Business Problem Structuring and Case Frameworks Questions

Assumptions
Ingest 10 TB/day (~115 GB/min), varied formats, enrichment via external APIs with rate limits, daily SLA for analytics tables.

Team & FTEs (estimate, 12-week project)

• 2 Data Engineers (platform & ingestion) — 12 wks
• 1 Streaming/Batch Engineer — 12 wks
• 1 Integration Engineer (API retry, auth) — 8 wks
• 1 Data QA / Test Engineer — 10 wks
• 0.5 Product Owner / Analyst — 12 wks
• 0.5 DevOps/SRE — 12 wks

High-level timeline
Weeks 0–2: design, infra provisioning (cloud storage, streaming cluster — managed Kafka/Kinesis + Spark/Flint)
Weeks 3–6: ingestion + schema standardization, build enrichment adapters with caching and rate-limiting
Weeks 7–9: enrichment integration, backpressure handling, retries, idempotence
Weeks 10–11: analytics table transformations, partitioning, performance tuning
Week 12: testing, rollout, runbooks

Key technical considerations

• Use partitioned Parquet/ORC on object storage with compaction
• Stream processing in Spark Structured Streaming or Flink with checkpointing
• API enrichments use async batching + local cache (Redis) to reduce external calls
• Idempotent writes, deduplication using event IDs

Risks & buffers

• API rate limits: build caching & fallback enrichment; assume 2-week delay if issues
• Storage and egress costs: estimate and monitor

This estimate balances speed and robustness; adjust FTEs for parallel work or faster timelines.

RICE framework (Reach * Impact * Confidence / Effort). Use numeric reach where given (convert users to relative units). For comparability, use reach in thousands.

Assumptions: scale reach as users impacted per month (k): A 100k=100, B all analytics users -> assume 200k, C 30k=30, D infra team -> 5k.

Compute scores:

• A) Reach=100, Impact=8, Confidence=0.6, Effort=40 -> Score = (10080.6)/40 = 48/40 = 1.20
• B) Reach=200, Impact=6, Confidence=0.8, Effort=20 -> Score = (20060.8)/20 = 960/20 = 48.0
• C) Reach=30, Impact=7, Confidence=0.5, Effort=15 -> Score = (3070.5)/15 = 105/15 = 7.0
• D) Reach=5, Impact=4, Confidence=0.9, Effort=10 -> Score = (540.9)/10 = 18/10 = 1.8

Ranking by RICE score: 1) B (48.0) 2) C (7.0) 3) D (1.8) 4) A (1.20)

Recommendation: prioritize automated data lineage/catalog (B) first because it unlocks broad analytics productivity and reduces risk; follow with schema-evolution automation (C) to reduce breakages; then cost-optimization (D); finally real-time ingestion (A) as it’s high effort with lower confidence relative to benefit.

Numbers

• Upfront cost = $250,000
• Monthly savings = $30,000
• Monthly revenue uplift = $10,000
• Total monthly benefit = $40,000

Payback period:

• Payback = Upfront / Monthly benefit = 250,000 / 40,000 = 6.25 months (≈ 6 months and 1 week)

3-year ROI (%):

• Total benefits over 36 months = 40,000 * 36 = 1,440,000
• Net benefit = 1,440,000 - 250,000 = 1,190,000
• ROI % = (Net benefit / Upfront) * 100 = (1,190,000 / 250,000) * 100 = 476%

Non-financial factors to include

• Time-to-insight improvements (decision velocity)
• Data quality and trust (reduced errors / incident rate)
• Developer productivity gains (faster feature delivery)
• Scalability & future-readiness (support new products)
• Regulatory/compliance risk reduction
• Employee satisfaction (less toil) and reduced vendor lock-in
• Strategic value: ability to monetize data and accelerate analytics-driven initiatives

Include sensitivity analysis: vary savings/uplift ±20% and show payback/ROI ranges.

Headline recommendation: Accelerate the data platform now to capture time-sensitive personalization benefits; do not defer.

Three supporting bullets:

• Business impact: accelerating yields estimated incremental revenue of ~3–5% over 12 months from improved personalization (projected +$XM), faster A/B test cycles, and reduced data ops costs.
• Risk & mitigation: delivery risk (unknown integrations) can be mitigated by a 6-week discovery, feature-flagged rollouts, and milestone payments; minor increase in near-term spend but faster ROI.
• Operational readiness: hiring two senior data engineers and one SRE reduces timeline by ~40% and keeps 99.99% SLA achievable.

Ask (short): Approve $Y budget for acceleration (covers 3 hires + 3 months cloud burst + contractor), and sign-off to start a 6-week discovery immediately.

Due-diligence checklist (high-level categories with verification steps):

1. Architecture

• Request system diagram and validate data flow end-to-end.
• Verify technology stack versions, dependencies, and third-party services.
• Check multi-tenancy design and deployment pipelines.

2. Data quality

• Review data lineage and transformation logic; run sample lineage queries.
• Inspect production data tests: unit tests, assertions, alerting thresholds.
• Check historical incident logs for common errors and mean time to repair.

3. Compliance

• Verify PII identification and masking, encryption at rest/in transit, and key management.
• Review audit logs, access controls, and policy enforcement (IAM roles).
• Confirm regulatory certifications (e.g., SOC2) and any outstanding compliance issues.

4. Scalability

• Review load tests (peak event rates), capacity planning, and autoscaling policies.
• Examine partitioning strategy and hotspot handling.
• Validate data retention and backfill procedures and recovery time objectives.

5. Maintainability

• Check codebase health: test coverage, CI/CD pipelines, and documentation quality.
• Review on-call rota, runbooks, and incident response processes.
• Evaluate technical debt items and backlog prioritization.

6. Business risks

• Customer concentration, recurring revenue, legal liabilities, SLAs and escalation history.
• Dependence on key personnel; retention plans and knowledge transfer readiness.
• Licensing and IP ownership clarity.

For each item: request evidence, run targeted tests (load, failover, data correctness), and estimate remediation effort and cost.