“Without lineage, teams operate blind, and governance becomes reactive cleanup,” says Carter Page, executive vice president of research and development at Astronomer. “When teams can see where data originated, how it was transformed, and every system that relies on it, updates become predictable, the right pipelines get tested, the target stakeholders are notified, and breaking changes are documented before they cause incidents.”
What is a data product’s life cycle?
Life-cycle management of an API, application, or AI model requires defining a release schedule for delivering improvements, fixes, and other required upgrades. Data product life-cycle management involves several similar disciplines. Ulf Viney, executive vice president of engineering, support, and operations at Precisely, says, “Life-cycle management must include versioning, testing, structured deployment, and stakeholder communication.”
One fundamental difference with data products is that their life-cycle management is closely linked to how their underlying data sets grow or undergo structural changes. Having a data product that works today but isn’t resilient to changes or doesn’t generate alerts when fixes are necessary can break downstream use cases and erode stakeholders’ and users’ trust in the data.
