What is Embedded Analytics?

Embedded Analytics refers to integrating analytical capabilities and data visualizations within business applications, enabling users to access insights and analytics without switching to separate analytics platforms. By embedding analytics directly within existing applications, users can make data-driven decisions in real time, enhancing efficiency, productivity, and overall business performance.

Embedded Analytics allows for a seamless user experience by providing analytics in the context of the user's workflow. It often encompasses a range of functionalities, including dashboards, reports, data visualizations, and other analytical tools, all accessible within the primary business applications being used. The goal is to provide users with the necessary insights and analytics at the decision-making point, reducing the time and effort required to switch between different platforms. This fosters a data-driven culture within organizations, as employees have immediate access to the analytics necessary to inform their decisions. Embedded Analytics can be applied across various departments and industries, enhancing operations, marketing, finance, and other business domains.

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Related Terms

RAG (Retrieval-Augmented Generation)

Retrieval-augmented generation (RAG) is an advanced artificial intelligence technique (Generative AI) that optimizes the output of large language models (LLMs)

LLMOps (Large Language Model Operations)

LLMOps refers to the set of practices and workflows dedicated to developing, deploying, and managing artificial intelligence (AI) models, specifically those based on large language models (LLMs).

Generative AI

Generative AI refers to a type of artificial intelligence that can create new data samples from the same distribution as a given set of training data.