Data Product Owner Track¶
Overview
Welcome to the DataOS Owner learning track! In DataOS, a data product owner must oversee the entire journey of a data product, from its initial design and deployment to its eventual deprecation.This track equips you to define, manage, and evolve Data Products that align with business goals, deliver measurable value, and stay governed throughout their lifecycle.
Who this track is for¶
| Persona | Why It Matters | Level |
|---|---|---|
| Data Product Owners / Managers | Drive product lifecycle, value delivery, and cross-functional alignment. Gain practical tools to manage strategy, quality, and usage. | Must-have |
| Technical Leads / Architects | Understand ownership principles to better support product design, governance enforcement, and delivery architecture. | Recommended |
What you will learn¶
Gain the skills and mindset to drive strategic data product outcomes across the lifecycle.
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Define & evolve Data Products: Learn how to define purpose, outcomes, and success criteria—and evolve products based on feedback and business needs.
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Ensure metadata completeness: Understand how to maintain accurate, complete metadata for lineage, ownership, classification, and discoverability.
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Oversee data quality: Monitor and enforce quality standards using profiling, validation, and cleansing techniques.
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Enforce governance & compliance: Apply policies for role-based access, data privacy, and regulatory frameworks (e.g., GDPR, HIPAA).
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Drive adoption with usage insights: Analyze product usage, prioritize improvements, and track performance to ensure relevance and value.
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Manage cost & efficiency: Monitor compute and storage consumption, optimize resources, and align with budgetary goals.
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Operationalize reliability: Configure alerts and response workflows to ensure timely detection and resolution of data product issues.
📚 Core modules¶
| No. | Module | Objective/Description | Key Topics |
|---|---|---|---|
| 1 | Data Product lifecycle management | Define the purpose of a data product and adapt it continuously based on evolving feedback and business needs to stay aligned with business value. | - Define purpose and goals - Capture and respond to feedback - Drive iterative enhancements - Decommission or retire obsolete Data Products |
| 2 | Ensuring metadata completion | Ensure completeness and consistency of metadata including ownership, lineage, and classifications to promote discoverability, trust, and collaboration. | - Metadata fields and standards - Lineage and classification - Ownership and stewardship - Update metadata to reflect lifecycle status |
| 3 | Ensuring data quality | Establish mechanisms to maintain data quality throughout the lifecycle, including validation, profiling, and cleansing. | - Data profiling and validation - Data cleansing - Monitoring and resolution of quality issues |
| 4 | Managing security and compliance | Enforce governance through role-based access, data masking, and compliance with regulations like GDPR or HIPAA. | - Access policies and controls - Role-based permissions - Compliance standards and audits |
| 5 | Usage analytics | Analyze how data products are used to improve design, performance, and adoption. Use insights to prioritize enhancements and ensure scalability. | - Track usage patterns - Optimize based on analytics - Performance tuning and adoption metrics |
| 6 | Resource management | Monitor and manage compute and storage to ensure cost-effective, scalable, and high-performing data product infrastructure. | - Monitor resource usage - Optimize storage and compute - FinOps practices |
| 7 | Alerts and notifications | Set up alerts for critical events like data issues or policy violations, and configure notifications for fast incident response and high system reliability. | - Alert triggers (e.g., failures, violations) - Notification channels (email, webhooks) - Incident response workflows |
