Many organization are moving towards becoming a data-driven organisations, but it can be costly or dangerous when something that appears to be true is not actually true. Even with the best of intentions, some of the world's most famous companies are challenged by skewed results because the data is biased, or the humans collecting and analyzing data are biased, or both. Data Analyst and Data Scientists recognize that big data analytics includes various forms of biases and their effects on analytical results and conclusions. However, business leaders are less likely to know the details of specific bias types, but they likely have experienced the effects of bias firsthand when a project or initiative did not yield the expected results. Flawed data analysis may leads to faulty conclusions and bad business outcomes. As such, this paper examines the various types of biases and errors that arise in big data projects, how and why biases occur, and their implications. It concludes by providing strategies for dealing with these biases in big data projects.