A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access…

In AI and machine learning projects, data is the most critical foundational asset. A forecasting project is entirely dependent on the quality and availability of historical data to train a useful model. Proceeding without access to the necessary data introduces a fatal risk to the project's success. The most prudent and professional action for a project manager is to halt the project until this critical dependency is resolved. This prevents the waste of time, budget, and resources on activities that cannot lead to a viable outcome. This approach aligns with risk management best practices by addressing a known, high-impact blocker before committing further resources.

A. This action prioritizes the schedule over project viability and quality, which is a critical project management error that will likely lead to project failure.

B. This misapplies the concept of an iterative approach. Iteration is for refining and building upon a working foundation, not for starting with a missing one.

D. While this option acknowledges risk, it still advocates for committing resources to a project with a known, fundamental flaw, which is an inefficient use of resources.

1. Project Management Institute. (2024). The Project Manager's Guide to AI. Newtown Square, PA: Project Management Institute. In its discussion of the AI project lifecycle, the guide emphasizes the "Data Sourcing and Preparation" phase as a critical prerequisite for the "Model Training and Tuning" phase. It states that without adequate data, the project cannot proceed, making data access a go/no-go decision point.

2. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS Inc. The CRISP-DM methodology, a standard for data-driven projects, outlines a sequential dependency. The "Data Understanding" and "Data Preparation" phases (Sections 2.2 and 2.3) must be substantially completed before the "Modeling" phase (Section 2.4) can begin. A lack of necessary data fundamentally blocks this progression.

3. Saltz, J. S., & Dewar, N. (2019). "Data science project management: A review and future research directions." Proceedings of the 52nd Hawaii International Conference on System Sciences. This academic review identifies "data acquisition" and "data availability" as primary challenges and critical early-stage activities in data science projects. The literature confirms that failure to secure necessary data is a common reason for project stalls or failure. (DOI: 10.24251/HICSS.2019.131).