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Data Platform Implementation
Advisory, implementation, and operational engagements where trust, alignment, and execution governance determine outcomes.
Slalom
Avanade
Capgemini
Thoughtworks
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 executive goals, decision roles, timeline, and adoption success metrics for the data platform initiative.
Alignment Questions
Why now? Tell us the moment that pushed this to the top of the table
- What single event, meeting, or board outcome triggered this initiative?
- Which executive or committee raised the concern most loudly?
- How urgent does leadership consider a fix (how soon must this show progress)?
- Can you briefly describe the most recent example when conflicting numbers caused a material business decision or reputational cost?
- If you had to pick one emotional reaction executives felt after that episode, which was it?
Who really signs the paper — and who silently blocks progress?
- If we built a flawless platform tomorrow, who in your organization would still say 'not yet' — and why?
- Please identify everyone who must formally approve this project (titles or names).
- Who is the executive sponsor who will drive cross-functional decisions day-to-day?
- Is there a formal RACI or approval workflow today for analytics initiatives?
- When approvals are needed, what is the typical calendar timeline (weeks/months) and bottleneck owner?
What would make executives stop arguing and start deciding?
- Which 3–5 KPIs would signal to your execs that the platform is reliable and should be trusted?
- Which metric(s) today spark the most debate between departments? Tell us a concrete example.
- What tolerance for data error or staleness would your execs accept for those KPIs?
- How are metric definitions validated today (single source of truth, email threads, spreadsheets, tribal knowledge, other)?
- What evidence or artifacts would convince executives to accept a metric as authoritative (auditable lineage, reconciliations, SLA dashboards, signed-off definitions)?
Who will actually use this — and who will refuse to trust it?
- Which teams and roles will be daily consumers of the platform's outputs?
- Roughly how many active users do you expect in the pilot (ranges are fine)?
- Who are the potential champions (names/roles) who will adopt and promote the platform internally?
- Which groups are most likely to distrust or resist the platform initially, and what are their reasons?
- Who will own enablement, training, and change management for pilot users?
- Which learning formats will drive adoption fastest for your teams?
If this pilot doesn't win them over, what happens next?
- If executives remain unconvinced after the pilot, what are the most likely organizational consequences?
- Is there a firm decision or review date already scheduled to evaluate pilot success?
- How sensitive is future funding to early wins (e.g., must show ROI within quarter to retain budget)?
- If the pilot fails, do you have a fallback or contingency plan? Describe.
- Which stakeholder(s) would you most worry about losing if the pilot underdelivers?
What has to be true on day one for us to confidently call the pilot a win?
- List the non-negotiable acceptance criteria for the pilot (specific KPIs, datasets, reconciliation checks, user access, etc.).
- Which data domains must be included in the pilot (pick all that apply)?
- What SLA targets for freshness, completeness, and accuracy should the pilot meet?
- Which checks or alerts must exist before handing dashboards to executives (reconciliation reports, pipeline failure alerts, data quality thresholds)?
- Who will formally sign the pilot acceptance certificate when criteria are met?
Which quiet organizational obstacles will sabotage adoption if left unchecked?
- Where does governance currently break down (ownership, definitions, access controls, change control)?
- Do you have named data stewards or owners for the domains in scope?
- What internal team capacity exists to operate, extend, and monitor pipelines after our engagement?
- Are there current governance or political hot spots we should be aware of (specific departments, legacy owners, or conflicting incentives)?
- Are there technical constraints (cloud provider lock-in, security policies, regulatory constraints) that could limit design choices?
How will you lock in success — not just prove it for a moment?
- What handoff model do you prefer after the pilot (full internal ownership, co-managed, managed services, or phased transfer)?
- Which concrete metrics will you track to prove sustained adoption (active users, dashboard refreshes, decision reliance, time-to-insight)?
- How do you plan to communicate pilot wins and learning to executives and business teams?
- What ongoing support or SLAs will you expect from us post-pilot (hours, response time, runbook ownership, training refreshes)?
- What would a meaningful celebration look like once executives accept the platform, and who should be recognized?
- What's one small governance or communication change we could make immediately that would materially increase your chance of success?
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Current State Mapping
Document sources, reporting gaps, pipeline failure modes, and existing trust issues that block reliable analytics.
Current State
Quick Snapshot — Where We Start
- Which statement best describes why you're asking us to map your current state now?
- List the primary data sources, systems, and storage locations we should know about (databases, apps, data lakes, ETL tools, file shares).
- Which BI and reporting tools are in production today (select all that apply)?
- Approximately how many distinct reports or dashboards are actively used by the business today?
- Who will be our central business and technical contacts for discovery and validation (names, roles, and availability)?
