This document is part of a series of white papers that introduces a framework referred to has: AUTOMATING INTELLIGENCE. The framework is a pragmatic methodology for operationalizing analytics by looking at the processes, applications, and data architecture considerations necessary to build successful solutions in this domain. It is positioned as a journey and not all components need to fit into all use cases, but generally the framework provides a useful set of recommendations from which a series of guiding principles could be followed when trying to make analytics actionable. The framework is organized into three categories:
- Industrialized Analytics – the ability to take previous human interactions and use these to determine how current interactions should be defined and executed to encourage the necessary outcomes in an automated fashion,
- Multi-Dimensional Personalization – strategies, techniques, and solutions for making non-human interactions personal, and
- Data Management Solutions for Analytics – the architectural strategy to deal with all the data, analytical, and integration requirements necessary to operationalize analytics.
This installment of the Automating Intelligence series deals with the first component of this framework, Industrialized Analytics. It does so by providing background on how the strategy was developed, the various analytical methods involved, how these methods map to an overall analytical process, and finally, how these factors fit into the overall context of the Automating Intelligence framework. Where applicable, real-world examples that outline how each relevant concept works are also provided.