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Data Lakehouse Platforms
Platform decisions with deep integration complexity, organizational change, and long-term data stakes.
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Inside this journey
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Pre-Discovery
Align the room on outcomes, decision process, and constraints before deeper discovery.
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Stakeholder Alignment
Confirm decision roles, timeline, and each stakeholder’s required success signals (CFO TCO target, CDO governance, VPDE performance metrics).
Alignment Questions
Quick Introductions — Who’s in the Room?
- Who's our primary point of contact for this initiative? Please include name, title, and best contact method.
- Which roles are actively involved in evaluating a new data platform for your organization?
- Roughly how many stakeholders will need to review or approve the final recommendation?
- Which single role will have the final signature authority on the commercial agreement?
- In one word, how would your team describe current accountability for enterprise data (e.g., fragmented, siloed, governed, unclear)?
Why This Now — Is It Money, Risk, or Momentum?
- If a single thing changed overnight — lower cost, airtight governance, or dramatically faster queries — which would create the most immediate business relief?
- Which stakeholder originally raised the pain that started this evaluation?
- Has finance provided a headline metric for the trigger (e.g., ‘cloud data bill up X% YoY’)? If yes, how was it expressed?
- What TCO reduction does the CFO expect within 12 months for a migration to be considered successful?
- Tell us one concrete example where cloud data cost or complexity directly blocked a project or business decision.
What’s Falling Through the Cracks — Governance You Can't See
- How many distinct copies of the same critical dataset do you suspect exist across your analytics and ML estate right now?
- Describe a recent governance or compliance incident (real or near-miss) and the business impact it caused.
- Which governance controls are currently centralized versus implemented separately per tool?
- Who is the functional owner for day-to-day data governance decisions in practice?
- How confident are you that an auditor could reconstruct who accessed specific datasets over the last 12 months?
The Hidden Cost of Slow — Performance Trade-offs You're Accepting
- How many users have quietly accepted slower analytics because 'moving the data' is the only workaround available today?
- Which recurring query patterns consume the most cost or engineering effort in your environment?
- Do you have benchmark queries, SLAs, or representative workloads that we can run against your current warehouse? If so, what format are they in?
- What is the expected interactive latency target for business users that you consider acceptable?
- Which single workload would be the highest-value candidate to migrate first (and why)?
- Share a recent example where performance issues blocked a business decision or created significant rework.
Clock, Board, and Procurement — What’s the Real Decision Timeline?
- If procurement moved twice as fast here, would it change what you prioritize in the evaluation?
- What is your target decision date for selecting a platform?
- Which internal deadlines drive that date (budget cycle, board review, contractual expiry)? Please specify.
- Which procurement or review steps typically create the longest delays here?
- Do incumbent contracts include termination fees, notice periods, or other supplier constraints we should be aware of?
What Will Actually Prove Success — Beyond Slideware
- If the CFO asked for tangible evidence next quarter, which proof would matter most: modelled savings, live benchmarks, or governance artifacts?
- Which quantitative acceptance criteria should we include for the pilot?
- Which financial inputs are mandatory for your TCO model?
- Who will sign off on pilot acceptance from Finance, Data, and Security (name/title for each)?
- How do you want TCO and benchmark progress reported (cadence and owner of the dashboard)?
The Unsaid Dealbreakers — Risk, Politics, and Culture
- What political or organizational fault line could quietly sink this project if not addressed up front?
- Will any team lose budget, perceived importance, or headcount if data consolidation succeeds?
- Are there entrenched vendor preferences or past commitments that restrict architectural choices?
- How tolerant is executive leadership of a temporary regression in performance during migration?
- What assurances (e.g., rollback plan, incremental cutover, pilot guardrails) would reduce stakeholder resistance?
- Have you attempted a similar consolidation before? If yes, what specifically caused it to stall?
Early Commitments — Who Will Own The First Mile?
- If we stood up a tight pilot this month, who on your team will unblock data access, run benchmarks, and be accountable for delivery?
- Which sample datasets can you provide for an initial benchmark and roughly how large are they?
- Can you commit to providing a named security contact and agreed access windows for testing?
- What cadence and format of checkpoints would make stakeholders feel informed and safe during the pilot?
- What would make you comfortable saying 'launch the pilot' this week—what specific assurance, resource, or piece of data do you need?
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Current State Mapping
Document existing data copies, storage and compute costs, recurring expensive query patterns, and governance blind spots.