If Your Reports Could Talk, What Would They Say?
- When two leaders present different numbers in a meeting, whose figure tends to carry credibility — and why do people accept it?
- Which specific metrics consistently show conflicting values across reports (pick the ones you see most often)?
- Describe a recent example when conflicting reports caused a rework, delay, or bad decision. What was the impact?
- How do teams reconcile differences today when two reports disagree?
- How frequently are discrepancies surfaced during executive reviews or board meetings?
Where Data Breaks — Pipeline Failure Modes We Don’t See Coming
- Which pipeline failure mode would you most fear happening undetected: silent drift, missing partitions, schema changes, late loads, or bad joins?
- How do you currently detect pipeline problems: automated monitoring, user reports, runbook checks, or not at all?
- Typical time to detect a failure (from occurrence to awareness):
- Typical time to resolve a failure once detected:
- Who is primarily responsible for incident response and recovery for ETL/ELT failures?
- What recurring root causes do you see most often when failures repeat? (list up to three)
Trust — How People Really Feel About Your Numbers
- If the executive team rated trust in your data 1–10, where would you be placed and what specific issues pull down the score?
- Which groups express the most skepticism about analytics outputs (select all that apply)?
- Have you intentionally retired dashboards or reports because users stopped trusting them? Tell us which and why.
- Which signals would convince a skeptical user that a metric is trustworthy (select all that apply)?
- How are data definitions, lineage, and owner information currently documented and surfaced to users?
Hidden Ownership — Who's Responsible vs Who Actually Fixes It
- When a dashboard is wrong, who do people call first — and are they empowered to resolve the underlying data issue?
- Which model best describes your organizational ownership of data and analytics?
- How often do ownership handoffs (build → run) fail because run teams lack knowledge or access?
- What onboarding, runbooks, or documentation exists to help someone take over a dataset or pipeline? (links or summary)
- What permissions or organizational blockers prevent owners from fixing issues quickly (e.g., environment access, lack of tooling, budget approvals)?
The Hidden Cost — Where Time and Money Leak
- How many analyst or engineer hours per week are estimated to be spent on firefighting data issues rather than proactive analytics?
- Share a concrete example where poor data quality or late data caused financial impact, missed opportunity, or strategic delay.
- Which teams currently spend the most time reconciling or manually transforming data (select up to three)?
- Do you currently track or attribute cloud / processing costs and engineering time to specific data incidents or duplicate reporting work?
- If firefighting time were cut in half next quarter, what three things would your teams prioritize instead?
What Must Be Preserved — Non-Negotiables and Risk Boundaries
- If we make changes to your platform during a pilot, what non-negotiable constraints or risks must we avoid at all costs?
- Are there regulatory, audit, or compliance requirements that rely on your current reports or pipelines?
- What SLAs for data freshness and incident response are required by your stakeholders (e.g., minutes, hourly, daily)?
- Which legacy integrations, reports, or processes cannot be changed during the pilot? Please list and explain why.
- What concrete success signals must we hit during discovery/pilot to prove we haven't introduced regressions or new risk?
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Outcome Discovery
Define the pilot use case, measurable KPIs, acceptance criteria, and what must be true for the platform to be adopted.
Discovery Questions
Setting the Table: What Brought You Here Today
- What's the single most urgent business question you hope a pilot data platform will answer?
- Who asked for this pilot and why now—an executive directive, a board issue, a new hire, or a migration?
- Which stakeholders must see early wins for this pilot to keep momentum?
- How would you describe the internal feeling about past data projects—excited, skeptical, burned out, or indifferent?
- What is your target timeline to show pilot results to decision-makers (weeks/months)?
- Who will be the primary sponsor and who will be the day-to-day owner for this pilot?
If This Pilot Fails, Who Pays the Price?
- What typically happens when two departments present conflicting numbers—do executives pick one, delay decisions, or launch investigations?
- How often in the past 12 months have decisions been delayed or reversed due to data disputes?
- Tell me about a time a data discrepancy caused real business impact—what happened and what was lost?
- How long has the organization tolerated ad-hoc fixes or spreadsheets instead of a governed pipeline?
- If the pilot doesn’t change trust in your numbers, what will likely be the next steps from leadership?
- On a scale from 1–10, how damaging would continued distrust in analytics be to the company’s strategy?
The One Metric That Would Make Everyone Stop Arguing
- If you had to pick a single pilot KPI that proves the platform works, what is it? (Name the metric, not a theme)
- Why would that metric convince executives—revenue accuracy, time-to-insight, reduced manual reconciliation, regulatory compliance, or something else?
- What is the current baseline for that metric and how will we measure improvement?
