DATA PRODUCTS

A Data Product is a curated, reusable, and well-documented data asset that delivers insights or value to users, similar to a traditional product. It’s designed for a specific use case, with clear ownership, governance, and quality standards.

A dashboard is a presentation layer of data, while a data product includes the data itself, its logic, lineage, metadata, governance, and access mechanism—all packaged for reuse and scalability.

• Customer 360 view

• Product recommendation dataset

• Monthly revenue metrics dataset

• Churn prediction model output

• Risk scores for loan approvals

It promotes accountability, usability, and adoption. When data is treated like a product, it has defined owners, quality benchmarks, documentation, and a lifecycle that leads to higher trust and business impact.

 Ownership is ideally shared: 

• Business team owns the use case and KPIs.

• Data team ensures data engineering, modeling, and availability.

Often a data product owner is appointed to manage this intersection.

• Data product owner

• Data engineer/architect

• Data steward

• Data analyst/scientist

• Business stakeholder/consumer

1. Ideation

2. Design

3. Development

4. Testing/Validation

5. Publishing

6. Monitoring & optimization

7. Retirement or iteration

By collecting user feedback, monitoring usage metrics, addressing data quality issues, and regularly reviewing the product against evolving business goals.

• Business context

• Dataset/API/model

• Data definitions & metadata

• Usage documentation

• Quality metrics

• Lineage and versioning

• Access controls

Start with user needs, define clear KPIs, ensure ease of access, document thoroughly, and provide training/support if needed. Usability should be central.

• Accuracy

• Completeness

• Timeliness

• Consistency

• Freshness

• Traceability

• Definitions of fields

• Source system

• Update frequency

• Owners & stewards

• Business rules & logic

• Last modified timestamp

• Data catalogs (e.g., OvalEdge, Alation)

• Data warehouses (Snowflake, BigQuery)

• ETL tools (Informatica, dbt)

• Visualization (Tableau, Power BI)

• Orchestration (Airflow)

• APIs / ML platforms
  

In data mesh, data products are the primary unit of value. Each domain team is responsible for creating and managing its own data products as part of a decentralized, federated model.

Yes. AI can help with data classification, quality checks, anomaly detection, data summarization, and even building pre-trained analytics "recipes" or ML models.

• Usage metrics (views/downloads/API calls)

• Business outcomes (e.g., revenue uplift, cost savings)

• User satisfaction (surveys, NPS)

• Data quality improvement

• Reduced time-to-insight

• Poor documentation

• Low data quality

• Difficult access or lack of discoverability

• Misalignment with business needs

• No training or change management

They provide ready-to-use, trustworthy insights that enable faster, data-informed decision-making—reducing dependency on data teams and speeding up time-to-action.

• Embed definitions and context

• Train users on how to use and interpret

• Offer internal forums or communities

• Use metadata and glossary integrations

By classifying sensitive data, masking PII, documenting data usage purpose, implementing consent management, and maintaining audit logs.

• Role-based access

• Attribute-based access

• Approval workflows

• Temporary or context-based permissions

Lineage shows where the data came from, how it was transformed, and who used it, helping with trust, debugging, and audits.