Current State
Quick Snapshot: Where We Really Stand Right Now
- Approximately how much analytics data do you actively query (select one range)?
- Which systems currently hold your analytics-ready datasets?
- How do you separate dev/staging/production datasets today?
- Which teams most frequently copy datasets into their own environments?
- Who is accountable for the CFO’s cost-reduction mandate and how urgent is it?
Why Is Your Cloud Bill Growing Faster Than Your Business?
- Have you measured what portion of last year’s analytics spend was driven by duplicated datasets versus net data growth?
- Roughly what percentage of your analytics spend is storage vs compute vs data transfer?
- Which of these behaviors most often drives surprise costs in your environment?
- Can you recall a recent unexpected spike in the analytics bill—what triggered it and how long did it last?
- How long have analytics costs been growing faster than user or revenue growth?
Hidden Copies and the Ghosts in Your Data Stack
- When was the last time you did a comprehensive audit to find every active copy of a critical dataset?
- Which three datasets are copied most often and cause the most downstream pain (list names or descriptions)?
- Where do copies of those datasets typically live?
- Do you have an automated inventory and lineage system that tracks dataset copies and transformations?
- On average, how long would it take your team to discover all active copies of a given production dataset?
- What are the main reasons teams create copies instead of accessing a shared source? (Select all that apply)
Who Holds the Keys—and Who Feels Exposed?
- Who would visibly lose autonomy or convenience if all analytics data were consolidated under one governed platform?
- Which stakeholders must sign off on migrating a high-value workload (select all that apply)?
- Which owners are most concerned that consolidation will degrade query performance or break downstream SLAs?
- Share an example of a governance incident tied to copies (PII exposure, audit finding, access lapse) and how it was handled.
- How does your current access request process work and how long does typical access provisioning take?
- If we made unified fine-grained controls available, who would be the day-to-day owner of those policies?
If Governance Had No Blind Spots — What Would Change?
- Imagine every dataset, lineage, and copy were visible and policy-enforced tomorrow—what decisions would you make differently?
- Where are your governance blind spots most damaging today?
- Do you currently have unified, cross-format fine-grained access controls (row/column-level) enforced across analytics and ML workloads?
- How often do governance gaps lead to delayed audits, failed compliance checks, or remediation work?
- Which compliance frameworks or internal policies are most critical for your governance design?
- How do you currently classify and tag sensitive data; what gaps exist in discovery and enforcement?
The Queries That Keep Your Cloud Bill Awake at Night
- If you had to name the single recurring query pattern that drives the biggest cost each month, what is it?
- Which of these recurring query patterns are present in your environment?
- How are these heavy queries typically executed or scheduled?
- When you run benchmark queries today, which platform delivers the best performance-for-cost vs your incumbent?
- What are the performance SLAs that matter for those workloads (median, p95 latency, throughput)?
- Describe one long-running query or dashboard that you’d pick as a benchmark for a migration pilot and why it matters.
Ready to Act? Choosing a Low-Risk, High-Impact Pilot
- If you could move one workload in 90 days to prove cost savings and performance, which workload would that be and why?
- What criteria will you use to select the initial workload for migration?
- Do you have sample datasets and representative benchmark queries available for a pilot?
- Who would form the cross-functional pilot team (list roles or names)?
- What acceptance criteria must the pilot meet to expand (select all that apply)?
- How will you measure and validate cost savings and who signs off on those numbers?
- What timeline, budget, or resource constraints should we plan around for a pilot?
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Outcome Discovery
Define measurable success (≥25% consolidated analytics TCO reduction in 12 months), target workloads for migration, and acceptance criteria for performance and governance.
Discovery Questions
Quick Snapshot — Who's in the Room and What They're Measuring
- Who are the core stakeholders who will sign off on success for this initiative?
- What is your current annual cloud spend on data infrastructure (storage + compute + managed services)?
- Which single business signal kicked this review off?
- Which primary cost metric will your CFO use to judge success?
- How does your team currently demonstrate baseline TCO (e.g., billing exports, internal models, tagging)?
What If 25% Was Non‑Negotiable — Where Would You Push First?
- If the CFO insists on ≥25% consolidated analytics TCO reduction in 12 months, what would that force you to reconsider about your current architecture?
- Which levers do you believe carry the most opportunity to achieve that 25% (select top 3)?
- Which workloads, if migrated first, would most directly impact your monthly bill?
- What internal constraints (political, technical, contractual) would make achieving 25% within 12 months difficult?
