In the context of faceted search and discovery solutions, “analytics” is often used to refer to the presentation of and interaction with quantitative facets or attributes, which are frequently derived or “calculated” (e.g. business metrics).  Faceted analytics applications allow end users to make better decisions by maximizing visibility and enabling dynamic interactive exploration of relevant quantitative information (often in conjunction with related qualitative information). They support progressive, serendipitous discovery by allowing users to address the unpredictable, cascading questions that emerge as they interact with complex quantitative and qualitative information sets.

Faceted Analytics applications summarize important metrics and trends and aggregate key quantitative and qualitative information sources, providing visibility and information scent through facet driven visualizations (e.g., dynamic charts, graphs, etc.), metrics tables, faceted navigation elements, and other analytic summaries.   These summaries enable users to readily examine important patterns and indicators, and quickly identify unexpected and important events, outcomes, and trends that require further investigation or action.  In addition, these applications enable ad hoc, intuitive, flexible , facet driven (i.e., “agile) exploration and investigation of metrics, patterns, trends, and data in response to users’ emerging “cascading questions” in near real time without requiring extensive training or familiarization with the underlying system or data structures or expert analytics assistance (e.g. “report” generation).

In this section, we provide guidance on some of the key principles underlying effective faceted analytic applications.