Validating Data as a Product

Move beyond "data as an asset" to "data as a product." This guide provides a comprehensive framework for validating usability, trustworthiness, and value before data ever reaches the consumer.

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Trustworthy

Validation ensures data is accurate, consistent, and reliable every time.

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Consumable

Products must have clear contracts, schemas, and documentation.

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Discoverable

Metadata and cataloging make the product easy to find and understand.

The Validation Lifecycle

Validation isn't a single step; it's a continuous lifecycle. Click on the stages below to explore the specific validation checks required at each phase of the data product journey.

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1. Design Contract
Schema & SLA Definition
🏗️
2. Build & Test
CI/CD & Unit Tests
⚙️
3. Run & Monitor
Observability & Drift
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4. Iterate
Feedback Loops

Validation Metrics

To treat data as a product, you must measure it like one. These visualizations demonstrate how to track data health across six key dimensions and monitor reliability over time.

The 6 Dimensions of Data Quality

Insight: This chart helps identify imbalance. A product might be timely (Freshness) but inaccurate, requiring a shift in engineering focus.

SLA Compliance Trend

Insight: Consistent delivery builds trust. Dips in the line indicate incidents where the "product" failed to meet its promised delivery time.

Product Readiness Assessment

Validate your dataset against core product principles. Check the boxes that apply to see your readiness score.

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