose the delivery method and recipients, and schedule automated delivery to them.
Step-level and stage-level orchestration analysis is reported the same way, as is module and provider usage. Module analytics show the most-used modules as a tree map, and provider analytics give the same breakdown for providers. Go to Module registry to review how module usage is recorded.
Configuration management operations are covered by the same treatment: every playbook execution in the account, its success rate, average duration, and the top playbooks by usage, each linking through to its own execution record. Day-two configuration work therefore gets the same dashboard visibility as provisioning, instead of staying invisible until something breaks.
Automation analytics
This set of views answers whether the automation is paying for itself.
Delivery performance
DORA metrics report delivery performance using four standard measures: lead time to production, production deployment frequency, mean time to restore (MTTR), and change failure rate. All four are filterable by project ID and by service name, and can be trended daily or over a custom date and time range.
Taken together, the four measures are the reported view of automation productivity. Two of them speak directly to incident reduction:
Change failure rate reports failure rates by service, which identifies where incidents are concentrated.
MTTR reports how quickly an incident is resolved, service by service.
Value realized and labor savings
Monthly value realized is reported broken down by organization and by project, with labor savings trended against total pipeline runs over the same period. Because the figure is derived from the platform's own execution data rather than modeled offline, it is a direct answer to the question of what the automation returned.
Utilization optimization
Two views answer the utilization question from opposite ends: the cluster views report where infrastructure spend is not being used, and workspace-level insights report the same finding against the resource an engineer owns.
Cluster cost and efficiency
The cluster cost view reports spend and utilization across cloud providers: the most-used clusters by name, total cost, forecasted cost, and a rolling efficiency score. Read together, these make it clear where compute is being paid for and not used.
The cost explorer presents the same spend in a different shape: cost trend by cloud provider, grouped by whichever cost categories are configured, down to per-unit costs.
Runtime optimization
Right-sizing and other runtime recommendations derived from observed utilization are also surfaced on the workspace itself, on the Insights tab, alongside the insights the IaCM Remediation Agent produces. The engineer who owns the resource sees the recommendation where the infrastructure code already lives, instead of in a separate cost portal that has to be remembered. Go to Workspace Insights to review what appears on that tab, and to IaCM Remediation Agent to review how those insights are generated.
Cost optimization recommendations
Recommendations arrive from three sources in a single queue: pass-through recommendations from cloud provider advisors such as Azure Advisor and AWS Compute Optimizer, recommendations Harness generates from observed utilization data, and asset governance findings. One queue with one owner replaces checking several native cloud consoles separately.
Each row carries the resource, the reason, and the projected monthly saving. To act on one:
Open the recommendation: Select a row in the queue to review the resource it applies to and the projected saving.
Create the ticket: Create a Jira ticket from the recommendation. The ticket is raised in the owning team's project automatically, so no one has to work out where to file it.
Track the workflow: Open the linked ticket to follow its approval workflow. The ticket status is shown against the recommendation in the queue.
Confirm the saving: When the work is done, the recommendation moves from Open to Applied and the realized saving is recorded against it.
Developers reach the same recommendations without opening a cost portal. Go to Runtime optimization for how a right-sizing recommendation appears at the workspace level.
Next steps
Go to Dashboards to build a custom IaCM dashboard, add visualizations, and schedule report delivery.
Go to Monitoring & Cost Insights to review provisioning activity, workspace status, and cost estimation for a single project.
Go to Cost Estimation to compare current and projected spend before a change is applied.
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