In my last two blogs I outlined the elements of business intelligence platforms according to Gartner’s 2013 Magic Quadrant for Business Intelligence and Analytics Platforms. The first two categories were Integration and Information Delivery. The final category is Analysis, what a business intelligence system does with data to create information.
Online analytical processing (OLAP) – Frequently referred to as “cube analysis”, OLAP is a technique for organizing and analyzing data structured in multiple dimensions. OLAP offers the capability to aggregate, drill down, and “slice and dice” data while simultaneously attempting to optimize the speed of calculations and queries.
Interactive visualization – Moving beyond the rows and columns of tabular reporting, interactive visualization or data visualization provides users with a graphic representation of data. The interactive aspect of such analysis could take the form of drill down to more granular levels of data detail.
Predictive modeling and data mining – In predictive modeling, the probability of an outcome is estimated within the boundaries of a given set of data. In most cases, analysts have a good sense of the question to be answered and are looking for data to support a decision. Data mining is a process of looking for previously unknown patterns, or cause-effect relationships within large datasets.
Scorecards – Organizations identify important metrics relating to specific organizational goals. The metrics are then arranged in a format in which they can be viewed together (typically a dashboard) to provide all relevant information to decision makers.
Prescriptive modeling, simulation and optimization – This family of analytical techniques generally focuses on determining an optimal value from analysis of data using a pre-determined equation or process.