- How flexible is your procurement/commercial model to adopt consumption-based pricing tied to benchmarks?
Where the Money Actually Vanishes — Mapping Duplication and Waste
- What patterns do you suspect are driving repeated storage and compute duplication today?
- Approximately how many petabytes (or TB) of data do you estimate are copied across systems for analytics + ML?
- Which team(s) are most frequently creating these copies or extracts?
- How do you currently attribute storage and compute costs back to specific workloads or teams?
- Share a concrete example of a recurring expensive query or job (frequency, runtime, approximate cost) that you'd like to optimize or migrate first.
If Performance Slips, Will People Walk Away?
- What performance trade-offs are unacceptable—even if cost savings are large?
- Relative to your incumbent warehouse, what is your minimum acceptable performance target for representative analytics queries?
- Please list 2–4 benchmark queries or query types we should reproduce for the pilot (e.g., heavy aggregations, high-cardinality joins, ad‑hoc explorations).
- Which performance metrics will be used for acceptance (select all that apply)?
- Who must approve performance results before expanding beyond the initial workload?
Guardrails That Keep Your Data From Becoming a Liability
- Which governance gaps worry you most when data gets copied across systems?
- Do you require unified, fine-grained access controls (row-level, column-level) across all workloads and table formats?
- What specific compliance requirements must we demonstrate during the pilot (select all that apply)?
- Describe the governance acceptance criteria you will use (examples: auditable lineage for migrated datasets, unified ACLs, PII masking confirmed).
- Who owns ongoing governance sign-off once the migration expands beyond the pilot?
Picking the First Battle — Which Workload Should Win the Pilot?
- If you had to pick one high-value workload to migrate first, which single characteristic matters most in your selection?
- List up to three candidate workloads (name, owning team, main datasets) that could be the pilot workload.
- Estimate the current monthly cost (storage + compute) for your top candidate workload.
- What would success look like for this pilot in concrete, measurable terms (e.g., 30% reduction in monthly spend, equal latency at 50% CPU, unified ACLs applied)?
- Who will be the day-to-day owner for migration tasks and who are the SME contacts we need for testing and acceptance?
What Would Success Feel Like to Each Decision‑Maker?
- For the CFO: what concrete evidence (reports, dashboards, cadence) will convince them the TCO target is met?
- For the VP of Data Engineering: beyond cost, what operational changes would signal a win (e.g., fewer ETL jobs, simpler ops, faster onboarding)?
- For the CDO: which governance outcomes must be demonstrable at pilot close (select all that apply)?
- For data scientists and ML teams: what workflow gains would make them adopt the new platform (examples: same-data feature engineering, simpler model training, reduced data prep time)?
- How will we capture and sign off on stakeholder satisfaction (e.g., acceptance checklist, executive review, internal audit)?
Contingencies, Non‑Negotiables, and a Clear Path Forward
- What are the absolute deal-breakers where you would halt the project immediately?
- What rollback criteria should we agree on for the pilot (examples: >X% performance regression, data mismatches, cost increases)?
- Which checkpoints and cadence do you want for monitoring progress toward the 12‑month TCO goal?
- What timeline would make the CFO confident — i.e., by when must we show initial cost wins and when should the 25% consolidated TCO be demonstrable?
- What would you like our next concrete step to be after this discovery (examples: cost/data audit, pilot scoping workshop, benchmark planning session)?
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Solution Experience
Use the customer’s benchmark queries and governance gaps to show the lakehouse future state that delivers the target TCO, unified controls, and native ML workflows.
Experience Meetings
- Solution Experience Kickoff — Diagnose & Align
- Benchmark Performance Walkthrough — Proof of TCO & Performance
- Governance & Access Controls Experience — Proof of Unified Controls
- Native ML Workflow Experience — Proof of Feature Engineering & Consolidation
- Solution Validation & Mutual Commit Prep
- Customer provides a sample training script/notebook and representative feature definitions.
- Confirm that lineage and audit trails provide the necessary evidence for compliance.
- Obtain security stakeholder affirmation or a clear list of remaining policy adjustments.
- Agree next steps to integrate with customer SIEM/GRC tools if required.
- Customer provides representative IAM roles, sensitive dataset list, and compliance requirements.
- Seller produces a policy-mapping document showing role-to-policy translations and lineage examples.
- Schedule a dedicated security POC for SIEM/GRC integration if requested.
- Recap Current ML Data Movement & Costs
- Prove that feature engineering and model training can run without copying data to separate clusters.
