Sua equipe está enfrentando discrepâncias na análise de dados. Como você pode resolvê-los sem criar tensão?

Make conversations about getting the right outcomes for your stakeholders and the learning experience that comes from understanding the data and analysis approach. The right culture has a big impact on effectively resolving discrepancies. Shift perspectives from justification and blame, to focusing on what you're trying to achieve for your stakeholders. I've found this reframe reduces the time to quality insights as people are more interested in getting the right answer, rather than being "right". Collaboration improves when people are focused on their customer outcomes and sharing the learnings that come from understanding the discrepancies. It's this joint investigation and sharing process that builds trust and strong relationships.

Addressing data analysis discrepancies requires a collaborative approach where team members can openly discuss findings and potential solutions. Encouraging a culture of constructive feedback and regular team meetings can help pinpoint the root causes without attributing blame. By focusing on improving processes and clarifying expectations, we can navigate these challenges effectively and foster a supportive environment for continuous improvement.

To resolve data discrepancies without tension, foster open communication and collaboration. Approach the issue with a problem-solving attitude, focusing on facts. Hold a meeting to review data together, encourage sharing perspectives, and confirm understanding. Use objective criteria and, if needed, seek an impartial third party. Agree on steps to prevent future issues and reinforce teamwork and continuous improvement.

In any data shop, it is essential to make sure that everyone is on the same page in terms of knowing how the data is collected, structured, and coded (e.g, maintaining updated data dictionaries/code books, copies of all codes, and clearly written methodologies). Also, reviewing and replicating one another's work is a good practice. In my experience, discrepancies occur when data is manipulated (e.g., combining data, recoding, creating composite measures, removing missing cases, etc.). When team members keep meticulous records of these types of manipulations, it is much easier to spot and rectify the problems.

I completely agree with this point. Team members should be able to openly discuss their rationale and processes in a collegial manner without fear of reprisals. Anyone who has studied statistics will tell you that it is not that cut and dry, meaning that there are disagreements among the experts on how to handle specific types of data, questions, and problems. It is good for team members to discuss these differing perspectives and which approach makes the most sense for the task at hand. More often than not, real world data is messier than what is discussed in the textbooks.