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Decision Trees: Business Intelligence, Data Latency, and Data Mining

The book begins with an overview of decision trees and discusses how decision trees are related to other concepts such as decision theory, optimization, and statistical inference. The author then explores the use of decision trees in data mining tasks and describes how these techniques complement and supplement other business intelligence applications, such as dimensional cubes (also called OLAP cubes) and data mining approaches, such as regression, cluster analysis, and neural networks. The author then demonstrates how decision trees can be used to solve real-business problems by applying them to scenarios in manufacturing, sales forecasting, CRM applications and marketing analytics.

Mobile Business Intelligence Trends

BI technologies allow direct access to data and information. With the help of these technologies, a business can make better decisions upon analyzing big data. They are used widely in data-centered warehouses or data marts and can help to achieve various business outcomes by integrating big data analytics with traditional BI systems. Mobile BI is one of the effective ways to use the massive amount of data. Mobile BI helps businesses deploy data analysis methods to collect, analyze and segment data. It makes business intelligence accessible everywhere and at all times, thereby enabling organizations to make decisions based on analytics-driven insights. According to a report by Datameer, mobile applications will be the second-largest technology integrating Business Intelligence and Big Data. Also, Gartner reports that over 50% of Mobile BI users will depend on mobile devices for insight delivery. Companies such as SAS Business Analytics are already positioning themselves as “mobile-first” companies through a diverse range of services and offerings.