- Demonstrate measurable reductions in ML compute and end-to-end time-to-model.
- Confirm governance and lineage for ML artifacts satisfy CDO/ML Ops needs.
- Agree on next steps to incorporate ML workloads into the migration scope.
- Introductions & Objectives
- Seller runs the training job, documents resource usage and cost, and shares the detailed results.
- Both parties update the migration scope to include validated ML workloads for the initial cutover.
- One-sentence Recap: Current-State, Consequence, Future-State
- Stakeholders agree the Solution Experience diagnosis and accept the evidence as proof of the future state.
- Confirm the TCO model demonstrates the path to the CFO's >=25% reduction in 12 months or document gap and mitigation steps.
- Lock acceptance criteria, milestone dates, and responsibilities for the initial migration.
- Obtain agreement to proceed to Mutual Commit drafting and schedule Pre-Deployment Readiness.
- Customer approves or annotates the acceptance criteria and provides decision timeline.
- Seller delivers final TCO model, SOW/MOU draft, and milestone plan for Mutual Commit.
- Both parties schedule the Pre-Deployment Readiness meeting and assign owners for migration tasks.
- Address any outstanding anomalies from benchmarks or governance demos with target remediation plans.
- Customer and seller can each state the current-state in the same one sentence.
- Agree and document the explicit consequences (cost numbers, governance risks) that make this urgent.
- Define the single-sentence future-state and measurable acceptance criteria for the experience.
- Lock the scope: which benchmark queries, datasets, and governance scenarios will be used.
- Customer provides raw benchmark queries, sample datasets, cost baseline report, and governance matrix.
- Seller provisions the evaluation environment and confirms data ingestion & access.
- Both parties finalize the success metrics and sign-off on the scope for the experience.
- Schedule the Benchmark Performance Walkthrough and Governance Experience sessions.
- Recap Agreed Baseline & Targets
- Demonstrate measurable performance improvements and resource efficiency on customer benchmark queries.
- Map measured results to a projected near-term TCO reduction and surface assumptions.
- Identify any deviations or anomalies and agree next steps to resolve them.
- Obtain customer validation that the results prove the defined future-state improvements.
- Seller delivers benchmark runbook, raw logs, and summarized results with cost mapping.
- Customer reviews results, flags any functional or performance anomalies, and provides feedback.
- Seller updates three-year TCO model inputs with measured consumption data.
- If needed, schedule a focused rerun for any failed or outlier queries.
- Recap Identified Governance Blind Spots
- Prove that unified controls cover the same use cases currently causing governance blind spots.
- Demo Feature Engineering on Native Storage
- Consolidated Benchmark & Governance Findings
- Execute/Replay Customer Benchmark Queries
- Customer Current-State (one sentence)
- Live Policy Mapping Using Customer Roles
- Lineage, Catalog, and Audit Log Demonstration
- Consequence: Cost, Risk, and Operational Impact
- Three-Year TCO Model & 12-Month Acceptance Checkpoint
- Resource Consumption & Cost Mapping
- Run Representative Training Job
- Quantify Risk Reduction & Compliance Impact
- Side-by-side Comparison with Incumbent
- Future-State (one sentence) & Success Metrics
- Model Deployment, Monitoring & Governance
- Acceptance Criteria, Decision Gates, and Milestones
- Mutual Commit Path & Next Steps
- Scope for the Experience & Success Criteria
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Solution Scope
Define the migration boundary (initial high-value workload), performance benchmarks, access control changes, ML integration, and three-year TCO modeling inputs.
Scope Configuration
- Migrate one high-cost recurring analytical workload
- Convert warehouse tables to open table format (Delta/Iceberg)
- Consolidate duplicated datasets into single canonical storage
- Deploy warehouse-grade query engine with caching and materialized views
- Implement row- and column-level access controls with audit logging
- Provision streaming ingestion and CDC pipelines with exactly-once semantics
- Deploy native feature store and integrated model training runtimes
- Enable in-lakehouse model serving and online feature joins
- Configure workload isolation and autoscaling for compute
- Activate consumption metering and exportable billing reports
- Implement schema evolution and automatic partition compaction
- Enable collaborative data-science notebooks with direct table access
Scope Questions
Migrate one high-cost recurring analytical workload
- Which recurring analytical workload are you proposing to migrate first (name and brief description)?
- What is the current monthly compute and storage cost for this workload (estimate $)?
- What is the dataset size involved for this workload (GB/TB)?
