The Data Product
Lifecycle.
Data products are always a work in progress, requiring a dynamic lifecycle. Become proficient in the five essential stages from initial exploration to ongoing evolution.
The End-to-End Journey
Contrary to conventional 'fire-and-forget' data projects, a genuine Data Product adheres to a strict, iterative process overseen by a specialized Product Manager.
Discovery
Recognize the issue faced by the business and interact with potential data users to grasp their challenges, objectives, and the unique benefits the data product will provide.
- Define use cases & ROI
- Assess data feasibility & sources
- Identify target consumers
Design
Design the solution before coding. Specify the data contracts, output ports, schemas, and required Service Level Agreements (SLAs) to establish trust.
- Draft Data Contracts
- Establish SLAs & SLOs
- Design schema & semantics
Build
During the engineering phase, create strong data pipelines, carry out transformations, ensure adherence to security policies, and incorporate automated data quality testing.
- Develop ingestion pipelines
- Implement transformations (dbt)
- Write CI/CD & quality tests
Launch
Deploy the product to production, add it to the enterprise data catalog for visibility, and open the output ports for consumer access.
- Register in Data Catalog
- Expose APIs / SQL endpoints
- Begin SLA monitoring
Iterate
Continuously monitor usage and collect customer feedback. Update product based on business requirements, or retire it gracefully if it becomes obsolete.
- Track adoption metrics
- Manage semantic versioning
- Execute sunsetting protocols
Why "Iterate" is the Most Important Stage
In the traditional data warehousing model, pipelines were constructed and then neglected until they malfunctioned. This approach of 'set it and forget it' results in significant technical debt and data stagnation.
Viewing data as a product requires acknowledging that business logic is subject to change. Iteration The mechanism ensuring the alignment of the data product with the business includes semantic versioning (v1.0 to v1.1), maintaining backward compatibility, and actively phasing out outdated data assets.
The Lifecycle Matrix
Traditional IT Project
Success is determined by meeting the requirements, successfully building the product, delivering it on time and within budget, and ultimately deciding whether to proceed or abandon the project.
Data Product Model
Discover $\rightarrow$ Design $\rightarrow$ Build $\rightarrow$ Launch $\rightarrow$ Iterate. Success is measured by "Consumer adoption and ROI."
Adopt Product Management for Data
Give your data teams the support of dedicated Data Product Managers to steer your assets through each stage of the process.