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Level 2 - Automated Product Maturity Assessment

Product Maturity Evidence Product​

Purpose​

The Product Maturity Evidence Product is a shared governance data product within the Holistic Data & Information Platform (HDIP). Its purpose is to provide a consistent, automated, and explainable assessment of data productization maturity across the organization, aligned with Data Mesh principles, DPCH (Data Product Characteristics), and the Base Product Specification (BPS).

Rather than relying on manual audits or static compliance checklists, this product continuously evaluates maturity by aggregating authoritative signals from existing HDIP shared services and producing periodic maturity snapshots that can be consumed by governance dashboards, onboarding workflows, and automation.

Why Productize Maturity?​

Productization maturity is not an operational metric; it is a governance and investment signal. Treating maturity assessment as a first-class data product ensures that:

  • Maturity scores are derived from system truth, not opinion
  • Results are reusable across governance, portfolio management, and automation
  • Evidence is traceable and auditable over time
  • Domains are not burdened with repetitive manual reporting

In a Data Mesh operating model, governance capabilities themselves must be productized, discoverable, and consumable — maturity assessment is no exception.

Core Design Principles​

The Product Maturity Evidence Product is designed around the following principles:

  1. Derivative, Not Invented Maturity scores are computed from existing platform signals (catalog, policy, observability, FinOps, etc.). The product does not create new truth; it aggregates and interprets authoritative sources.

  2. Facade-Based Integration A single facade API exposes maturity evidence and scores, shielding consumers from the complexity and evolution of underlying shared services.

  3. Snapshot-Oriented, Not Real-Time Productization maturity changes slowly. Assessments are produced as weekly/monthly snapshots, providing stability, clarity, and meaningful trends.

  4. Explainable by Construction Every maturity score is decomposable into DPCH-level results with explicit evidence links and timestamps.

  5. Automation-First, Human-Compatible Automation is the target state, but human self-attestation remains a supported input where signals are not yet available.

Scope of Assessment​

The product evaluates maturity against the 14 Data Product Characteristics (DPCH01–DPCH14), covering:

  • Ownership and accountability
  • Deployability and declarative specification
  • Discoverability and self-description
  • Trust, observability, and compliance
  • Reuse and semantic alignment
  • Economic accountability

Each characteristic contributes a weighted score to an overall productization maturity percentage, accompanied by prioritized improvement recommendations.

Input Ports — Maturity Signals​

The Product Maturity Evidence Product consumes signals from existing HDIP shared services. Conceptually, these are modeled as input ports, even if implemented as APIs or scheduled extracts.

Typical input ports include:

  • Product Catalog & Registry Signals Ownership, discoverability, metadata completeness, declared purpose

  • Deployment & Specification Signals (DPDS / CI) Declarative spec presence, pipeline status, versioning, contract tests

  • Observability & SLO Signals SLI/SLO definitions, availability, freshness, incident indicators

  • Trust Signals Data quality metrics, lineage coverage, control execution status

  • Policy & Entitlement Signals Policy bindings, data classification, access controls, audit readiness

  • Interface & Output Port Signals APIs, SQL endpoints, SDKs, documentation availability

  • Semantic Alignment Signals Ontology mappings, canonical identifiers, model conformance

  • FinOps & Usage Signals Cost attribution, usage metrics, showback/chargeback readiness

  • Domain Self-Attestation Signals (Optional) Temporary human-provided evidence where automation is not yet available

Each signal remains owned by its authoritative service; the maturity product simply consumes and interprets them.

Evaluation and Snapshot Model​

Maturity is evaluated on a scheduled basis (weekly by default). Each evaluation produces a snapshot containing:

  • Overall maturity score (percentage)
  • DPCH-level results (Yes / Partial / No)
  • Weighted score contribution per characteristic
  • Evidence links and source systems
  • Evaluation timestamps and confidence level

Snapshots are persisted to support:

  • Trend analysis over time
  • Audit and certification needs
  • Portfolio-level governance reporting

Output Ports​

The Product Maturity Evidence Product exposes multiple output ports, including:

  • Maturity Evidence API (Facade) Latest maturity snapshot per data product / per domain / per division

  • Snapshot Dataset Periodic historical maturity records

  • Event / Notification Port Alerts on significant regressions or threshold breaches

  • Governance Dashboard Feed Aggregated views across domains and portfolios

These outputs enable both human decision-making and automated governance workflows.

Relationship to Manual Checklists​

The interactive Data Product Maturity Checklist published on the BPS site represents Phase 0 of this journey:

  • A lightweight, domain-friendly self-assessment
  • A mechanism to capture early evidence links
  • A bridge toward automated evaluation

As automation matures, computed results progressively replace self-attested inputs, while preserving transparency and explainability.

Strategic Outcome​

By modeling productization maturity as a data product:

  • Governance becomes continuous, measurable, and scalable
  • Domains receive clear, actionable feedback without heavy process
  • Leadership gains portfolio-level visibility grounded in system evidence
  • HDIP delivers governance as a product, consistent with Data Mesh principles

This approach ensures that maturity assessment evolves from a checklist into a durable, trusted governance capability.

Aligned with: Base Product Specification (BPS 0.1) · Data Mesh Principles · HDIP Governance Model