- How many queries or jobs does this workload run per day?
- Who are the primary owners/stakeholders for this workload?
- What is the acceptable downtime or cutover tolerance for migrating this workload?
Convert warehouse tables to open table format (Delta/Iceberg)
- How many tables do you intend to convert initially?
- What is the total storage footprint of those tables (TB)?
- What table format(s) are you converting from?
- Do you need automatic conversion tooling or a manual conversion plan?
- Are there schema evolution patterns (frequent column adds/drops) we should plan for?
- List any compatibility requirements (e.g., support for Iceberg, Delta, downstream tools that must continue to work).
Consolidate duplicated datasets into single canonical storage
- Which datasets are currently duplicated across environments (list top 3 by cost/size)?
- How many copies of those datasets currently exist (approximate per dataset)?
- Who are the owners of the duplicate copies and what teams depend on them?
- Are there differing retention or compliance requirements across copies that would prevent consolidation?
- What is the expected storage savings target from consolidation (percentage or $)?
- Do any downstream processes require a physically separate copy (e.g., regulatory silo, on-prem appliance)?
Deploy warehouse-grade query engine with caching and materialized views
- What are your current query performance baselines (average/95th percentile latencies) for representative queries?
- What concurrency and user counts should the query engine support?
- Do you require materialized views for frequently run aggregations or dashboards?
- What cache invalidation / freshness window is acceptable for cached results or materialized views?
- Are there specific benchmark queries you want to run during validation (paste sample queries or describe patterns)?
- Which existing query engine or warehouse are we benchmarking against (vendor/version)?
Implement row- and column-level access controls with audit logging
- Do you require row-level access control (RLS), column-level masking, or both?
- How many tables contain sensitive columns that need masking or restricted access?
- Which compliance frameworks must logging and controls satisfy (e.g., HIPAA, SOC2, GDPR)?
- What retention period is required for audit logs (days/months/years)?
- Do you need integration with existing IAM/SSO (Okta, Azure AD, etc.) and role mappings?
- Describe any delegated admin or cross-team access models we must support (e.g., data stewards, auditors).
Provision streaming ingestion and CDC pipelines with exactly-once semantics
- What are the source systems for streaming/CDC (databases, Kafka, Kinesis, cloud pub/sub)?
- What is the expected ingestion volume (events per second) and average message size?
- What end-to-end latency is required for streaming data to be queryable?
- Do you require exactly-once semantics or is at-least-once acceptable?
- Are schema changes expected in the stream and how should they be handled?
- Do you need built-in monitoring, backpressure handling, and alerting for ingestion pipelines?
Deploy native feature store and integrated model training runtimes
- How many ML models and feature sets do you expect to onboard initially?
- Do you require both offline feature pipelines and online feature serving (yes/no)?
- Which ML frameworks and runtimes must be supported for training (e.g., Spark ML, TensorFlow, PyTorch)?
- Are GPUs or other accelerators required for training jobs?
- What are your requirements for feature lineage, versioning, and metadata cataloging?
- Should the feature store integrate with CI/CD for models and support scheduled retraining pipelines?
Enable in-lakehouse model serving and online feature joins
- What latency SLA is required for model inference (ms/seconds)?
- What QPS (queries per second) should the model serving layer support?
- Which model formats/frameworks must be supported for serving (e.g., ONNX, TorchScript)?
- Do online feature joins require sub-100ms join times for production requests?
- Do you need canary/A-B testing and rollout controls for models in production?
- Describe monitoring, drift detection, and logging expectations for model serving.
Configure workload isolation and autoscaling for compute
- Do you want dedicated compute pools per team/project or a shared multi-tenant pool?
- Estimate typical concurrent job counts and peak concurrency for planning autoscale.
- Are preemptible/spot instances acceptable to reduce cost?
- What autoscaling triggers and thresholds would you prefer (CPU, queue depth, latency)?
- Do you require hard resource quotas, soft throttles, or priority-based scheduling?
- Are there compliance or billing reasons to isolate workloads by project or cost center?
Activate consumption metering and exportable billing reports
- Which billing granularity do you need (daily/hourly/monthly)?
- Should metering be grouped by team, project, workload, or tag?
- What export formats are required for finance (CSV, Parquet, API export)?
- Do you need automated alerts when consumption exceeds thresholds?
- Do you require chargeback/showback reporting with allocation rules (describe rules)?
- Who in finance or engineering will own consumption report validation monthly?
