The use of analytics that includes statistics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error. There is a requirement to gain insights, foresight, and inferences from the treasure chest of raw transactional data (both internal and external) that many organizations now store (and will continue to store) in a digital format. Organizations are drowning in data but starving for information. An experienced analyst is like a caddy for a professional golfer. The best ones do not limit their advice to factors such as distance, slope, and the weather but also strongly suggest which club to use. The power of analytics is to turn huge volumes of data into a much smaller amount of information and insight. BI mainly summarizes historical data, typically in table reports and graphs, as a means for queries and drill downs. But reports do not simplify data or amplify its value. They simply package up the data so it can be consumed. In contrast to BI, decisions provide context for what to analyze. Work backward with the end decision in mind. Identify the decisions that matter most to your organization, and model what leads to mak- ing those decisions. If the type of decision needed is understood, then the type of analysis and its required source data can be defined. Many believe that the use of BI software and creating cool graphs are the ultimate destination. BI is the shiny new toy of information technology. The reality is that much of what business intelligence software tools provide, as just described, has more to do with query and reporting, often by reformatting data. A common observation is: “There is no intelligence in business intelligence.” It is only when data mining and analytics are applied to BI within an organization that has the skills, competencies, and capabilities that deep insights and fore- sight are created to understand the solutions to problems and select actions for improving business operations and opportunities. Data mining that uses statistical methods is the foundation and precursor for predictive business analytics. For example, data mining can identify similar groups and segments (e.g., customers) through cluster or correlation analysis. This allows analysts to frame their analytics to predict how their objects of interest, such as customers, new medicines, new smartphones, and so on, are likely to behave in the future—with or without interventions. To clarify, BI consumes stored information. Analytics produces new information. Predictive business analytics leverages data within an organizational function focused on analytics and possessing the mandate, skills, and competencies to drive better decisions faster, and to achieve targeted performance.