Embedded analytics is the integration of analytic content and capabilities within applications, such as business process applications (e.g., CRM, ERP, EHR/EMR) or portals (e.g., intranets or extranets). The goal is to help users work smarter by incorporating relevant data and analytics to solve high-value business problems and work more efficiently.
Embedded analytics differs from traditional business intelligence (BI), which focuses on extracting insight from data within the silo of analysis. While traditional BI has its place, the fact that BI applications and business process applications have entirely separate interfaces forces users to switch between multiple applications to derive insights and take action.
Instead, embedded analytics puts intelligence inside the applications people use every day. This improves the analytics experience and, in turn, makes users more productive by combining insight and action in the same application.
Said another way, business intelligence is a map that you utilize to plan your route before a long road trip. Embedded analytics is the GPS navigation inside your car that guides your path in real time.
Analytics may be embedded within business applications and workflows in several ways, each with varying levels of integration. The Analytics Maturity Model depicts these methods in four stages. The model begins with a standalone analytics application, where no embedding takes place, and ends with infused analytics, the deepest and most advanced form of embedding.
Deeper integration of analytics within applications is correlated to improving the user experience, increasing end user adoption, and differentiating the product.