When the team is divided on data interpretations, start by encouraging open discussion where everyone can share their views. Bring the focus back to the core goals of the analysis and how each interpretation supports those objectives. Look at the data together, testing different perspectives to see which one is best supported by the facts. If needed, involve an unbiased third party to offer a fresh take. Encourage decisions based on data rather than opinions, and try to quantify assumptions. If both sides have value, consider combining insights or running experiments to see which approach works better. Throughout, emphasize collaboration, reminding the team that you're all working toward the same goal.

When dealing with conflicting data interpretations in healthcare, focus on the following strategies: 1. Emphasize patient impact: Shift the conversation to how each interpretation affects patient outcomes or care strategies. 2. Leverage case studies: Reference past projects where similar conflicts were resolved, showcasing successful approaches. 3. Quantify differences: Break down the conflicting viewpoints into measurable components to clarify divergence. 4. Run scenario analyses: Simulate outcomes based on each interpretation to visualize potential impacts. 5. Agree on key metrics: Establish shared performance indicators to ground discussions in common priorities.