Research and knowledge dissemination is not the place for politics and opinions. Research and data are factual pieces of information that should be utilized to reveal the truth about a given subject. The difficulty lies in the ability to remove expectations and beliefs from the collection and analysis of data. This feat is a difficult undertaking.
Since – at times – it is challenging to identify personal bias (and near impossible to remove politics from the current attempts at research), it is a good idea to have seemingly neutral parties provide feedback – as well as admittedly biased parties from opposing sides. Since opinions can’t be kept out of research, we should use them. Feedback with the intent to support a desired outcome from opposing sides can uncover bias from each agenda and highlight more modalities to assure neutrality – much like the principals of yin and yang. In this way, it is also possible to identify data skewing through data analysis.
Today, this happens through agendas. Groups present data to support their intentions. Whether these intentions are viewed as good or bad, the opposing data should be compared using methods of analysis. What are the variables used? Have all variables been considered? Is the data reliable? What formulas have been used? Who funded the research? What is the apparent purpose of this research? Ideally, research could be conducted in a joint effort to uncover the truth rather than create propaganda for a particular end. Place these sorts of studies side by side to bring bias to the surface – or to bring your own bias to the surface.
Applying these ideas to a corporate or organizational structure, even departments within the same organization can differ in analysis of the same data. In efforts to avoid this, data should be collected and managed centrally using standardized data methodology. Once the data has been collected, they can be accessed and manipulated independently of the organizational data hub. This sort of data distribution can eliminate some discrepancies; yet, still allow departments convey their own brand of algorithmic analysis for review. The remaining discrepancies can be further analyzed when necessary. By using this methodology, organizations can also avoid the compartmentalization, gate-keeping, and isolation of data access that is created through internal competition and departmental objectives.