Business administration class of discussion board comments
First, it is essential to identify what type of data you are working with in order to properly assess its reliability. For example, if the collected data relies heavily on self-reported information such as surveys, this could lead to issues of response bias or exaggeration due to social pressures. Likewise, if the method used contains flawed assumptions or is outdated then it should not be trusted as reliable either.
Once any potential problems have been identified it is important to try and compensate for them in your analysis by using additional sources of evidence where possible; this could include seeking out alternative data sets or triangulating information through different methods wherever feasible. It may also help to add context around the source of the data when presenting your results so future audiences can make their own assessments about reliability.
If all else fails then transparency about what was found during analysis should always be provided regardless of how good (or bad) the outcome may have been – this will allow others to evaluate whether there were indeed any significant inaccuracies present within that particular dataset which can help inform future decisions related to similar projects moving forward. Ultimately though, taking these steps will hopefully ensure a more reliable set of outputs overall which will prove beneficial in both short term decision making and long term planning alike.