- What minimum improvement would you need to see to call the pilot a success for stakeholders?
- Which systems contain the canonical values for this metric today (ERP, CRM, ad-hoc spreadsheets, data lake, other)?
- What frequency and latency are acceptable for that metric—near real-time, hourly, daily, weekly?
What Would Adoption Actually Feel Like—Beyond the PowerPoint
- Imagine three months after pilot completion—what behaviors would show the platform is genuinely adopted?
- Who will use the pilot outputs day-to-day and what decisions will they change because of them?
- What training, documentation, or embedded guidance would make business users trust and prefer the new metrics?
- What would cause people to revert to spreadsheets after the pilot—lack of trust, missing fields, slow performance, or tooling complexity?
- Who needs to sign off that users are adopting the pilot (names, roles), and what evidence will they accept?
Boundaries and Failures: What's In Scope and What Breaks the Pilot
- If you had to define a ruthless but achievable pilot scope today, what data domains and business processes would you include?
- Which specific source systems must be ingested for the pilot to work, and which systems are explicitly out-of-scope?
- What sample size or coverage do you expect for the pilot (e.g., last 12 months of transactions, a single region, top 10 customers)?
- How will we define a failure for the pilot—unreliable metric, missed timeline, inability to onboard users, or unacceptable data latency?
- If a source is low quality, what remediation are you willing to accept during the pilot—data cleansing, business rules, or source fixes?
- What rollback or stop-gate conditions should trigger a re-scope or cancellation?
Trust and Alerting: How Will Someone Know the Numbers Are Safe?
- What would make a finance leader wake up at 2am and trust that reported revenue numbers are correct?
- What existing monitoring, lineage, or reconciliation checks do you have today (if any)?
- Which data quality dimensions matter most for your pilot (completeness, accuracy, freshness, uniqueness, timeliness)?
- Who should receive data quality alerts and what is the expected response time for investigated incidents?
- What level of false positives in alerting is acceptable during pilot ramp (high, moderate, low)?
- How should anomaly context be presented so non-technical users can decide whether to act?
People, Power, and Playbooks: Who Keeps This Running Long-Term?
- After the engagement ends, who will be accountable for operating and evolving the pipelines and models?
- What gaps in skills or headcount worry you most about sustaining the platform?
- What handoff artifacts would make you confident the internal team can operate the pilot (runbooks, run frequency, onboarding guides, code comments)?
- What is an acceptable ramp-to-autonomy period for your team—weeks or months after pilot end?
- Who will be the escalation contact when pipelines break after hours?
- How would you like ownership of schema changes, metric updates, and dashboard edits to be governed?
Commitments That Unlock the First Build
- What internal approvals, budgets, or legal reviews must be completed before we begin ingesting production data?
- What is non-negotiable to you in the pilot contract—time-to-value, fixed scope, price cap, or an exit clause?
- Which milestone would make leadership comfortable to sign off and expand the project (e.g., validated KPI, user adoption threshold, zero critical alerts)?
- What are the biggest internal blockers you expect when we ask for access, credentials, or sample datasets?
- If we propose a 6–8 week pilot delivery with defined acceptance tests, what would make you say yes today?
- Who should be invited to a one-hour kickoff to lock scope, success criteria, and access?
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Solution Experience
Anchor the proposed architecture and operating model to the customer’s pilot scenario, showing how trusted metrics and monitoring prevent conflicting executive numbers.
Experience Meetings
- Solution Experience Kickoff — Current State, Consequence, Future State
- Pilot Architecture Walkthrough — End-to-End Data Flow & Mapping
- Monitoring & Trusted Metrics Workshop — Alerts, Reconciliation, and Runbooks
- Validation, Acceptance & Operating Model — Sign-off and Handover Decisions
- Schedule the executive review meeting to present pilot readiness and expected outcomes.
- Estimate implementation effort and propose sprint breakdown for pilot components.
- Recap Divergence Scenarios to Prevent
- Agree the monitoring signals, reconciliation checks, and alert severities required to prevent executive conflicts.
- Finalize the runbook ownership and SLA for resolving metric divergence incidents.
- Validate reconciliation queries and thresholds against sample data to ensure practical detection.
- Engineering to produce sample monitoring dashboards and configure three prototype alerts (freshness, row-count delta, schema change).
- Create and share reconciliation SQL templates and a sample result set comparing golden metric and departmental extracts.
- Assign runbook owners and document the escalation path with SLAs for each alert severity.
- Agree on threshold values for acceptance tests and add them to the acceptance checklist.
- Review Pilot Deliverables & Acceptance Checklist
- Finalize and sign off the acceptance checklist and test execution plan.
- Assign clear operating model roles and confirm owners for runbooks and monitoring.
