Historical Data Analysis is a popular method that helps forecast task delivery time through investigating and re-appraising durations of past-period tasks and activities. The method involves a portion of comparison analysis to predict future time trends of current tasks.
The key advantage of using historical data analysis in estimating task duration is that the method offers a comparison-based approach to duration forecasting and trending. If there are completed tasks that reach success in the past, then this valued experience can be applied to scheduling new similar tasks. Through comparing both the past and current experience, time forecasting tends to be more effective because comparison analysis lets avoid past-period mistakes in the present. The only limitation here is that the method requires available records about tasks completed in the past.
The following action plan explains how to perform historical data analysis for estimating task duration:
- Identify a current task to be estimated for time length
- Check if there are tasks that have been completed in the past
- Analyze the current task and the past tasks for similarity
- Reject those of the past tasks that are not similar to the current one
- Retrieve time parameters of the past tasks (Start time, Finish time, Duration, Idle time)
- Review problems the completed tasks faced in the past
- Understand solutions to those problems
- Figure out if solutions can be applied to the current task
- Develop a rough duration estimate for the current task
- Check if this task is feasible and achievable in terms of the rough estimate
- Understand and make necessary corrections to the estimate if there are any troubles with the task’s feasibility
- Confirm the estimate and develop the final version.