There are multiple angles of quality that we pay attention to:

• Quality of the inputs (source data quality)
• Quality of the deliverables (KPIs, benchmarks, forecasts, models, recommendations, etc.)
• Quality of the development pipeline (change control and testing before release)
• Quality of the value or operations pipeline (detecting changes in data, monitoring models and watching for drift, etc.)

Multiple roles may be involved in ensuring quality:

• The developers (e.g. data scientist, data engineer, ML engineer, data analyst, or other)
• The development team (e.g. peers reviews, manager reviews, etc.)
• The product owner (deserves special mention)
• The user or a representative of the user (e.g. UAT)

I’d like to borrow from an excellent QA methodology created by FedEx named: Quality Driven Management

The key principles in QDM are:

• Quality is defined by the customer
• Be scientific
• Measure measure measure
• Optimize business performance
• Quality involves teamwork
• View failures as opportunities

In the weeks and months to come I will be writing more about how to apply these principles to ensure adequate quality in BI and AI projects while also remaining fast. More to come!