Cost Optimization and Technology Financial Management Questions

Framework / approach:

• Phase 0: Discover (1 month) — inventory, baseline cost, usage patterns by workspace/dataset/query.
• Phase 1: Diagnose (1 month) — identify waste across compute, storage, query inefficiencies.
• Phase 2: Remediate & Automate (months 3–9) — implement playbook items.
• Phase 3: Govern & Measure (months 9–12) — embed policies, measure savings.

Identify waste (examples & methods):

• Compute: list expensive clusters/VMs by dollar/hour and utilization (CPU, memory). Flag idle or low-utilization compute used >4 hours/day.
• Storage: find large/old tables, unpartitioned raw dumps, duplicate datasets; calculate hot vs cold access %.
• Query inefficiencies: capture top-cost queries (by execution time & bytes scanned), look for full-table scans, missing partitions, unnecessary SELECT *.

Playbook to reduce costs:

• Scheduling: auto-stop non-prod clusters at off-hours; schedule heavy ETL for off-peak; use job queues and concurrency limits.
• Caching & materialization: cache results for heavy dashboard queries; build aggregated materialized views refreshed nightly; use BI extracts for frequent reports.
• Right-sizing: downsize instance types for low-utilization jobs; use autoscaling policies and spot/preemptible VMs for batch.
• Storage tiering & lifecycle: move cold data to cheaper tiers, compress/parquet, enforce TTL on staging data.
• Query tuning: add partitions, predicate pushdown, selective projections, limit joins, push heavy transforms to pipelines rather than ad-hoc dashboards.
• Cost controls: budget alerts, query cost caps per user/group.

Stakeholder governance:

• Governance board (monthly): Data Analyst rep, Data Engineer, Finance, Product owner — approve big changes, review anomalies.
• User policies: tagging requirement (owner, project), chargeback showback dashboard.
• Onboarding: mandatory cost-awareness training, query best-practice checklist.
• Change process: low-risk automations via RFC; high-impact sizing changes require board sign-off.

Target metrics (12-month roadmap):

• Month 0 baseline.
• 3 months: 10% reduction via scheduling + stopping idle compute.
• 6 months: 25% reduction adding right-sizing + storage tiering.
• 12 months: 35–45% reduction combining caching, query tuning, and governance (example target: reduce monthly analytics spend from $100k to $55–65k).
• KPIs to track weekly: $/workspace, cost per dashboard view, bytes scanned per query, % idle compute hours.

How I’d operate as Data Analyst:

• Provide the cost/usage analyses and prioritized list of expensive queries/tables.
• Work with Data Engineers to implement materialized views and scheduling.
• Build and maintain the cost dashboard and run monthly savings reviews with stakeholders.