Performance Tracking

Measuring the
Data Product.

When viewing data as a product, it is crucial to measure it accordingly. This means going beyond mere table and row counts and instead focusing on factors such as adoption rates, reliability, and actual business impact.

View the Metrics
Data Product Measures and KPIs

The Core Measurement Pillars

A thorough measurement plan monitors performance in four key areas: Adoption, Quality, Operational Health, and Business Value.

Consumer Focus

1. Adoption & Usage

Assesses if the intended audience is actively using the data product to gain valuable insights.

  • Monthly Active Users (MAU) Number of unique consumers querying the product.
  • Query Volume Total number of API calls or SQL queries executed.
  • Downstream Dependencies Number of ML models or dashboards reliant on this data.
Asset Focus

2. Data Quality & Trust

Evaluates the intrinsic precision and reliability of the data provided to users.

  • Data Contract Pass Rate Percentage of pipeline runs that successfully pass all schema and anomaly checks.
  • Null & Duplicate Rates Continuous tracking of missing or corrupted records.
  • User Trust Score (NPS) Qualitative survey scores from users on data reliability.
Engineering Focus

3. Operational Reliability

Evaluates the efficiency, availability, and speed of the data product's infrastructure.

  • Data Freshness (Latency) Time elapsed between source event and output port availability.
  • Endpoint Uptime Success rate of accessing the API or SQL view.
  • Incident Resolution Time (MTTR) Mean time to repair when a pipeline breaks.
Executive Focus

4. Business Value

The key metric: directly connecting the data product's existence to financial or strategic results.

  • Time to Insight Decreased time analysts need to locate and utilize data.
  • Cost of Data Operations Compute and storage costs associated with running the product.
  • Impact ROI Projected revenue generated or expenses reduced by downstream use cases.
The Metric Pyramid

The Hierarchy of Data Metrics

Different people prioritize different metrics. An effective data strategy includes a range of metrics that span from operational health to business impact.

If your pipeline consistently operates without issues (High Reliability) but lacks user engagement (Low Adoption), your Business Value will be minimal. It is crucial to assess the entire system to pinpoint where your data product strategy is failing.

Business Impact

ROI, Revenue Uplift, Cost Savings

Adoption & Usage

MAU, Query Volume, Dependant Apps

Trust & Quality

Contract Pass Rate, User NPS, Accuracy

Operational Health

Uptime, Latency, MTTR, Compute Costs

Build Your Data Dashboard

Don't speculate on the worth of your data teams. Use a standardized KPI framework to monitor the success and ROI of each data product in your network.

Review DataKnobs Case