- Agree on post-pilot handoff materials, training, and support terms.
- Schedule the pilot kick-off sprint and executive review date.
- Produce the written acceptance checklist with evidence requirements and circulate for signatures.
- Assign and record pipeline, metric steward, and monitoring owners with contact details.
- Deliver runbook and training plan for the internal team prior to pilot completion.
- Introductions & Meeting Objectives
- Obtain agreement on a single-sentence current state describing what is breaking today.
- Surface and quantify the business consequence of the problem so urgency is explicit.
- Agree the target future-state sentence and the pilot KPIs and acceptance criteria.
- Identify required pre-work (data extracts, lineage docs, stakeholder list) and assign owners.
- Customer to confirm and provide evidence for the one-sentence current state (incidents, dashboard screenshots, data extracts).
- Customer to provide quantified consequence metrics (estimated revenue impact, hours lost, number of conflicting reports) with owner assigned.
- Jointly finalize the pilot KPIs and acceptance criteria document and circulate for sign-off.
- Gather and share sample source extracts and lineage artifacts for the pilot (deadline and owner).
- Recap Preconditions
- Validate the proposed end-to-end architecture is anchored to the pilot and addresses the specific divergence points.
- Agree the canonical metric definition and the transformations required to produce it.
- Identify source fixes, owners, and any required sample-data remediation before build.
- Confirm modeling approach and governance handoffs for metric stewardship.
- Engineering to deliver a one-page annotated data flow diagram and transformation spec for the pilot metric.
- Customer data stewards to provide missing lineage details or approve required source changes.
- Agree and document canonical metric SQL (or pseudo-SQL) and edge-case rules for the metric steward to review.
- Monitoring Architecture & Telemetry Points
- One-sentence Current State Readback
- End-to-End Pilot Data Flow
- Acceptance Test Execution Plan
- Reconciliation Patterns & Deterministic Checks
- Operating Model & Role Assignments
- Surface & Quantify Consequences
- Lineage & Transformation Mapping (Where discrepancies occur)
- Alerting, SLAs & Escalation Runbooks
- Define One-sentence Future State
- Post-Pilot Support & Handoff Deliverables
- Modeling & Metric Definition (Golden Metric Design)
- Access, Roles & Governance Touchpoints
- Confirm Pilot Use Case, KPIs, and Acceptance Criteria
- Validation & Threshold Tuning
- Final Sign-off & Next Steps
- Validation Checkpoint
- Next Steps & Pre-work for Architecture Walkthrough
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Solution Scope
Define pilot deliverables, data domains, architecture choices, governance roles, handoff responsibilities, and acceptance tests.
Scope Configuration
- Provision and Configure Cloud Data Warehouse or Lakehouse
- Implement CDC Ingestion for OLTP Databases
- Build ELT Pipeline: CRM (Salesforce) to Raw Zone
- Implement Bronze/Silver/Gold Data Layer (Lakehouse)
- Model and Implement Finance Revenue Dimensional Schema
- Implement Automated Data Quality Checks and Remediation
- Deploy Pipeline Monitoring, Alerting, and SLA Dashboards
- Implement Role-Based Access Control and Row-Level Security
- Deploy Data Catalog with Automated Lineage
- Implement CI/CD for Data Pipelines and SQL Models
- Deploy BI Semantic Layer and Shared Metrics
- Develop Executive Revenue Dashboard and Self-Serve Templates
- Migrate and Reconcile Historical Data with Legacy Reports
- Deliver Operational Runbooks and Handover Documentation
Scope Questions
Provision and Configure Cloud Data Warehouse or Lakehouse
- Which cloud provider(s) do you prefer for the data platform?
- Which runtime/warehouse technology do you plan to use (or evaluate)?
- Do you already have cloud accounts and a landing zone / subscription ready for provisioning?
- Estimate the initial data volume to host (raw + processed) for the pilot.
- Are there compliance, encryption, residency, or regulatory constraints we must enforce at provision time? If yes, please summarize.
Implement CDC Ingestion for OLTP Databases
- Which OLTP source systems require CDC for the pilot?
- What database engines and versions are in scope (e.g., Postgres, MySQL, Oracle, SQL Server)?
- What are the latency requirements for CDC (near real-time, hourly, daily)?
- Do you have a preferred CDC tooling or pattern (Debezium, vendor ETL, cloud-native replication, custom)?
- Are there network/security constraints (private VPC, VPN, whitelist) or DB performance concerns that affect connecting CDC tools?
Build ELT Pipeline: CRM (Salesforce) to Raw Zone
- Which Salesforce objects (Accounts, Opportunities, Leads, Contracts, Custom objects) must be included in the pilot?
