Estimación relativa

How to estimate in complex contexts with high uncertainty?

Mastering estimation in complex contexts with high uncertainty is essential for those who want to work efficiently in agile teams. In these environments, the traditional approach of estimating projects in terms of days or hours fails due to its lack of flexibility and adaptability. As the distance or complexity of what we are estimating increases, so does the uncertainty. This can lead us to devote effort and energy to forecasts that may prove to be inaccurate and unhelpful.

Why is it important to use short cycle times in agility?

• Reducing uncertainty: The shorter the work cycle, the lower the uncertainty. Agile teams often opt for small iterations that allow constant adjustments.
• Minimization of waste: By planning in short cycles, the risk of investing in tasks or products that may ultimately have no impact or utility is reduced.
• Increased adaptability: Short cycles allow agile teams to respond quickly to changes that arise during project development.

What is relative estimation and how is it applied?

Relative estimation is a technique that focuses on comparing the size of tasks or features of a product, not looking at specific days but at the complexity that each task involves. Instead of calculating exact hours, references and universal numbers are used to facilitate comparison.

• Using rocks as a metaphor: Imagine several rocks representing different tasks; some are large (complex) and some are small (less complex). This method allows us to measure complexity in relation to each other.
• Defining a pivot: Choose a reference point that functions as the simplest task (pivot), and from there, estimate double, triple or more complexity with respect to that reference.
• Advantages: Simplifies discussions about the exact amount of effort and avoids unnecessary debates about numerical accuracy.

How does the Fibonacci sequence work in relative estimation?

The Fibonacci sequence provides a progressively growing set of numbers, establishing intervals that increase in distance as the numbers get higher, which is ideal for relative estimation.

• Characteristic of the sequence: Each number is the sum of the previous two (1, 2, 3, 5, 8, 13, etc.). This reflects a natural curve in which the first quantities are closer together, facilitating its use in incremental complexity.
• Good practice: For cycles or iterations, it is recommended that the estimated tasks do not exceed values such as 8 or 13. If a task exceeds these numbers, it should be broken down into smaller parts.

This method not only helps to classify tasks according to their complexity, but also leads teams to curb exaggeration in accuracy and focus on whether a task can be completed in a specific iteration.

Understanding and applying these techniques allows agile teams to run more smoothly, reducing uncertainties and adjusting their practices to the realities of working with high uncertainty. Continue to learn and practice these approaches to sharpen your agile skills and increase your effectiveness in complex environments.