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Mutual Commit
Finalize commercial terms, consumption-based pricing, milestones tied to benchmark results and TCO checkpoints, and mutual operational responsibilities.
Agreement Modules
- Master Services Agreement (MSA)
- Statement of Work (SOW)
- Order Form / Quote
- Consumption-Based Pricing Annex
- Milestone & Acceptance Schedule
- Benchmark Acceptance Certificate
- Payment Terms & Invoicing
- Implementation Responsibilities / RACI
- Service Level Agreement (SLA) & Support
- Data Protection & Security Addendum (DPA)
- Governance & Access Controls Agreement
- Change Order & Scope Management
- Termination, Exit & Data Return Plan
- Renewal & Expansion Option
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Deployment
Operationalize rollout with readiness checks, enablement, and outcome validation.
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Pre-Deployment Readiness
Confirm data access, sample datasets for benchmarks, security and IAM mappings, rollback plans, and owners for migration tasks.
Readiness Questions
Quick Check: Who's in the Room?
- To get momentum, who are the primary people we'll work with during pre-deployment (name, role, and best contact)?
- Which of these roles are confirmed decision-makers for go/no-go cutover?
- How confident are you that these stakeholders will be available during the migration window?
- What is your ideal migration window and any hard blackout dates we must avoid?
- Who has final approval authority for emergency rollback decisions?
Are We Leaving Anything Critical Out?
- What data, if lost or temporarily unavailable during migration, would trigger an immediate incident declaration?
- Which datasets contain regulated or sensitive elements (PII, PCI, PHI, GDPR-restricted) and where are they located?
- Do you have a current data classification or sensitivity map we can reference?
- Are there business processes that depend on exact timestamps, CDC, or sub-minute freshness during cutover?
- Who on your team feels most anxious about governance gaps, and what specifically keeps them up at night?
Can We Actually Get to the Data?
- What access hurdle do you secretly assume could derail this project?
- Which of the following best describes how we currently access your analytics data?
- Are there network constraints we must plan for (VPC peering, firewall rules, restricted egress, data transfer limits)?
- How long does it typically take to provision a privileged service account or cross-account role today?
- Can you provide representative sample datasets and the secure delivery path (S3 path, signed URL, staging DB snapshot)? If not, what blocks that?
- Who owns the keys/certificates and what is the process to obtain them?
If We Had To Roll Back Tomorrow, Could We?
- Imagine a midnight rollback—what's the single weakest link in your ability to recover quickly?
- Do you have an existing rollback or backout playbook for similar migrations, and when was it last tested?
- What are the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) targets for the workload we're migrating?
- Which backup/versioning mechanisms are in place (object versioning, snapshots, table time-travel, logical backups)?
- Who must be present and what approvals are required to execute a rollback?
- Have you ever executed a rollback after a data platform change—what happened and what did you learn?
Who Will Move What — and When?
- If two teams disagree mid-migration, who breaks the tie and why?
- Who owns data access provisioning for this project?
- Who owns migration orchestration (scheduling, runbook execution)?
- Who will run and sign off on the performance benchmarks?
- Who has authority to approve cutover and stop the migration if needed?
- Who will monitor post-cutover health and take first action on incidents?
- Do we have documented runbooks and playbooks for each task and where are they stored?
Security & IAM: One Policy or Many?
- If someone says 'we can't trust the new controls' what concrete evidence will convince them otherwise?
- Which identity provider(s) and SSO methods do you use today?
- Do you require fine-grained access controls (column-level, row-level, masking) for the migrated workload?
- Are there entitlement syncs or group mappings we must replicate (AD groups, business roles)? How are they delivered today?
- What audit and compliance evidence will be required post-migration (access logs, query history, data lineage, certification reports)?
- Who in security signs off on IAM changes and what is the typical approval timeline?
Benchmarks & Sample Data: Will They Be Representative?
- If our benchmark passes but real users complain, what’s the likely mismatch we overlooked?
- Do you have a set of benchmark queries or representative workloads we should run, and how will you deliver them?
- What percentage of your monthly query volume should our benchmark sample represent to be realistic?
- What dataset size and cardinality should our benchmark use to reflect production behavior?
- Which performance metrics matter most to you (select top priorities)?
- What acceptance criteria will engineering and finance require to mark the benchmark successful?
What Could Break This Before It Starts?
- Name the single external dependency that would stop the migration if it failed — are we monitoring it?
- Are there upcoming org events (audits, product launches, code freeze) that create blackout windows?