- What ingestion cadence is required for CRM data (near-real-time, hourly, daily, batch windows)?
- Do you need full historical extraction or only incremental updates from Salesforce?
- Are there PII or sensitive fields that require masking or restricted access in the raw zone?
- Will attachments, activities, or large binary objects need to be ingested, or only structured records?
Implement Bronze/Silver/Gold Data Layer (Lakehouse)
- How do you define Bronze, Silver, and Gold in your organization (raw, cleansed, business-ready)?
- Which transformations should occur in Silver vs Gold for the pilot domain?
- Preferred storage/format for each layer (e.g., Parquet, Delta, Iceberg, ORC)?
- What retention and data lifecycle policies are required per layer (e.g., retention days, compaction cadence)?
- Who are the primary consumers of Bronze/Silver/Gold outputs (data engineers, analysts, BI, ML)?
Model and Implement Finance Revenue Dimensional Schema
- Which revenue sources and source systems must be represented (ERP, billing, payments, CRM)?
- What grain is required for revenue reporting (transactional, daily rollup, invoice-level, contract-level)?
- Are there mandatory mappings or reconciliation rules to the GL or official finance reports? Please summarize acceptance criteria for reconciliation.
- Do you require multi-currency handling and historical FX rates applied to revenue?
- Who is the business owner(s) for revenue metrics and who will sign off on the dimensional model?
Implement Automated Data Quality Checks and Remediation
- Which types of data quality checks are priority for the pilot (nulls, schema drift, ranges, freshness, duplicates, referential integrity)?
- Do you prefer automated remediation (e.g., quarantine, backfill) or human-in-the-loop alerts for failures?
- What tolerance thresholds or SLA targets should trigger alerts or remediation (e.g., X% completeness, latency > Y minutes)?
- Do you have a preferred DQ framework/tool (Great Expectations, Deequ, custom tests) we should integrate?
- Who should receive DQ alerts and what escalation path should be defined (teams, channels, on-call)?
Deploy Pipeline Monitoring, Alerting, and SLA Dashboards
- What pipeline SLAs/metrics matter most (success rate, end-to-end latency, data freshness, throughput)?
- Which monitoring and visualization tools should host dashboards (Looker, Grafana, Tableau, native cloud)?
- Which notification channels should be used for alerts (Slack, Email, PagerDuty, ServiceNow)?
- What are the on-call / ownership expectations for pipeline incidents during and after the pilot?
- Do you require historical SLA reporting and trend dashboards for executive reporting?
Implement Role-Based Access Control and Row-Level Security
- What identity provider and access model do you use (Okta, Azure AD, GSuite, LDAP, other)?
- How many distinct roles/groups should be represented initially (analyst, BI viewer, data engineer, finance analyst, exec)?
- Is row-level security required by business attributes (region, customer, business unit) for the pilot dataset?
- Are there column-level masking or encryption requirements for sensitive fields?
- Who will own access requests and periodic access reviews after deployment?
Deploy Data Catalog with Automated Lineage
- Do you have a preferred data catalog solution (Alation, Collibra, DataHub, Amundsen, native)?
- Which metadata types are required for the pilot (business glossary, owners, SLA, sensitivity classification)?
- Is automated lineage from ingestion and transformation tools required (true lineage), or are manual lineage entries acceptable?
- Who are the primary catalog consumers (data stewards, analysts, auditors, engineers)?
- Do you require PII discovery and auto-classification as part of the catalog scope?
Implement CI/CD for Data Pipelines and SQL Models
- Where will code and SQL artifacts be stored for CI/CD (GitHub, GitLab, Bitbucket, other)?
- What automated tests are required before deployment (unit tests, integration, data regression, contract tests)?
- What deployment cadence do you expect for pipeline and model changes (continuous, daily, weekly, ad-hoc)?
- Are there approvals or change-control requirements (change board, finance sign-off) that must be enforced in CI/CD?
- Do you require rollback and versioning policies for datasets and model artifacts?
Deploy BI Semantic Layer and Shared Metrics
- Which BI tools need the semantic layer (Looker, Tableau, Power BI, ThoughtSpot, other)?
- Do you have an existing metrics catalog or definitions that must be migrated into the semantic layer?
- Should the semantic layer live in the warehouse (materialized views / marts) or in the BI tool’s modeling layer?
- Who approves and governs shared metrics, and how should metric changes be communicated to consumers?
- Are there performance targets (query response time, concurrency) the semantic layer must meet?
Develop Executive Revenue Dashboard and Self-Serve Templates
- What are the top executive KPIs to include on the revenue dashboard (ARR, MRR, Bookings, Revenue recognized)?
