This chapter takes an in-depth look into the reason why a data science project may not make it into production, focusing on five reasons that are systematic in nature, and providing examples of each. These reasons are: 1. The scope of the project is too big, 2. The project scope increased in size as the project progressed—e.g. scope creep, 3. The model couldn’t be explained, hence there was lack of trust in the solution, 4. The model was too complex, and 5. The project solved the wrong problem.