- Are there third-party vendors or external teams whose participation is required?
- What's the typical cadence for change freezes in your environment?
- What contingency budget or escalation path exists if migration testing incurs unexpected cloud spend?
- How would you feel if a critical blocker pushed the migration by two weeks?
Next Steps: Who Signs, When, and With What Expectations?
- If this pilot fails to hit the CFO’s 25% TCO target, what happens next and who owns that decision?
- What are the top milestones you need to see before approving production cutover?
- What is your target date for starting the migration of the initial high-value workload?
- Which financial checkpoints will the CFO require during the 12-month TCO validation?
- Who will be the single point of contact for day-of migration communications and incident triage?
- Are you ready to provision sample data and access for benchmark runs within the target timeline?
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Deployment Enablement
Schedule and execute the migration of the initial workload, run performance benchmarks, enable monitoring, and coordinate cross-team cutover tasks.
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Validation Checklist
Verify performance against baseline, confirm data integrity, validate unified access controls, and record TCO inputs for financial tracking.
Validation Questions
Quick Check: Who’s in the Room?
- Which of the following roles will actively participate in this platform evaluation?
- Who is the single primary decision owner for platform selection and budget sign‑off?
- What deadline has finance set for achieving measurable cost reduction?
- Briefly describe the finance team's primary motivation for this review (e.g., YoY bill growth, audit response, merger synergies, budget cuts).
- Are there any non‑negotiable compliance, residency, or vendor requirements we should know up front?
Why Are We Still Paying Twice for the Same Data?
- How confident are you that duplicate copies across teams—not product growth or increased usage—explain your spike in analytics spend?
- Which teams or functions regularly create full or partial copies of core datasets?
- Estimate the percentage of your analytics storage that exists as duplicated copies across systems.
- Which recurring query patterns generate the highest compute costs for you?
- Tell us about a recent example where duplication caused a tangible problem (cost spike, inconsistent results, failed model, or governance issue): who was impacted and what happened?
- How long has the organization been operating with this level of dataset duplication without a formal consolidation plan?
Where Exactly Is the Money Escaping?
- If you had to point to one line on the cloud bill that's 'waste', would it be storage, compute, data egress, tooling/licensing, or multiple lines?
- Monthly compute spend (approximate)?
- Monthly storage spend (approximate)?
- Monthly data transfer / egress spend (approximate)?
- Which specific datasets or workload categories (name or short descriptor) are the top contributors to storage or compute spend?
- Do you have automated data tiering or archival policies in production, and how effective are they at reducing cost?
Who Owns Governance When Data Lives in Dozens of Places?
- Do you trust that access controls and lineage are accurate and enforceable across every location your copies live?
- Which systems currently enforce your fine‑grained access controls? Select all that apply.
- How do you provision and revoke access across analytics systems today?
- Have you experienced a governance incident (exposed PII, unauthorized access, failed audit) in the last 24 months? If yes, briefly describe impact.
- How long does it typically take to fully revoke a user's access across all analytics platforms?
- How critical is unified, fine‑grained access control across formats to your CDO's agenda?
What Would a Truly Unified Lakehouse Change for You?
- If consolidation onto a lakehouse could guarantee one measurable outcome in 12 months, which would you pick—cost, performance, governance, or ML velocity?
- The CFO's benchmark is ≥25% consolidated analytics TCO reduction in 12 months—do you consider that achievable and acceptable?
- Which initial workload(s) would you consider the highest‑value candidate to migrate first?
- What acceptance criteria should we measure for that workload? (pick up to 3)
- Which three‑year TCO inputs matter most to your finance team for modeling consolidation savings?
- Describe one concrete customer outcome that would convince the CFO and CDO to expand beyond the initial workload.
What Would Day‑1 of Migration Actually Look Like?
- If we agreed to migrate the chosen workload tomorrow, what single operational risk would keep you awake during week one?
- Do you have representative benchmark queries and sample datasets available for testing?
- What sample dataset size would you prefer for initial benchmarks?
- Who will own the day‑to‑day migration tasks (list names or roles and primary contact)?
- What rollback plan or success/failure criteria must be validated before cutover?
- Are there security or IAM mappings that cannot be changed during the migration window? Please list constraints.
Are You Ready to Put Benchmarks, Contracts, and Risk on the Table?
- Would your finance and legal teams accept a consumption‑based commercial commitment tied to benchmark results and TCO checkpoints?
- Which commercial model would you prefer for an initial engagement?
