If You’re Managing Data Science Projects Like Engineering, You’re Setting Them Up to Fail

Many companies make the mistake of managing data science projects the same way they manage engineering projects. It seems logical on the surface—both fields involve technical problem-solving, right? But applying the same approach to two vastly different types of work often leads to frustration and failure.

Here’s why.

Data Science: Known Scope, Unknown Solution

Data science is all about experimentation and discovery. You often start with a clear question (like predicting customer churn), but the path to finding the solution is far from certain. What algorithm will work? Which features should you use? It’s a process of trial and error, testing hypotheses, and iterating on models. Because the solution is unknown, predicting timelines for data science projects can feel like throwing darts in the dark.

• Timelines are unpredictable: The process is non-linear, and it can take multiple iterations to arrive at something that works.
• It’s an exploratory process: Data scientists need room to experiment with different approaches, which makes it hard to define rigid deadlines upfront.

Engineering: Known Solution, Unknown Constraints

In contrast, engineering is more structured. Once the scope is defined, the solution is usually clear. You know what needs to be built, and while challenges (like scaling issues or system limitations) may arise, you can generally work through them because the overall process is well-understood.

• Timelines are clearer: Once the plan is in place, you can map out milestones and deliverables with a higher degree of confidence.
• Constraints are managed: Engineers focus on navigating known constraints within the framework of a defined solution.

Why Managing Data Science Like Engineering Sets You Up to Fail

When you try to manage data science projects with the same rigid timelines and expectations used in engineering, you’ll face several problems:

• Misaligned expectations: Stakeholders expect early deliverables or quick results, but data science often requires weeks or months of testing before arriving at meaningful insights.
• Resource misallocation: Data science needs flexible timelines to pivot when experiments don’t pan out. Locking down resources too early can waste time and effort on paths that don’t lead to viable outcomes.

How to Manage Data Science Differently

1. Embrace Flexibility: Data science projects need an iterative, agile approach. Set short-term goals for experimentation rather than locking into long-term deliverables.
2. Celebrate Discovery: Data science is about learning from the data. Even failed experiments can provide valuable insights. Recognize these intermediate wins.
3. Align Expectations Early: Be upfront with stakeholders about the uncertainty in data science. The solution will emerge, but it will take time and iteration.

Bottom line: Treating data science like engineering will lead to missed deadlines, frustrated teams, and failed projects. By adopting a flexible, discovery-driven approach, you’ll give your data science team the space they need to uncover meaningful insights and deliver real value.