Which best describes Aware's data products lifecycle stages?

Which best describes Aware's data products lifecycle stages?

• A. Ingest, store, transform, archive.
• B. Create, curate, publish, monitor, iterate; manage SLAs, owners, and consumption by apps/users.
• C. Plan, approve, deploy, retire.
• D. Collect, clean, analyze, visualize.

Answer: (B)

Aware treats data products as living artifacts that go through creation, curation, publication, monitoring, and continuous improvement, with explicit ownership and service expectations. Creating a data product defines its scope, schema, and interfaces. Curating it ensures data quality, lineage, metadata, governance, and appropriate access controls so it can be trusted and reused. Publishing exposes the product to apps and users, enabling discoverability and consumption through defined interfaces and SLAs. Monitoring provides visibility into usage, performance, data freshness, and SLA adherence. Iterating uses feedback and metrics to enhance the product over time. Managing SLAs, owners, and consumption by apps/users ties the product to business outcomes, clarifies accountability, and governs who can access and how it is used. This combination of lifecycle stages with governance and consumption management is what makes this option the best fit for describing Aware's data products lifecycle. Other options describe more general data pipelines, project deployment steps, or analytics workflows, but they miss the integration of product lifecycle, governance, and explicit consumption by apps/users that characterizes Aware’s data products approach.

Aware treats data products as living artifacts that go through creation, curation, publication, monitoring, and continuous improvement, with explicit ownership and service expectations. Creating a data product defines its scope, schema, and interfaces. Curating it ensures data quality, lineage, metadata, governance, and appropriate access controls so it can be trusted and reused. Publishing exposes the product to apps and users, enabling discoverability and consumption through defined interfaces and SLAs. Monitoring provides visibility into usage, performance, data freshness, and SLA adherence. Iterating uses feedback and metrics to enhance the product over time. Managing SLAs, owners, and consumption by apps/users ties the product to business outcomes, clarifies accountability, and governs who can access and how it is used. This combination of lifecycle stages with governance and consumption management is what makes this option the best fit for describing Aware's data products lifecycle.

Other options describe more general data pipelines, project deployment steps, or analytics workflows, but they miss the integration of product lifecycle, governance, and explicit consumption by apps/users that characterizes Aware’s data products approach.