4. Perform a practical analysis workflow from row review to total estimate
From: Microsoft Azure Cloud Computing Payment Plan Comparison Dataset
Perform a practical analysis workflow from row review to total estimate
Use the Microsoft Azure Cloud Computing Payment Plan Comparison Dataset through a repeatable workflow that combines feature comparison and cost review.
Step 1: Map the workbook
Identify the plan columns and feature categories, then locate the pricing worksheets and the cost-analysis memo. Keep the comparison dimensions and pricing fields separate so that availability findings are not confused with cost findings.
Step 2: Extract comparable rows
For each selected feature, record the Classic value, TGS ELM value, and Recommended value. Note whether the row shows a capability gap, matching availability, or a governance recommendation. Use consistent dimensions such as Audio Capture, Content, Email, Homepages, IMS Configuration, and Role Switch.
Step 3: Review cost drivers
For every pricing line item, record service, region, configuration, and displayed cost. Separate nonzero drivers from rows that show $0.00. Examples of material cost drivers include Azure Storage, Azure SQL Database, Virtual Machines, HDInsight, and Azure DDoS Protection.
Step 4: Verify assumptions and totals
Check region, tier, capacity, hours, redundancy, and other configuration assumptions. Confirm the total and currency. In the retrieved pay-as-you-go estimate, the total is $26,404.31; the related one-year option total is $7,949.84. Treat these as dated summary estimates, not formal quotes.
Step 5: Write the recommendation
Explain the annual difference with quantitative evidence, state which option is preferable for the stated workload, and justify the recommendation. Mention the estimate date and verify current pricing before implementation.
Final checklist
- Map worksheets and comparison columns.
- Extract feature values consistently.
- Identify nonzero cost drivers.
- Validate configuration, region, total, currency, and date.
- Give a quantified recommendation with its assumptions.