- What refresh frequency is required for executive dashboards (real-time, hourly, daily, weekly)?
- Which audiences need tailored self-serve templates (executives, finance, sales ops, region managers)?
- Do you require drill-down capability from executive KPIs into root-cause supporting data?
- Are there branding, export, or embedding requirements for executive dashboards (PDF exports, embedding in intranet)?
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Mutual Commit
Finalize commercial terms, timeline, success milestones, and responsibilities for delivery, monitoring, and post-engagement support.
Agreement Modules
- Non-Disclosure Agreement (NDA)
- Master Services Agreement (MSA)
- Statement of Work (SOW)
- Commercial Terms & Payment Schedule
- Timeline & Milestone Schedule
- Success Milestones & Acceptance Criteria
- Roles, Responsibilities & Handoff (RACI)
- Service Level Agreement (SLA) & Support Commitments
- Data Processing & Security Agreement (DPA)
- Pre-Deployment Access & Environment Commitment
- Change Order Agreement
- Renewal, Extension & Ongoing Engagement Terms
- Termination & Exit / Transition Plan
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Deployment
Operationalize rollout with readiness checks, enablement, and outcome validation.
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Pre-Deployment Readiness
Confirm access, environment provisioning, sample data quality remediation plans, and runbook ownership before build begins.
Readiness Questions
Quick Win: Who's in the Room?
- Who will be our primary day-to-day contact for the pilot? (name, role, best contact)
- Which stakeholders should receive a weekly status update? (select roles)
- How confident are you that those stakeholders can make timely decisions when we hit blockers?
- Are there existing meeting cadences or governance rituals we should join rather than create?
- Tell us about one past pilot or handoff that went well or poorly—what made the difference?
If We Can't Get Access, Everything Stops
- If a single missing credential could stop the pilot cold, what would it be?
- Which systems must we access to build the pilot? Select all that apply.
- Are there corporate security gates we must clear (SSO/SAML, VPN, IP allowlist, service-account approvals)?
- Who in IT/security can expedite access, and how do we reach them? (name/role/contact)
- What is a realistic lead time for creating service accounts and granting needed permissions?
What Would Failed Provisioning Look Like?
- Imagine the environment isn't provisioned on day one—what breaks first for your users?
- Do you already have cloud subscriptions and capacity reserved for this pilot?
- Are there networking constraints (VPC peering, private endpoints, firewall rules) that typically cause delays?
- Describe any compliance, data residency, or audit requirements that will shape provisioning.
- Which provisioning approach do you prefer or require?
Show Me the Data We'll Trust
- If we started the pilot tomorrow, would the sample data pass basic quality checks—or fail silently?
- Which source systems will supply sample data for the pilot? Select all that apply.
- How representative is the sample dataset of production volume, schema variability, and edge cases?
- List the known data quality issues we should expect (duplicates, missing timestamps, inconsistent keys, PII masking needs). Give examples.
- Who owns source data remediation and what options can they apply (fix upstream, transform in pipeline, accept known gaps)?
If the Metrics Fight Again, Who Fixes It?
- When two dashboards show different numbers, who is empowered to declare the 'source of truth' and enforce it?
- Do you have a designated metric owner or data steward for the pilot domain?
- What governance processes exist for metric definitions, change control, and access?
- How are metric disputes resolved today?
- Would you agree to gate dashboards on approved metric definitions during the pilot to prevent conflicting executive numbers?
Who's Actually Running the Runbook?
- If a pipeline fails at 2 a.m., who do you expect to wake up and resolve it?
- Do you have runbooks for onboarding, incident response, and recovery for analytics pipelines?
- Who will own the pilot runbook (name/role) and will they be available during sprint cycles?
- Which alerting and escalation channels should we integrate with?
- What RTO (recovery time objective) and RPO (recovery point objective) goals should this pilot meet?
How Exact Are Your Acceptance Gates?
- Would you sign off on the pilot if data accuracy was 95% but a critical dimension was missing?
- Which acceptance tests must pass before we call the build complete? (select all that apply)
- What concrete data quality thresholds matter to you (examples: <1% nulls, <0.1% duplicate keys, latency < X minutes)?
- Who will execute these tests and who has final authority to approve acceptance?
- Do you require a documented sign-off artifact (checklist, ticket, or formal approval) for acceptance?
Will Your Team Be Ready to Run It?
- After handoff, will your team be confident to operate, monitor, and extend the platform without ongoing vendor support?
- Rate your internal team's current capability to operate the chosen platform.
- What training format will make your team confident (select all that apply)?
- How much shadowing or co-working time does your team need during handoff?
- Which documentation artifacts are must-haves at handoff? (select all that apply)
Is the Timeline Aspirational or Achievable?
