What do you mean by data validity? There is internal and external validity when it comes to data. This should not be a matter of opinion. There are techniques and processes to check for data validity. Insights are not the same as data. Could you stop confusing the terminology?

By addressing the validity concerns systematically, you can align the team on how to interpret the data, reduce disagreements, and ultimately ensure that the insights derived from the research are both accurate and actionable.

To ensure accurate insights when your team is divided on data validity, start by establishing clear criteria for what constitutes valid data. Conduct a data audit to verify sources and collection methods. Encourage open discussions to address concerns and perspectives. If needed, seek external validation from experts. Use consensus-building techniques like the Delphi method to reach agreement. Document all decisions and criteria. For example, if there's a dispute over survey data due to response rates, an audit and expert consultation can validate the survey's design, ensuring reliable insights.

From my experience, it's crucial to establish a framework that prioritize data validity and integrity. I believe your first approach will be to revisit the purpose of your research as this reduces the risk of biased interpretations. When this is established, you have to prioritize the credibility of your data sources and verify their accuracy through historical validation. Cross validate your findings to ensure a more comprehensive perspective for your team and employ an adequate sample size that represents the target market to improve the reliability of your results. Conduct thorough data cleaning, ensure collaboration through periodic team reviews and move your team from subjective interpretations to an evidence-based understanding.