Data science projects often involve developing machine learning (ML) models to unravel business problems. While this may occasionally seem commonplace in business today, it still comes with several risks.

Namely, developing ML models is inherently uncertain, technically demanding, expensive, and time-consuming. These risks motivate project management frameworks specifically designed for data science projects in mind.

Here, I’ll describe one such approach and break down the important thing contributions of a project manager on this context.

The approach I wish to use for data science projects is printed by the 5-step framework illustrated below.

Digging deeper, listed here are a number of key activities for every phase.

• Phase 0: Problem Definition & Scoping — Formulate the business problem. Design the information science solution. Define project milestones, tasks, and success metrics. Key role: Project Manager
• Phase 1: Data Acquisition, Exploration, & Preparation — Evaluate available data. Acquire and explore data. Develop data pipelines. Key roles: Data Engineer, Data Scientist
• Phase 2: Solution Development — Develop ML solution. Evaluate solution validity and value. Iterate with stakeholders and revisit past phases as needed. Key role: Data scientist
• Phase 3: Solution Deployment — Integrate solution into real-world business context. Develop solution monitoring pipeline. Key roles: ML Engineer, Data Scientist
• Phase 4: Evaluation & Documentation — Evaluate project outcomes. Deliver technical documentation and user guides. Reflect on lessons learned and future work. Key role: Project Manager

A very important point here is that data science projects often don’t progress linearly through each of those phases. Reasonably, some amount of iteration is required through key feedback loops. Listed here are a number of examples of what this might appear to be.

• Phase 1 → Phase 0: When exploring the available data, it becomes clear that key information will not be available, and the project plan have to be revisited.
• Phase 2 → Phase 1: After training a handful of models, it’s discovered that an exception was not properly handled in data preparation.
• Phase 2 → Phase 0: Preliminary models don’t reveal strong predictive performance, which requires reevaluating the project’s value.
• Phase 4 → Phase 0: Every project has its opportunities for improvement. Upon completion, teams can evaluate these opportunities and kick off one other project, starting with Phase 0.

The project manager (PM) is ultimately chargeable for a project’s success. If the project is late, it’s on the PM. If costs exceed estimates, it’s on the PM. If the worth doesn’t meet expectations, it’s on the PM.

While this responsibility involves a various range of tasks from multiple contributors, one key determinant of a project’s success is the PM’s execution of Phase 0 (as described above).

Phase 0 sets the inspiration of a knowledge science project. Just as a poorly constructed foundation will end in a difficult construction project, a poorly executed Phase 0 will end in a difficult data science project.

The three key elements of Phase 0 include Problem Diagnosis, Solution Design, and Implementation Plan [1].

1) Problem Diagnosis

Of the three elements, that is probably the most critical because in case you get this mistaken, you may spend quite a lot of money and time solving the mistaken problem (i.e., little value is generated). Despite its importance, many are likely to gloss over (if not skip entirely), taking the time to stop and think in regards to the business problem.

Just as a health care provider interviews a patient to provide a diagnosis, a PM interviews stakeholders to higher understand the business problem and discover the foundation cause. Although there are various ways to do that, I wish to keep things easy and concentrate on asking two key questions.

1. What problem are you trying to unravel? — that is all the time one of the best start line for these conversations [1]
2. Why is that vital to the business? — this may kick off a series of 5 why-based inquiries to get to the issue’s root cause (see Toyota’s 5 Why’s approach) [2]

One in all the PM’s most vital skills is effectively collaborating with stakeholders to grasp their problems. I discuss this further in a past article.

2) Solution Design

Once the business problem is clearly understood, the subsequent step is to define the best way to solve it. Various solutions at various levels of complexity can address any given problem.

As an example, if customer churn is high attributable to a slow onboarding process, some potential solutions could possibly be removing unnecessary onboarding steps, analyzing where drop-off occurs and transforming that step, customizing onboarding based on customer information, etc. Notice that these solutions may not require machine learning (and that’s okay).

Suppose, after extensive back-and-forth, the stakeholder wants to maneuver forward with developing a personalised onboarding experience based on customer profiles. While this narrows things down, this solution can still be implemented in some ways. Due to this fact, the PM must use their judgment to propose an answer based on stakeholder conversations, similar industry projects, and available resources.

3) Implementation Plan

The ultimate element of Phase 0 is translating the proposed solution right into a concrete project implementation plan. This plan consists of two key pieces: a project roadmap and the project requirements.

A project roadmap consists of key project milestones. I wish to base these milestones on Phases 1–4, as described above. Each phase consists of tasks assigned to a specific role (e.g., data engineer, data scientist, or ML engineer) and a due date [1].

Project requirements specify all of the vital resources for implementation, including data requirements, key roles, software tools, and compute infrastructure.

I’ll walk through Phase 0 for an example case study to solidify these ideas. While this is supposed to be instructive, it’s an actual project I’ll implement (and document) in future articles of this series.

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