Question 42

Explanation

It's a tricky question and the tricky word here is quickly.

According to SAFe 6 documentation, historical data is a valuable tool for Product Managers to quickly estimate the size of Features in Story Points. By analyzing previous projects and the work done, Product Managers can develop a baseline for estimating the effort required for similar types of Features.

Here are some reasons why historical data is an effective approach for estimating Story Points:

• Experience-based: Historical data is based on actual experience, providing a more accurate assessment of effort compared to theoretical or arbitrary methods.

• Predictive power: Historical data's ability to predict future performance can help Product Managers make informed decisions about resource allocation and project planning.

• Continuous refinement: By continuously analyzing historical data, Product Managers can refine their estimations over time, improving their accuracy and efficiency.

While other methods, such as Ideal Days and T-Shirt Sizing, can be helpful in long term situations, historical data remains the most reliable and consistent approach for estimating quickly Story Points in SAFe 6.

Ideal Days is a relative estimation method that assigns a time duration to each Story Point, typically ranging from 1 to 10 days. It may not be as accurate as historical data, as it does not consider the specific complexity and variability of each Feature.

T-Shirt Sizing is another relative estimation method that categorizes Story Points into sizes, such as Small, Medium, Large, and Extra-Large. It is a simple method, but it may not provide enough granularity for accurate estimation.

Please check the SAFe 6 POPM workbook, Customer-centric Features section to get more informations.