Business Problem Structuring and Case Frameworks Questions

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.

Approach Overview
Use a scoring-based portfolio prioritization combining business value, effort, risk, and dependencies.

Selection criteria (weighted)

• Business value (40%): revenue uplift, cost savings, strategic importance
• Effort (20%): FTE-weeks, infra changes
• Risk (15%): data sensitivity, operational risk
• Time-to-value (15%): how fast it returns value
• Dependency complexity (10%): upstream/downstream blockers

Scoring method
Score each pipeline 1–5 on each criterion, multiply by weight, sum to get priority score. Normalize and rank.

Dependency handling

• Map dependencies and form DAG. Identify prerequisite pipelines and shared infra components.
• Use dependency-adjusted score: add penalty for unresolved blockers; add bonus if building a pipeline creates reusable assets.

Sample 3-pipeline rollout (first 3 builds)

1. Pipeline A (Shared ingestion + schema registry) — Rationale: high reusability; enables others; moderate effort. Score high due to platform leverage.
2. Pipeline C (High revenue analytics) — Rationale: immediate business value and short time-to-value; depends on A for ingestion.
3. Pipeline B (Risk/compliance reporting) — Rationale: regulatory urgency; moderate effort; uses A and builds monitoring components.

This plan builds platform capabilities first (reduces incremental cost), then high-value use-cases, while respecting dependencies.

Assumptions & given:

• Transactions/day = 100,000
• Avg order = $40
• Data error rate = 0.5% = 0.005
• 60% of affected transactions misrouted/billed incorrectly
• 50% of those are lost revenue
• Year = 365 days

Calculations:

• Affected transactions/day = 100,000 * 0.005 = 500
• Misrouted/billed/day = 500 * 0.6 = 300
• Lost revenue transactions/day = 300 * 0.5 = 150
• Lost revenue/day = 150 * $40 = $6,000
• Annual lost revenue = $6,000 * 365 = $2,190,000

If error rate halved (0.25%):

• New affected/day = 100,000 * 0.0025 = 250
• Misrouted/day = 250 * 0.6 = 150
• Lost/day = 150 * 0.5 = 75
• Lost revenue/day = 75 * $40 = $3,000
• Annual lost revenue = $3,000 * 365 = $1,095,000

Potential annual savings:

• Savings = $2,190,000 - $1,095,000 = $1,095,000

Notes / sensitivity:

• Excludes downstream costs (support, chargebacks, reputation). If include 20% operational cost on lost revenue, savings higher.
• Use this to justify investment in data quality tools, monitoring, and automated validation.

End-to-end plan to cut apparel returns by 10%

Data sources

• Order history, SKU/catalog, product attributes (size, fabric), product images
• Customer attributes and past returns
• Clickstream (size charts viewed, session duration)
• Post-purchase feedback and return reasons, warehouse return scans

Analytic models / experiments

• Size recommendation model (collaborative + content-based) to suggest best size
• Return propensity model to flag high-risk orders for interventions
• Visual similarity model for “expected look” to reduce mismatch
• A/B experiments: show size recommendation vs control; free size guide vs detailed fit chart

Pipeline changes

• Ingest clickstream and product metadata in near-real-time
• Feature store for size/history features; nightly batch training
• API to surface recommendations at checkout; webhook to alert fulfillment

Quick wins (short-term)

• Improve product page with clearer size charts and highlight fit notes from reviews
• Post-purchase emails with tips to reduce returns (fit guidance)
• Manual review for SKUs with highest return rates

Metrics to measure impact

• Overall return rate (target: 10% relative reduction)
• Return rate by SKU and cohort
• Conversion and AOV (ensure no negative impact)
• Model precision/recall for return propensity
• Uplift in correct-size selections and reduction in size-related returns

Phased rollout: start with top 10 SKUs (highest volume/returns), run A/B for 8 weeks, iterate, then expand.

Compare commercial models:

Fixed-price:

• Risk: vendor bears scope risk; buyer gets price certainty.
• Incentives: vendor may under-deliver or pad scope; careful SOW needed.
• Quality: can be high if incentives include acceptance criteria; risk of adversarial change requests.

Time-and-Materials (T&M):

• Risk: buyer bears cost overruns; flexible for unclear scope.
• Incentives: vendor has less incentive to be efficient unless governance exists.
• Quality: good for exploration; needs tight change control and periodic reviews.

Milestone-based hybrid:

• Risk: shared; ties payments to outcomes.
• Incentives: aligns delivery to milestones while allowing flexibility.
• Quality: better when milestones include acceptance tests and success metrics.

Recommended contract: Milestone-based hybrid with capped T&M overrun + shared risk/reward.

• Structure: discovery fixed-fee (2–4 weeks) to de-risk; delivery split into 3–4 milestones (architecture, MVP, scale), each with acceptance criteria and payment. Include a capped T&M buffer for unknowns (e.g., 15% of estimated cost), plus performance bonus if delivered early or under budget and penalties for missed critical milestones.

Justification: Discovery reduces uncertainty; milestone payments align incentives and quality; capped T&M protects buyer from runaway costs while keeping flexibility; bonuses motivate efficiency.