- Which milestones would you require to release payments? (select all that apply)
- Procurement & budget cadence: when could you realistically sign an initial pilot or contract?
- What legal, regulatory, or procurement clauses are non‑negotiable for your organization?
- On a readiness scale from 1–10, how prepared is your organization to begin an initial migration project?
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Success
Review benchmark and financial results, confirm achievement of acceptance criteria (including the CFO’s cost target), and plan workload-by-workload expansion.
Success Reviews
- Benchmark & Financial Results Review
- Acceptance Criteria Confirmation & Risk Review
- Workload-by-Workload Expansion Planning Workshop
- Operational Runbook & Monitoring Alignment
- Executive Steering & Commercial Checkpoint
Issues & Enhancements
- Legal/commercial teams to prepare any contract amendments required for consumption-based expansion milestones.
- Finance to provide incremental TCO tracking template to record realized savings per workload migration.
- Operational Future State (one sentence)
- Ensure monitoring and alerting are configured to detect regressions that would jeopardize acceptance or CFO targets.
- Deliver and validate runbooks so ops teams can execute rollback and remediation without escalation delays.
- Agree on automated cost guardrails and a reporting cadence for finance and engineering stakeholders.
- Enable and share role-based dashboards and alerts with SRE and data engineering teams; schedule a shadow alert exercise.
- Finalize and publish runbooks and incident playbooks into the shared operational runbook repository.
- Configure automated TCO checkpoint reports and cost guardrail policies; verify tags and chargeback mappings.
- Acceptance Snapshot
- Obtain executive confirmation of acceptance outcomes and formal permission to proceed with the agreed workload expansion roadmap.
- Agree commercial terms (consumption model, milestone incentives) to support the expansion and record any amendments.
- Establish a governance cadence for executive reviews of TCO and benchmark progress.
- Generate an executive-one-pager summarizing acceptance, planned workloads, and commercial amendments for signature.
- Re-state Future State (one sentence)
- Schedule monthly executive TCO/benchmark checkpoint meetings for the next six months.
- One-sentence Current State Summary
- Confirm whether benchmark performance and financial results meet the predefined acceptance criteria including the CFO’s cost reduction target.
- Establish root cause and corrective actions if acceptance criteria are not met, with clear ownership and timelines.
- Produce a documented, reproducible record of benchmarks and financial reconciliation for audit and future expansion planning.
- Publish a one-page acceptance summary signed by VPDE, CDO, and CFO indicating pass/fail and any conditional items.
- Deliver reproducible benchmark artifacts (query scripts, sample datasets, logs) and a short validation guide to customer engineering within 48 hours.
- Finance to update the three-year TCO model with actuals and publish variance commentary.
- Formally confirm which acceptance checklist items are satisfied for the initial workload and which require remediation.
- Document operational risks and agreed rollback criteria to enable safe expansion.
- Assign owners and deadlines for any remediation required to declare formal acceptance.
- Create a signed acceptance checklist artifact for the initial workload with attachments of evidence and owner signatures.
- List and prioritize remediation tasks with owners and due dates in the project tracker.
- Publish rollback playbook and ensure runbook owners acknowledge receipt and readiness.
- One-sentence Expansion Objective
- Produce a sequenced roadmap of specific workloads to migrate with expected TCO impact per workload and deadlines.
- Assign owners and create milestone-based accountability for each workload migration.
- Align cross-functional dependencies and finalize a cadence for progress checkpoints and benchmark re-validation.
- Deliver the workload expansion roadmap with scored prioritization and expected TCO deltas for each workload.
- Assign migration owners and schedule the first three workload migrations with dates and acceptance checkpoints.
- Commercial Implications for Expansion
- Monitoring & Alerting Coverage
- Prioritization Criteria Review
- Consequence Recap (Finance)
- Acceptance Checklist Walkthrough
- Evidence Review (Artifacts & Logs)
- Benchmark Methodology & Evidence
- Workload Inventory & Scoring
- Executive Risks & Commitments
- Runbooks & Incident Playbooks
- Per-Workload Acceptance & TCO Delta
- Cost Guardrails & Automation
- Sign-off & Governance Cadence
- Measured Performance vs Baseline
- Operational Risks & Rollback Criteria
- Milestones, Owners & Timeline
- TCO Reconciliation and Variance Analysis
- Operational Handoff Checklist
- Open Issues & Remediation Plan
- Root-cause Discussion for Any Gaps
- Dependencies & Cross-team Coordination
- Decision & Next Steps