Project Objective#

Once the problem is identified with clarity, the next crucial step in the data science lifecycle is the determination of project objectives. These objectives act as the guiding principles that shape the direction of the entire project. Let’s explore the key aspects of defining project objectives:

SMART Criteria Alignment

Project objectives must align with the SMART criteria - Specific, Measurable, Achievable, Relevant, and Time-bound. This alignment ensures that the objectives are not vague or overly ambitious, making them practical and attainable within the project’s constraints.

  • Specific: The objective is clear

  • Measurable: The reduction in prediction error is quantifiable through metrics

  • Achievable: While aiming for a substantial reduction in prediction error, the objectives are realistic and feasible within the scope of the project.

  • Relevant: The objectives directly contribute to the overarching goal of improving the accuracy for better decision-making over time

  • Time-bound: The project timeline is designed to strike a balance between thorough analysis and efficient resource utilization

Granular Objectives

Breaking down the overarching objective into granular components provides a clearer roadmap for the project.

In our example: comparison of Models for comparing the performance of spatial and non-spatial predictive models to identify the most accurate approach for price prediction, and Neighborhood-level Accuracy for assessing the accuracy of predictions at the neighborhood level within Madrid, aiming for a narrow margin of ±5% of the actual property prices for each neighborhood.

Alignment with Business Goals and Communication of Objectives:

Project objectives should closely align with broader business goals or specific contextual requirements. On the other hand, clear communication of objectives to all stakeholders, including the data science team, business decision-makers, and domain experts, is vital. This ensures a shared understanding of the project’s purpose and expected outcomes.

Continuous Evaluation

Objectives are not set in stone. Regular evaluation and, if necessary, adjustment of objectives ensure that the project remains aligned with evolving business needs and technological advancements.