Follow the data- "data driven decisions" is more than Marketing buzzwords. Safety is something to be taken very seriously, and when there are conflicting reports, it is an immediate flag to investigate further. If your data is the root cause for the "conflicting interpretations" then that should be a priority to be resolved. The question is irrelevant if a company genuinely values safety and has the data to support the delivery of a safe work environment. I would suggest if the safety data can be "misinterpreted" the definition of "data" and "safety" need to be further evaluated. #rootcauseanalysis # safetyfirst

Para resolver interpretações conflitantes ao analisar dados de segurança, é essencial manter a objetividade e promover uma discussão aberta entre os colegas. Estabeleça critérios claros e baseados em fatos para garantir uma análise consistente. Utilize ferramentas como análise de causa raiz e revisão do histórico de incidentes para esclarecer divergências. Se necessário, valide situações diferentes com novos dados ou consulte os padrões da indústria. O objetivo é buscar um consenso que promova uma interpretação mais sólida e precisa, garantindo maior segurança e tomada de decisões eficazes.

Turning debate into collaboration is a challenging, yet the only way to resolve conflicting views on safety data. 1) Active listening: Listen and understand the perspective and reasoning behind every colleague's interpretations, and document if required. 2) Data Clarification: Ensure that there is no misalignment with the information and data that all the colleagues have. 3) Analyze collectively: Use tools like Tableau or PowerBI to analyze the data from different angles collectively. 4) Shared Goals: Remind the team, that improving safety outcomes is the common shared objective, and to keep their perspective aligned with the objective. Create an action plan, on the grounds of common consensus achieved

Turning debate into collaboration in data analysis involves a few key strategies: 1. Clarify Goals and Assumptions: Ensure everyone agrees on the problem being addressed and the key metrics for success. Often, disagreements arise from misaligned objectives or different assumptions about the data. 2. Promote Data-Driven Discussions: Anchor discussions around the data itself. Encourage team members to present evidence for their views, helping to steer debates away from opinions or preferences. 3. Foster Open Communication: Encourage an environment where team members feel comfortable expressing their thoughts. Active listening can help diffuse tension and lead to a more collaborative exchange.