Data analytics seeks to provide operational observations into issues that we either know we know or know we don’t know. Descriptive analytics, for example, quantitatively describes the main features of a collection of data. Predictive analytics, that focus on correlative analysis, predicts relationships between known random variables or sets of data in order to identify how an event will occur in the future. For example, identifying the where to sell personal power generators and the store locations as a function of future weather conditions (e.g., storms). While the weather may not have caused the buying behavior, it often strongly correlates to future sales.
The goal of Data Science, on-the-other-hand, is to provide strategic actionable insights into the world were we don’t know what we don’t know. For example, trying to identify a future technology that doesn’t exist today, but will have the most impact on an organization in the future. Predictive analytics in the area of causation, prescriptive analytics (predictive plus decision science), and machine learning are three primary means through which actionable insights can be found. Predictive causal analytics precisely identifies the cause for an event, take for example the title of a film’s impact on box office revenue. Prescriptive analytics couples decision science to predictive capabilities in order to identify actionable outcomes that directly impact a desired goal.