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Key Findings

Note

  • ADO cannot act autonomously due to ND dependency and access constraints.
  • Strategic misalignment and fire-drill culture inhibit sustainable progress.
  • Critical data remains scattered, undocumented, and poorly governed.
  • Excessive vendor reliance increases cost and decreases institutional knowledge.
  • Organizational focus on dashboards over infrastructure creates fragile outcomes.
  • A reset in priorities, architecture, and ownership is essential for scalability.

The current state of NNA’s data organization reflects systemic challenges that limit agility, transparency, and long-term scalability.

My assessment surfaced the following core issues:

  • Unclear Roles and Accountability: Confusion persists between the missions of ADO and Nissan Digital (ND). ADO is expected to deliver strategic value, yet remains dependent on ND for infrastructure, tooling, and access—creating misalignment and executional delays.
  • Reactive Culture (“Fireman Syndrome”): The organization often prioritizes urgent issues over long-term solutions. This reactive model limits innovation, fosters technical debt, and perpetuates short-lived fixes rather than scalable capabilities.
  • Fragmented Business Requirements: Most business data needs are undocumented, maintained via spreadsheets, or shared informally. This severely hinders the ability to align data strategy with business value, prioritize use cases, or scope projects effectively.
  • Foundational Gaps in Data Governance: There is no formal governance model for data ownership, stewardship, or quality. While individual efforts are emerging, they lack coordination, authority, and scalability.
  • Misaligned Data Priorities: Efforts are disproportionately focused on end-user outputs like dashboards and AI tools—while foundational layers like data quality, metadata, lineage, and governance are underdeveloped. This results in fragile, unsustainable outcomes.
  • Technical Debt and Vendor Lock-In: Legacy systems (e.g., C360), fragmented ETL processes, and proprietary platforms have created inconsistencies, duplication, and limited agility. Multiple “golden record” definitions and non-standardized data formats complicate even basic operations.
  • Lack of Cataloging and Discovery: Employees struggle to find or reuse datasets due to limited metadata and poor visibility into available assets. This leads to duplicated efforts and inefficient onboarding.
  • Over-Fragmented Data Landscape: With more than 280 isolated databases and limited reuse, data remains siloed and duplicative across the enterprise—reflecting a lack of centralized stewardship and architectural oversight.

These findings form the foundation for my recommendations—designed to establish clear ownership, streamline tooling, and reorient priorities toward sustainable data governance, productization, and business alignment.