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Data Products

Overview

Data Products are curated datasets, APIs, or analytical outputs designed for consumption by business users, analytics teams, or external partners.
Data products enable self-service analytics, operational efficiency, and data-driven decision-making while maintaining governance, quality, and compliance.

Purpose

  • Treat data as a product with defined owners, quality standards, and lifecycle.
  • Facilitate discoverability, access, and reuse across the enterprise.
  • Ensure security, privacy, and regulatory compliance for shared or exposed datasets.
  • Support analytics, AI/ML initiatives, and operational reporting with trusted, well-documented data.

Key Principles

  1. Ownership & Stewardship: Each data product has a defined owner and steward responsible for quality, updates, and lifecycle.
  2. Discoverability: Products are cataloged and documented for easy discovery by users.
  3. Quality & Reliability: Data products meet data quality, consistency, and timeliness standards.
  4. Security & Compliance: Access controls, privacy, and regulatory compliance are enforced.
  5. Versioning & Lifecycle Management: Track updates, deprecations, and retirements of data products.
  6. Documentation & Metadata: Provide clear definitions, lineage, usage guidance, and KPIs.

Data Product Types

Type Description Examples
Curated Datasets Cleaned and standardized datasets ready for analysis Vehicle sales by region, dealer performance metrics
Analytical Models Data-driven models for predictions, recommendations, or scoring Customer churn model, inventory forecast
APIs / Data Services Programmatic access to datasets or analytics results Dealer portal API, real-time vehicle telemetry API
Reports & Dashboards Predefined outputs for operational or strategic decision-making Executive KPI dashboard, warranty cost report

Data Product Lifecycle

  1. Ideation & Request: Identify business need and define data product requirements.
  2. Design & Build: Develop the product with standardized schema, quality checks, and documentation.
  3. Testing & Validation: Validate accuracy, consistency, and performance; ensure compliance.
  4. Deployment & Publication: Register product in the data catalog, assign access controls, and share with consumers.
  5. Monitoring & Maintenance: Track usage, performance, quality, and user feedback.
  6. Retirement / Decommissioning: Retire outdated or redundant products according to lifecycle policies.

Roles & Responsibilities

Role Responsibility
Data Product Owner Approves design, ensures alignment with business objectives, manages lifecycle
Data Steward Maintains quality, documentation, and metadata; monitors usage and compliance
Data Engineers / Developers Build and maintain data pipelines, APIs, and integration with platforms
Governance Council Reviews data product standards, approves exceptions, and monitors adherence

Tools & Technologies

  • Data Catalog / Governance Platforms: Collibra, Alation, Ataccama
  • Data Pipeline & ETL Tools: Informatica, Talend, Apache NiFi
  • API Management: Apigee, MuleSoft, or enterprise API gateways
  • Monitoring & Reporting: Dashboards, alerts, and quality metrics tracking

Visual Representation

flowchart TD
    A[Business Needs] --> B[Data Product Design & Build]
    B --> C[Testing & Validation]
    C --> D[Deployment / Catalog Registration]
    D --> E[Consumption by Users / Applications]
    E --> F[Monitoring & Feedback]
    F --> B
    D --> G[Security & Compliance Controls]