7 Big Data Analytics Introduction Data that exist in very large volumes and many different varieties (data types) and that need to be processed at a very high velocity (speed).AnalyticsSystematic analysis and interpretation of data—typically using mathematical, statistical, and computational tools—to improve our understanding of a real-world domain.This chapter is primarily about these two new advances in data technologies and approaches.Traditional data management technologies were created to ensure accurate and efficient transaction processing. As we saw from chapter 9, later database structures were created to support decision-making and overall understanding of the business. We called these data warehouses. Big data and analytics takes us further down this road.
8 Figure 11-11 Generations of Business Intelligence and Analytics Adapted from Chen et al., 2012BI&A 1.0Focus on structured quantitative data largely from relational databasesBI&A 2.0Include data from the Web (web interaction logs, customer reviews, social media)BI&A 2.0Include data from mobile devices, (location, sensors, etc.) as well as Internet of ThingsBI&A has evolved along with other elements of information technology. It should be noted that there is far more unstructured and semistructured data (characteristic of Web and mobile technology) than there is structured data (typically found in relational databases). And although all data (structured and unstructured alike) is increasing in volume over time, the rate of growth is largest in the unstructured space.
9 Types of AnalyticsDescriptive analytics – describes the past status of the domain of interest using a variety of tools through techniques such as reporting, data visualization, dashboards, and scorecardsPredictive analytics – applies statistical and computational methods and models to data regarding past and current events to predict what might happen in the futurePrescriptive analytics –uses results of predictive analytics along with optimization and simulation tools to recommend actions that will lead to a desired outcomeWith descriptive analytics we ask what happened already (last week, last year, etc.). With predictive analytics we ask what’s going to happen in the future and how will it affect us. With prescriptive analytics, we ask what is the best decision to make.