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This essay sheds light on what is predictive analytics, demystifies how to implement predictive analytics, delineates the benefits of companies leveraging predictive analytics, and expounds upon the problems with companies not implementing predictive analytics. Succinctly stated, predictive analytics is deemed to be "a type of data analytics" that refers to the practice of harnessing "statistical techniques and modeling techniques to forecast future outcomes. Statistical techniques and modeling techniques" can be leveraged to not only ascertain patterns in the data, but to also extrapolate future patterns in the data. Engaging in predictive analytics can render it possible for companies to forecast their future performance. Companies should aim to forecast their sales volume, sales revenue, and net income to better inform their strategic planning decision-making. Engaging in sales forecasting can render companies at a higher probability to better manage their inventory levels, research and development budgets, and sales personnel. Engaging in predictive analytics can be an act of prudence since it can render it possible for companies to forecast future outcomes at a high level of accuracy in contexts in which the data that is meticulously analyzed by predictive models to generate forecasts is characterized by validity and reliability. Even though engaging in predictive analytics can render it possible for companies to forecast future outcomes at a high level of accuracy, there is, however, no guarantee that the future outcomes elicited from engaging in predictive analytics will be actualized in the pending future. This is because the future is enigmatic and obscured behind a penumbra of time. Engaging in predictive analytics can demystify insights appertaining to patterns in the data. Furthermore, engaging in predictive analytics can also provide insights into the likelihood that past patterns will reoccur.