Cloud Cost Optimization Dashboard | Business Intelligence & FinOpsd

This project delivers a complete FinOps Cost Intelligence Dashboard analyzing AWS spending for October 2025, built end-to-end in Power BI using FinOps Foundation principles.

It provides:

Cost visibility

• Optimization modeling
• Rightsizing insights
• Tagging health
• Pricing model efficiency
• Daily anomalies & waste signals

The dashboard uses curated DAX measures, KPI engineering, allocation logic, and spend modeling (On-Demand → Savings Plans) to identify cost drivers and expose optimization opportunities.

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Project Overview

This FinOps dashboard uncovers:

• Total AWS spend across services, regions, usage types, and pricing models
• Which workloads drive the highest cost
• Where resources are over-provisioned
• Which services should move to Savings Plans/Reserved
• Daily spend anomalies caused by unusual workloads
• Monthly savings opportunities from tuning compute, storage, and data transfer

The solution is organized into two analytical pages, each with a strategic purpose.

Page 1 - Executive Cost Summary

File: page1_summary.png

This page gives management a high-level understanding of cloud spend, drivers, and patterns.

Key Features

• Total AWS Spend
• Key Features
• Total AWS Spend (Oct 2025)
• Average & Max Daily Cost
• Active Services
• Active Regions
• Production Cost %
• Average Cost per Service
• Daily Spend Trend
• Cost by Pricing Model
• Cost by Environment (Prod/Stage/Dev)
• Top Services by spend
• Top Regions by spend
• Cost by UsageType

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Page 2 - Optimization Insights

File: page2_insights.png

This page transforms raw billing data into actionable optimization strategies.

Optimization KPIs

• Estimated Savings (On-Demand → Savings Plans): $10.9K
• Rightsizing Opportunity: 22%
• Daily Cost StdDev: $518.30
• Monthly Spend: $50.12K

Cost Driver Deep-Dives

• Cost Drivers by Service
• Spend by ServiceName
• Spend by Region
• Spend by UsageType
• Spend by Tag — Environment
• Spend by Pricing Model

##Waste & Anomaly Detection

• Daily spend anomalies (spikes > 2× baseline)
• Staging/Development environments showing unnecessary cost
• Over-provisioned compute/storage services

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Key DAX Measures Used

Total AWS Cost = SUM('aws_billing_daily_oct2025'[TotalCost])

Avg Daily Cost = 
AVERAGEX(
    VALUES('aws_billing_daily_oct2025'[Date]),
    CALCULATE(SUM('aws_billing_daily_oct2025'[TotalCost]))
)

Max Daily Cost = 
MAXX(
    VALUES('aws_billing_daily_oct2025'[Date]),
    CALCULATE(SUM('aws_billing_daily_oct2025'[TotalCost]))
)

Active Services = DISTINCTCOUNT('aws_billing_daily_oct2025'[ServiceName])

Regions Used = DISTINCTCOUNT('aws_billing_daily_oct2025'[Region])

Production Cost % =
DIVIDE(
    CALCULATE(SUM('aws_billing_daily_oct2025'[TotalCost]), 'aws_billing_daily_oct2025'[Tag_Environment] = "Production"),
    SUM('aws_billing_daily_oct2025'[TotalCost])
)

Avg Cost per Service =
DIVIDE([Total AWS Cost], [Active Services])

Estimated Savings (OnDemand_SP) =
[Total AWS Cost] * 0.30   -- Assuming 30% savings potential

Rightsizing % =
DIVIDE([Estimated Savings (OnDemand_SP)], [Total AWS Cost])

Daily Cost StdDev =
STDEVX.S(
    VALUES('aws_billing_daily_oct2025'[Date]),
    CALCULATE(SUM('aws_billing_daily_oct2025'[TotalCost]))
)

Insights Summary (Business + Technical)

1.Major Cost Drivers

• Highest spend from AWS Glue, Redshift, RDS, and EC2
• Primary region: US-East-1
• Heavy usage types: TimedStorage-GB, DataTransfer-Out, Requeststs
• 72% of spend is On-Demand → strong optimization opportunity

2.Optimization Opportunities

• Converting On-Demand to Savings Plans yields ~30% savings
• Rightsize low-utilized compute/storage resources
• Consolidate workloads across regions
• Reduce large DataTransfer-Out workloads causing hidden cost spikes

3.Anomaly Detection

• Several daily spikes >2× above baseline

• Likely sources: ETL pipeline inefficiencies, temporary workloads, unplanned data transfer

4.Estimated Savings

~$10.9K/month in potential savings through:

~Savings Plans

~Rightsizing

~Eliminating waste in Staging/Dev

~Reducing data-transfer inefficiencies

Skills Demonstrated

• Built a multi-page Power BI dashboard with professional KPI layout and UX design
• Engineered custom DAX measures (AVERAGEX, MAXX, DISTINCTCOUNT, STDEVX.S)
• Applied FinOps best practices: savings modeling, rightsizing, tagging, anomaly detection
• Identified cost drivers across services, regions, pricing models
• Modeled savings from On-Demand - Savings Plans transitions
• Interpreted AWS usage patterns (storage, compute, requests, data transfer)
• Delivered a complete BI - FinOps - Optimization lifecycle
• Packaged the project into a structured, professional GitHub repository

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👨‍💻 Author

Tinevimbo Mukodza MS Information Systems — Pace University FinOps & Business Intelligence Analytics Portfolio GitHub: https://github.com/tinemukodza/finops-analytics-portfolio