- If executive pressure doubled tomorrow, would you rather compress scope or push a go-live date—and why?
- What are the hard deadlines that constrain this pilot (board reviews, financial close, migration windows)? Please list dates.
- Which resources are already committed and available for the pilot? Select all that apply.
- Where do you expect the longest delays—access, data cleaning, approvals, testing, or something else?
- Are there frozen windows (quarter-end, audit, migrations) when we cannot perform disruptive work? If yes, list them.
Risk Radar: What's Secretly at Risk?
- What single hidden dependency would most likely derail the pilot if not resolved quickly?
- Do any third-party vendors, contracts, or licenses need to be in place before we begin?
- Are there legal, privacy or compliance reviews that historically take longer than expected?
- What budget or procurement approvals could pause work mid-sprint if not secured?
- If we hit a showstopper, what is your preferred escalation path (who to call, who to loop in)?
Let's Lock the First 30 Days
- What one constraint—if unresolved in 30 days—would make continuing the project pointless?
- Please confirm which artifacts we should receive within the first 30 days (select all that apply).
- Propose three concrete milestones we should hit in the first 30 days (dates or deliverables).
- Who will be the single point of contact for day-to-day blockers and how should we escalate urgent issues?
- Do you agree to a weekly readiness checkpoint for the first 30 days? If yes, share preferred day/time or let us propose a slot.
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Deployment Enablement
Execute the pilot build with scheduled sprints, assigned owners, monitoring setup, and handover tasks to the internal team.
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Validation Checklist
Run acceptance tests, validate pipeline alerts and data quality thresholds, and confirm business users can trust and access the results.
Validation Questions
A Fast Read: Are We Ready to Validate?
- How confident are you that the pilot build is functionally complete and ready for acceptance testing?
- If you selected anything less than 'Completely confident', which specific areas give you the most pause (architecture, data quality, access, performance, other)?
- Who will be the single day-to-day contact from your side for coordinating validation activities and decisioning?
- Which time window do you prefer for running the initial acceptance tests?
What If the Tests Don’t Agree?
- When executive dashboards show different numbers after deployment, who will be expected to own the explanation?
- Tell us about a recent incident where metrics conflicted—what happened, who discovered it, and how was it resolved?
- How often do your executive-facing reports require reconciliation (daily, weekly, monthly, ad-hoc)?
- What SLA do you expect for investigating and resolving a high-impact metric discrepancy?
- If stakeholders disagree on a KPI definition during validation, who has final sign-off?
Where Are the Silent Failures Hiding?
- Which pipelines, if they failed silently overnight, would cause the most executive harm?
- How do you currently detect incomplete, delayed, or corrupt upstream data before it surfaces in dashboards?
- Describe the most recent pipeline failure you discovered—how was it detected, who fixed it, and how long did it take?
- What data-quality thresholds would you consider unacceptable for an executive-facing dashboard (e.g., nulls in key fields, mismatch % vs source, lag time)?
- Which teams must receive immediate alerts for a high-severity pipeline failure?
Do Alerts Mean Action or Noise?
- Are your current alert thresholds trusted, or are they mostly ignored as noise by the on-call team?
- How many false positives per week would be tolerable for your on-call team without causing alert fatigue?
- Who is on the escalation path when a critical alert fires after business hours?
- What runbooks, playbooks, or documented steps exist for the common alert types we might trigger during validation?
- Would mapping alert severity to business impact (e.g., executive-facing, operational, low) help your team prioritize responses?
If We Could Prove Trust, What Would It Look Like?
- What specific evidence would make your executive team stop challenging the platform’s numbers?
- Which artifacts would you want as proof: lineage, automated test reports, reconciliation dashboards, SLA dashboards, or others?
- How important is visibility into source-to-dashboard lineage for everyday users?
- Which user groups should have self-serve access to validated data versus read-only executive views?
- Would a daily 'trust' dashboard showing reconciliation success and active alerts increase executive confidence?
How Will We Test Acceptance—Not Just Run Tests?
- If the pilot passes all technical checks but business users still don't trust the numbers, what contingency should we have to get to genuine adoption?
- Which acceptance tests must pass for a green light (select all that apply)?
- Who from the business will participate in live UAT sessions and sign off on user-facing reports?
- How will we collect, track, and prioritize UAT feedback during the validation window?
- What acceptance criteria or test failures should trigger a rollback, remediation sprint, or postponement of sign-off?
Handoffs, Ownership, and Long-term Operability
- When consultants leave, what's most likely to break first in your environment—and why?
- Which responsibilities must shift to your internal team at handoff (monitoring, incident management, pipeline fixes, governance, security)?
