I think that it is important to emphasize with investors if necessary, often the importance of sticking with a company no matter what conflicts may arise throughout the development process because that is where the growth and relationship comes from otherwise yes they're going to have investments and they're going to have companies that they invest in and make money with quickly but that's just a quick return we're looking for companies that want relationships and a consistent growth opportunity this base the relationship strongly on trust as well and will guarantee a long and successful business relationship will evolve as a result. This is where I would find common ground no matter what situation it would work well too.

Research should produces areas of discussion, hence why market research in conducted, conflicting views from a stakeholder are par of the course, you often find they come from an internal person who was not part of the initial brief setting, and now receiving insight into their area of the business. I would advise in all cases to go back over the initial brief, methodology and what was required prior to delivering the overall output. Any challenges to the data should be faced with professionalism, along with robust examples of where the data source has come from, so they know you are talking to the right audience.

The first point would be to get all the stakeholders in the room and explain the various different interpretations of data. How each one sees things and why. Common ground will thus be established through: 1.0 Reviewing the raw data and methodology together to ensure everyone is working with the same baseline 2.0 Asking each stakeholder to explain their interpretation and what led them to the conclusion. 3.0 Identify what everyone can agree on regarding the analysis before touching on areas of disagreement.

things we might consider when we face conflicting view in research data are: - Data uncertainty. very often, we compare two data (from different sources) of the same entity without describing their uncertainty. without uncertainty, we can't confidently say if the data are significantly different. - Instrument calibration. We must make sure that all instruments and methods to collect the data are calibrated and follow a standardized procedure. random procedures means random results. - Operator skills. We need to make sure that operators who take the data should be competent (properly trained) and have enough experiences. - Analysis method. We need to look at the analysis method, different analysis may give different perspective on data.