BA Skills

A Quick guide for Data Analytics who want to get better on Business Analysis Skills.


Effort estimation is a crucial aspect of project management, as it helps determine the resources, time, and budget required for a project. There are several methods for effort estimation, and selecting the right one depends on the project’s complexity, available data, and specific requirements. Here are some common estimation techniques and when to use them:

  • Expert Judgement: Leveraging the knowledge and experience of experts to estimate project efforts. This method is suitable for smaller projects or when there is limited data available for more formal estimation techniques.

  • Analogous Estimation: Comparing the current project with similar past projects to estimate efforts based on historical data. This technique works best when there is a sufficient record of comparable projects and the projects are relatively similar in nature.

  • Parametric Estimation: Using mathematical models and historical data to calculate project efforts based on key parameters or variables. This method is suitable for projects with well-defined, measurable parameters and access to reliable historical data.

  • Bottom-up Estimation: Breaking down the project into smaller tasks, estimating the effort for each task, and then aggregating the efforts to determine the overall project effort. This approach is ideal for complex projects with well-defined work breakdown structures.

  • Three-point Estimation: Incorporating optimistic, pessimistic, and most likely estimates for each task, and then using statistical methods, such as PERT (Program Evaluation and Review Technique), to calculate a weighted average. This technique is useful when there is uncertainty in the estimates and can help account for potential risks.

Risk analysis plays a crucial role in effort estimation. It involves identifying, assessing, and prioritizing potential risks that could impact the project’s effort, cost, or schedule. By incorporating risk analysis into effort estimation, you can:

  • Account for uncertainties and potential obstacles in the estimation process.
  • Allocate contingency reserves for unforeseen events or risks.
  • Develop risk mitigation strategies to minimize the impact of risks on project efforts.
  • Improve the overall accuracy and reliability of project estimates.
  • Incorporating risk analysis into your effort estimation process helps ensure that your project plan is more resilient, robust, accurate, and better prepared to handle potential challenges.

Follow this steps to enhance your estimations:

  • Arrange the team to decompose requirements into individual tasks, enabling more accurate estimations.
  • Utilize the proper methods for both rough and precise estimation and planning.
  • Monitor estimation discrepancies throughout the project to refine and enhance accuracy.
  • Ensure that estimates encompass all facets of delivery, including coding, testing, automated testing (including unit testing), documentation, code review, and more.
  • Proactively inform the customer about changes in estimates to prevent any last-minute surprises.
  • Buffering - getting ready for the unknown unknowns. Allocate funds in the budget for contingencies and risk mitigation efforts

Remember about:

Undisciplined Pursuit of More Pursuit of Better
Wait and React Prepare
Assuming Best Case Resilient to unexpected events
Falls into the Planning Fallacy Right Time

Priority Settings

Priority makes sense only in singular.

  • Be aware of:
    • The paradox of success: a team member can deliver great results given the proper workload
    • Trade-offs:
Undisciplined Pursuit of More Pursuit of Better
Everything Right thing
Popular Right Reason
Now Right Time
  • What is important?
    • The can do anything but not everything approach.
    • Considering the team’s bandwith, understanding the trade-offs on which tasks must be finished first is key.
    • Pareto Principle: understand that 80% of user satisfaction will come from 20% of tasks
    • The law of diminishing returns

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