This project simulates a Phase 3 FinOps MVP delivery engagement for an enterprise cloud environment. The goal is to demonstrate how a consulting team could deliver persona-based cloud cost dashboards, automated alerting, ServiceNow-aligned workflow concepts, and support documentation to help users understand, adopt, and operate a FinOps capability.
Enterprise cloud teams often struggle with fragmented cost visibility, inconsistent tagging, unclear ownership, delayed cost anomaly response, and disconnected change-management workflows. Finance, engineering, leadership, and cloud operations teams each need different views of cloud spend in order to make decisions and take action.
This project models a FinOps MVP that turns raw cloud cost and usage data into persona-specific dashboards, alerts, and workflow-ready recommendations.
This project is framed as a Phase 3 MVP delivery effort. In this phase, the focus is not only on building dashboards, but also validating requirements with end users, aligning with existing workflows, documenting administration steps, and supporting adoption after delivery.
The MVP architecture models how cloud cost data could flow from provider billing exports into a normalized multi-cloud data layer, then into AWS QuickSight / Quick Suite-style dashboards, alerting logic, and ServiceNow workflow integration.
The MVP uses a simplified normalized cost model that could support AWS, Azure, and Google Cloud cost data. The goal is to standardize cost fields so dashboards and alerts can work across providers.
In a production environment, these views could be implemented in AWS QuickSight or AWS Quick Suite using calculated fields, datasets, filters, and row-level security to provide role-specific access.
$248,750
+8.4% month over month$287,400
Projected 12% over budget$41,200
16.5% of monthly cost$32,800
Idle resources and rightsizingSQL can be used to aggregate cloud cost by service, owner, environment, cost center, and time period. Example use cases include identifying top cost drivers, budget variance, and tagging gaps.
SELECT
billing_month,
cloud_provider,
service_name,
SUM(amortized_cost) AS total_cost
FROM cloud_cost_data
GROUP BY billing_month, cloud_provider, service_name
ORDER BY total_cost DESC;Python could support the MVP by ingesting CSV or API-based cloud cost exports, validating required fields, normalizing provider-specific columns, and preparing clean data for dashboards and alerting.
import pandas as pd
df = pd.read_csv("sample-cost-data.csv")
monthly_cost = (
df.groupby(["billing_month", "cloud_provider", "service_name"])["amortized_cost"]
.sum()
.reset_index()
)
print(monthly_cost)Automated alerts should provide context, not just notifications. A strong FinOps alert should identify what changed, who owns it, the estimated cost impact, severity, and recommended next action.
FinOps alerts and optimization recommendations can be connected to ServiceNow or other change-management tools. For example, a cost anomaly could automatically create a task or change request with the impacted account, service, owner, cost impact, recommended action, and priority.
{
"short_description": "Cloud cost anomaly detected",
"assignment_group": "Cloud Operations",
"cloud_provider": "AWS",
"service": "EC2",
"cost_center": "IT-Cloud",
"severity": "Medium",
"recommendation": "Review EC2 usage spike and validate business need"
}Supporting files were created to demonstrate the technical components behind the FinOps MVP delivery model. These artifacts show the sample cost data, SQL analysis logic, Python ETL concept, and ServiceNow alert payload.
This project simulates a FinOps MVP delivery engagement focused on persona-based dashboarding, cloud cost data modeling, automated alerting, ServiceNow workflow alignment, requirements gathering, documentation, and adoption support for an enterprise FinOps capability.