Big Data, which many companies, fondly depend on for their decision-making efforts, fails them by the very virtue of its volume and scope. It is a matter of wonder that almost 80% of the big data projects. Companies that passionately adapt to looking at big data in the best hopes of making data-driven decisions almost do not see any major results from the endeavor. A Harvard University review states that around 99% of businesses it has surveyed reported that they intend to implement big data analytics and AI in the future but only around 30% have succeeded. When dealing with big data, there is not one but a host of concerns from predicting customer behavior to data archiving and monitoring, which equally need attention for successfully implementing a big data project. Though all companies cannot afford to monitor every parameter with gusto, there are some ground rules a company can follow to derive maximum output from their data science projects. They are: