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Data Office Rollout – Key Milestones

🎯 Objective

Establish a sustainable, efficient, and value-driving Data Office that aligns business strategy with high-quality, governed, accessible data.

Milestones & Timeline

Step Milestone Title Description Timeline
0 Redefine and Implement Organization Group all technical branches under the same unique SVP; align incentives, clearly define roles and responsibilities Month 0
1 Define Strategy & Governance Model Align on vision, scope, org structure, funding. Form the governance council. Draft policies (e.g., data levels, stewardship roles). Month 0–2
2 Stand Up Foundational Data Capabilities Stand up secure repositories (e.g., Snowflake, Postgres). Integrate pipelines (Lambda, Glue, Cleo, Informatica). Month 2–4
3 Deploy Metadata & Data Catalog Platform Launch Microsoft Purview or Atlan. Onboard datasets. Configure classification, lineage, and access roles. Month 3–6
4 Define and Enforce Access Model Implement tiered access (Level 0/1/2), align with InfoSec. Create request workflows (ServiceNow). Month 4–6
5 Enable Data Brokering & Self-Service Launch data portals, APIs (via Apigee), and curated datasets. Enable role-based access and service SLAs. Month 6–8
6 Data Quality & Monitoring Framework Deploy Dynatrace/Soda.io for observability. Create data SLA dashboards. Establish escalation playbooks. Month 7–9
7 Deliver Data-Centric Products Develop BI dashboards, ML models, or APIs to serve business needs. Prioritize high-impact domains. Month 8–12
8 Operationalize Feedback & Iteration Launch quarterly governance reviews, data satisfaction surveys, and continuous catalog expansion. Ongoing

Strategy and Governance

Purpose:

Establish a clear strategic vision, define the organizational model, and implement governance frameworks to ensure data quality, privacy, and accountability from the start.

Key Activities:

  • Define the Data Office mission, scope, and strategic KPIs
  • Form the Data Governance Council (IT + business leaders)
  • Develop data policies: ownership, classification, lifecycle, access
  • Implement tiered data access (Level 0 = PII, Level 1 = strategic, Level 2 = open/internal)
  • Define stewardship roles and escalation models

KPIs:

  • % of data assets with clear ownership/stewards (Target: 90%)
  • Governance council engagement (Target: Monthly cadence with >80% attendance)
  • % of business units participating in governance policies (Target: 100%)
  • Policy compliance rate for PII/regulated data (Target: >95%)

Nissan Data Repository (NDR)

Purpose:

Establish a unified, scalable and secure data platform as the foundation for future analytics and product development.

Key Activities:

  • Consolidate data sources using AWS Lambda, Glue, Informatica, Cleo
  • Centralize in Snowflake + Postgres (hybrid setup)
  • Establish naming conventions, data lake/warehouse zones (raw, cleansed, curated)
  • Tag datasets with metadata and classification (via Purview or Atlan)
  • Monitor and log ingestion quality, freshness, and SLA compliance

KPIs:

  • % of business-critical data sources ingested (Target: 80% by Month 6)
  • % of ingested data assets with metadata and classification (Target: 90%)
  • Data pipeline SLA adherence (Target: >98% uptime / freshness compliance)
  • % of documented and searchable datasets in catalog (Target: 500+ in Year 1)

Nissan Data Services (NDS)

Purpose:

Enable business users and data teams to leverage trusted, secure data for analytics, innovation, and automation, efficiently and at scale.

Key Activities:

  • Publish datasets in catalog and enable role-based access (Snowflake + Apigee)
  • Launch self-service dashboards, APIs, and data broker tools
  • Create reusable data products (sales KPIs, fleet intelligence, etc.)
  • Embed privacy controls (CCPA, GDPR) with automated workflows
  • Roll out internal education and literacy programs

KPIs:

  • Time-to-access for new dataset requests (Target: <2 business days)
  • of unique users accessing data services monthly (Target: 200+)
  • of reusable data products/APIs published (Target: 25+ in Year 1)
  • Internal satisfaction rating of data services (Target: ≥ 4/5)

Nissan Data Lab (NDL)

Purpose:

Act as an agile experimentation unit to validate new ideas, technologies, and data use cases rapidly, bridging business needs with technical feasibility in a low-risk environment.

Key Activities:

  • Form a small, cross-functional team (≤ 5 members) with a mix of data engineers, analysts, and business SMEs
  • Launch 2–3 week proof-of-concepts (PoCs) focused on high-potential use cases
  • Operate in a sandboxed environment with flexible tooling and minimal red tape
  • Define clear success/failure criteria at project kickoff
  • Transition validated PoCs to Nissan Data Services (NDS) for industrialization
  • Archive and document failed PoCs with key learnings

KPIs:

  • % of PoCs completed within 30 days (Target: 80%+)
  • % of PoCs transitioned to NDS or adopted by business teams (Target: ≥ 50%)
  • Time from idea to prototype demo (Target: ≤ 3 weeks)
  • Business satisfaction score on iLab outputs (Target: ≥ 4/5)

Tips for Success

  • Don't skip ahead: ensure governance and quality are functioning before brokering or product development.
  • Maintain regular alignment between business sponsors and technical stakeholders.
  • Create lightweight, repeatable processes for intake, approval, and escalation.