Quality Standards

The Core Attributes of a
Data Product.

To earn the title of "Product," a dataset must meet rigorous baseline standards. These six attributes ensure data is treated as a first-class asset that generates frictionless value.

Data Product Attributes and Principles

The DaaP Baseline

Every true Data Product must possess these six foundational characteristics to minimize consumer friction and maximize trust.

Discoverable

Data consumers shouldn't have to rely on tribal knowledge. A data product must be easily searchable in a centralized or federated data catalog.

  • Registered in a catalog
  • Rich, business-friendly metadata
  • Clear ownership attribution

Accessible

Also known as Addressable. Once found, the data product must provide a unique, programmatic address to reliably access the information.

  • Unique Global Identifier (URN/URI)
  • Standardized output ports (SQL/REST)
  • Machine-readable endpoints

Trustworthy

Consumers must trust the data without running their own validation checks. This requires guaranteed SLAs and transparent lineage.

  • Published SLAs (uptime, freshness)
  • Automated data quality testing
  • Transparent data lineage

Self-describing

A product shouldn't require an engineer to explain it. Schemas, semantics, and syntax must be embedded directly with the data.

  • Attached semantic definitions
  • Sample queries and documentation
  • Version-controlled schemas

Interoperable

Data from one domain must easily join with data from another. This is achieved through enterprise-wide standardization.

  • Standardized naming conventions
  • Shared master data references
  • Polyglot data formats (Parquet, JSON)

Secure

Security cannot be an afterthought. Access control, masking, and encryption must be handled locally but governed globally.

  • Role-Based Access Control (RBAC)
  • PII/PHI data masking policies
  • Automated audit logging

Why These Attributes Matter

Without these rigorous attributes, a "Data Product" is just another table dumped in a data lake. The goal of these six pillars is to completely eliminate consumer friction.

When a Data Scientist or Analyst finds a dataset that is self-describing, guaranteed to be accurate via SLAs, and accessible via standard SQL, they can generate insights in hours instead of weeks.

"Data as a Product shifts the focus from simply moving data around, to ensuring the data is fundamentally usable and valuable the moment it lands."

The Consumer Experience Check

1
I found it instantly. (Discoverable)
2
I know how to connect. (Accessible)
3
I understand the columns. (Self-Describing)
4
I can trust it's up to date. (Trustworthy)
5
I can join it with CRM data. (Interoperable)
6
My access was approved securely. (Secure)

Is Your Data a True Product?

Use our evaluation framework to score your existing datasets against the six core DaaP attributes and identify gaps in your architecture.

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