- Do you have staff with the required skills for the chosen stack (Snowflake, Databricks, BigQuery, Synapse)?
- What training, runbooks, or shadowing would make your team confident to operate independently after handoff?
- How would you like post-engagement support structured: retainer with SLAs, on-demand hourly, training only, or hybrid?
Final Sign-offs and Next Moves
- What will it take for you to confidently sign off on pilot acceptance and authorize scale-up?
- Who must sign the pilot acceptance document (select all who must approve)?
- If validation finds issues, what timeline do you expect for remediation sprints before re-assessing acceptance?
- Would you prefer a structured go/no-go review meeting with clear decision criteria and artifact checklist?
- Are there any constraints, compliance obligations, or risk factors we should surface now that could block validation or sign-off?
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Success
Review pilot outcomes against success signals, capture learnings, and maintain a shared channel for ongoing issues and enhancements.
Success Reviews
- Pilot Success Review (All Stakeholders)
- Learning Retrospective (Delivery + Customer Teams)
- Operational Handoff & Runbook Acceptance
- Executive Outcomes Briefing
- Ongoing Support & Enhancement Cadence Setup
Issues & Enhancements
- Send a one-page executive summary and ROI appendix to attendees within 24 hours.
- Create a documentation gap list and assign authors to update runbooks and onboarding materials.
- Plan and schedule training sessions for the customer's operational team.
- Handoff Objectives & Acceptance Criteria
- Achieve operational sign-off on runbooks and acceptance tests from customer ops/engineering.
- Ensure monitoring and alerting are actionable and routed to the correct teams.
- Agree a shadowing schedule to transfer run-time knowledge before final cutover.
- Document outstanding technical items and owners for closure.
- Update runbooks with any gaps identified and re-publish for sign-off.
- Adjust alert thresholds or routes as agreed and verify alert delivery.
- Add required users to monitoring/incident tools and confirm access.
- Schedule three shadowing sessions between vendor and customer ops within 30 days.
- One-sentence Current State, Consequence & Future State
- Communicate clear business value and secure executive alignment on the recommended next step.
- Obtain approval for required budget/timeline or commit to a follow-on decision point.
- Clarify executive-level risks and required support from other business functions.
- Opening & Objectives
- If approved: initiate procurement/budget steps and update the project roadmap.
- If further info needed: provide targeted follow-up materials and schedule a short decision call.
- Purpose & Shared Channel Setup
- Establish a clear, accessible channel and cadence for ongoing communication and status updates.
- Agree on SLAs and escalation paths so incidents are handled predictably.
- Create a repeatable enhancement intake and prioritization process tied to business value.
- Publish a 90-day roadmap with owners to start continuous improvement immediately.
- Create the shared channel and populate with initial documentation, SLAs, and cadence invites.
- Publish the enhancement intake form and prioritization rubric; link it in the channel.
- Set up automated KPI reports/dashboards to post into the shared channel on the agreed cadence.
- Schedule the first operational review and first quarterly steering meeting with invites.
- Validate whether pilot met each success signal with evidence and stakeholder agreement.
- Achieve a clear decision to expand, extend, or remediate with assigned owners and timelines.
- Document unresolved risks and immediate mitigation actions that change the project's trajectory.
- Confirm the shared communication channel and cadence for ongoing updates.
- Publish a one-page pilot outcome report mapping each success signal to evidence and decision (expand/extend/remediate).
- If expand: prepare scope and budget amendment for signed approval within agreed timeline.
- If remediate: create prioritized remediation plan with owners, effort estimates, and target completion dates.
- Create the shared status channel and add agreed participants with permission levels.
- Set the Tone & Goals
- Create a prioritized, actionable backlog of improvements derived from the pilot.
- Assign clear owners and reasonable deadlines for each improvement.
- Identify and document training and documentation work needed for operational handoff.
- Establish follow-up checkpoints to verify improvements are completed.
- Publish the prioritized improvement backlog in the shared channel with owners and due dates.
- Schedule focused working sessions for top 3 remediation items within the next 2 weeks.
- Current State & Consequence (1-sentence)
- Headline Metrics & Business Impact
- Access, Environment & Permissions Review
- Timeline Recap
- Cadence & Meeting Formats
- ROI & Business Case Recap
- What Worked Well
- Success Signals & KPI Evidence
- Runbook & Playbook Walkthrough
- SLA & Escalation Matrix
- Enhancement Intake & Prioritization Criteria
- Monitoring, Alerting & Thresholds
- What Didn’t Work & Root Cause Analysis
- Recommendation & Decision Request
- Proof of Future State (Demo of Adopted Flows)
- Monitoring & Reporting KPIs
- Risks, Mitigations & Ask