Build a KPI-Ready Data Product
Key takeaway
A KPI-ready data product is a governed, reusable dataset or API that reliably powers a specific business KPI. Building one takes five steps: define the KPI and assign ownership, map sources and agree a data contract, build a tested pipeline, validate outputs and monitor SLAs, then document it in the catalog and package the pattern for reuse.
This tutorial walks through delivering a single data product that powers a business KPI from ingestion to consumption.
Step 1: Define the KPI and Ownership
- Document the KPI definition, calculation logic, and intended users.
- Assign a data product owner and steward.
Step 2: Map Sources and Data Contracts
- Identify source systems and data owners.
- Define a data contract with schema, freshness, and quality thresholds.
Step 3: Build the Pipeline
- Ingest raw data and apply standard transformations.
- Add automated data quality tests in the pipeline.
- Publish curated tables or APIs for consumption.
Step 4: Validate and Monitor
- Validate KPI outputs with business stakeholders.
- Set SLAs for refresh time and quality checks.
- Monitor usage, failures, and downstream impact.
Step 5: Document and Scale
- Add metadata, lineage, and definitions in the catalog.
- Package the pattern for reuse across other domains.
Next Steps
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Sources
Frequently asked questions
What is a data product?
A data product is a curated, owned data asset, such as a table or API, built to serve a defined business need like a KPI. It has a named owner and steward, a data contract covering schema, freshness, and quality thresholds, automated tests, SLAs, and catalog documentation so others can trust and reuse it.
What should a data contract include?
A data contract should include the schema, freshness expectations, and quality thresholds that source data must meet. It is defined with source system owners so upstream changes do not silently break downstream KPIs. Automated data quality tests in the pipeline then check incoming data against those thresholds.
How do you make sure a KPI is calculated correctly?
Make sure a KPI is correct by documenting its definition, calculation logic, and intended users before building, then validating outputs with business stakeholders once the pipeline runs. Automated data quality tests in the pipeline and SLAs for refresh time and quality checks keep the KPI accurate over time.
How do you scale a data product pattern to other domains?
Scale a data product by documenting it thoroughly and packaging the pattern for reuse. Add metadata, lineage, and definitions to the data catalog so others can discover and trust it, then apply the same steps, contracts, and pipeline approach to other data domains instead of designing each product from scratch.
