Big data can be problematic when it comes to misinterpretation by marketers, data algorithms used by digital marketers may draw false conclusions about individuals, which can lead to discomfort or privacy matters. There were a lot of reliable statistics and a failure to examine data which could lead to risky conclusions. In the article, big data can sometimes be biased and missing content based on convenience subsets. This can result in false conclusions in data. This emphasizes the importance of understanding big data and not trusting unreliable statistics. Relying on datasets can lead to inaccurate information, missing inputs, and inconsistent factors. Large datasets can sense unwanted information from individuals and lead to consequences with privacy issues. Also, trends change rapidly over time, and failing to keep up can result in irrelevant conclusions. It's important to incorporate other methods like expert research, qualitative and quantitative research, and experimental conduct. We should not put our faith in trusting all forms of big data. The article emphasizes the need to thoroughly look at the information instead of blindly trusting the statistics which can be inaccurate. Data is a tool, and to never give up common sense. The issues and inadequacies of big data, such as biased content, miscalculations, important examination, and miscommunication. Trusting different forms of big data is not reliable. In conclusion, big data offers innovation potential, blindly trusting forms of data is unwarranted. The important part is to thoroughly evaluate quality, be skeptical of statistics, and consider using human judgment based on decision$-$making processes.