Business Problem Structuring and Case Frameworks Questions

Migration Phases (6 months)

1. Discovery & planning (Weeks 0–4): inventory data, jobs, schemas, SLAs, stakeholders, cost model.
2. Proof-of-concept (Weeks 4–8): migrate subset (10 TB) to validate tooling (Snowflake/Databricks + S3/GCS), test performance.
3. Parallel ingestion & refactor (Months 2–4): run dual pipelines (on-prem and cloud) for new data; convert ETL to cloud-native (Spark, serverless).
4. Bulk data transfer (Months 4–5): perform staged lifts using offline transfer (Snowball / Transfer Appliance) + network-accelerated sync.
5. Cutover (Month 5–6): incremental sync, freeze window, validate, switch consumers.
6. Decommission (Month 6): retire on-prem after confirmation.

Cutover strategies

• Blue-green: keep both environments live; route a subset of consumers to cloud first.
• Phased per domain: cutover non-critical domains first.
• Full freeze: short maintenance window for final sync.

Key risks & mitigations

• Technical: performance regressions — mitigate via POC benchmarking, profiling, tuning.
• Business: data latency/availability impacts — use dual-run and rollback capability.
• Cost: egress and storage spikes — model costs early, compression, tiering.

Rollback plans

• Maintain read-only copy in on-prem for 30 days post-cutover.
• Keep finalized job definitions and orchestration to switch back within SLA.

Success metrics

• Data completeness (% records validated)
• Job success rate and mean time to recovery
• Query latency and throughput parity compared to baseline
• Cost per TB/month and overall TCO trend
• Number of consumer incidents post-cutover

Deliver regular checkpoints, automated validation, and stakeholder sign-offs at each phase.

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.

Framework: 4-step repeatable approach

1. Clarify objective & constraints

• Ask for target metric (margin improvement), timeline, data availability.

2. Build MECE issue tree

• Revenue side: price, mix, promotions, customer segments.
• Cost side: COGS, logistics, procurement, shrinkage.
• Operational: data quality, reporting lag.

3. Hypothesis-driven prioritization

• Form 2–3 high-impact hypotheses (e.g., cut out low-margin SKUs, optimize price by segment).
• Prioritize by impact × confidence.

4. Quick sizing & tests

• Front-of-envelope: use available public benchmarks or sample data to estimate potential uplift.
• Run lightweight experiments: A/B pricing, targeted promotions, supplier renegotiation pilots.

Tactical tips:

• Use data slices that are mutually exclusive to stay MECE.
• Always attach a measurable test/metric for each hypothesis (lift in gross margin %, win-rate).
• Keep analyses consumable: top 3 drivers, recommended quick wins, longer-term analytics roadmap.

Situation: Mid-project, client requested expanding scope to include additional data sources that would double effort.

Task: Renegotiate scope, timeline, and pricing to keep delivery achievable.

Action:

• Assessed impact: I mapped new data sources, estimated effort (FTE-weeks), infra and QA cost, and risks.
• Proposed options: (A) phased approach — deliver original scope on time, add new sources in Phase 2 with separate budget; (B) expanded scope with extended timeline and delta cost.
• Presented a clear change order: new deliverables, acceptance criteria, timeline, and updated cost.
• Negotiated: suggested delivering high-value subset of new sources in current sprint for smaller incremental fee; rest deferred.
• Documented: formal change request (CR) signed by client, updated JIRA scope, and contract amendment.

Result: Client approved phased approach; core milestones met on original timeline. Additional sources delivered in Phase 2 with agreed budget. Outcome preserved trust and avoided scope creep.

Learning: Quantify impact quickly, present clear choices, and document decisions to keep client aligned.

Goal: Turn internal analytics into a revenue-generating data product in 12 months.

Months 0–2: Market research

• Interview 20 target customers, analyze competitors, define value hypotheses and willing-to-pay ranges. Produce TAM/SAM.

Months 2–4: Product definition & pricing

• Define personas, core use-cases, KPI uplift claims. Choose pricing: freemium metered + tiered feature/volume pricing. Validate with 3 pilots.

Months 4–7: MVP build

• Scope: curated datasets + dashboards, API access, CSV export, basic alerts. Deliver multi-tenant access, onboarding flow, docs.

Months 7–9: Pilot & iterate

• 3 paying pilot customers, collect usage, SLA, ROI evidence. Harden security/compliance.

Months 9–12: GTM & scale

• Launch: marketing playbook, sales enablement, pricing finalization, support SLAs. Drive first 10 customers.

Data engineering investments:

• Multi-tenant ingestion pipelines, data productization (cleaned, documented schemas), RBAC & metering, API layer, observability, costs: initial investment in ETL orchestration, data catalog, feature store, costed for scale.

Milestones & metrics: pilot NPS, time-to-value, ARR by month 12 target. Iteratively expand features based on paid adoption.