How do we ensure our Data Science projects actually solve the business problems they were built for?

All answers to this question.

The "Last Mile" problem is the silent killer of most projects. You need to start with the "Decision" first, not the data. In my experience back in 2023, we stopped asking "What can we predict?" and started asking "What decision is currently being made poorly?" Once you identify that, you build the model to support that specific choice. You should also create a "Shadow Phase" where the model runs in the background for a month. This allows you to compare the model's suggestions against the human experts' choices in real-time. This builds the trust necessary for full deployment.

   Answered 2025-03-16 by Cynthia Reynolds

Do you involve the end-users—the people who will actually use the model's output—during the initial feature engineering phase of the project?

   Answered 2025-03-18 by Jeffrey Hudson

• That is a vital step, Jeffrey. For a successful outcome, the domain experts need to be in the room. They know the "nuisance variables" that the math might miss. We once spent weeks on a supply chain model only to realize we ignored a local holiday that the warehouse managers knew was critical. By including them early, you don't just get better features; you get "internal champions" who will actually use the tool once it’s live, which is the only way to prove ROI.

     Commented 2025-03-19 by Gregory Marshall

Focus on "Interpretability" over "Complexity." A simple regression model that the manager understands is often better than a black-box neural network they don't trust.

   Answered 2025-03-20 by Bradley Fischer

• I totally agree with Bradley. Trust is the currency of data science. If the stakeholders can't explain why the model made a choice, they won't put money behind it.

     Commented 2025-03-21 by Kimberly Lawson