Businesses need analytics that can give more actionable insights in real-time—for example, an instant alert message when the number of users accessing your website reaches one million.
Traditional BI was great for static dashboards and reporting. However, satisfying modern business requirements required a more interactive approach, which led to the popularity of self-service BI. Over time, analytics matured drastically from being just a basic reporting component, a standalone reporting and dashboard component, to an infused (embedded) component into the application workflow itself.
From separate workflows to analytics integrated in the application itself, embedded analytics has come a long way.
Embedded analytics lets you view data analytics inside of an enterprise business application through interactive dashboards and reports, removing the need to switch to a separate application.
Embedded analytics integrates intelligence and visualization inside your applications and workflows for a more proactive analytical and visual experience through:
There are many ways in which reports and dashboards can be integrated into an application—for example, via an iframe, API, or a plugin. Modern data platforms like MongoDB support highly valuable embedded analytics.
An example of embedded metrics is the Atlas UI. In the following image, you can see how users can get real-time information about their cluster status metrics—like reads, writes, connections, and data size—inside the Atlas interface itself.
Interactive dashboard that provides real-time insights, performance metrics and much more in the application
With this kind of real-time monitoring, you have more chances to quickly rectify any glitches and also offer a better user experience based on their behavior.
You can create a free Atlas cluster today to see it live.
With standalone or traditional analytics, application and analytics are in a different environment, and there need to be dedicated people with data-handling expertise. Embedded analytics gives users relevant and timely insights within their application workflow. Businesses are able to use the data more effectively for improving sales and service. Embedded analytics is important because of its capability to:
Embedded analytics provides self-service capabilities, customized UX elements, click events, and predictive analysis capabilities to business users.
With embedded analytics, data access can be restricted via custom applications, thus ensuring security.
Various industries—including financial services, technology, retail, manufacturing, healthcare, business services, and education—use embedded analytics for internal as well as commercial purposes. For example, a company’s IT department may use embedded analytics to improve operations, while a customer may use it to drive more website traffic and offer product discounts.
Embedded analytics has something to offer everyone—developers, business analysts, stakeholders, and even end-users. From an enhanced user experience to increased profits, you get a lot from embedded analytics.
Embedded analytics platforms have many capabilities. Advanced tools even support augmented analytics that mostly rely on artificial intelligence and automated analytics. Some common capabilities of embedded analytics platform are:
Core capabilities of embedded analytics platforms
Some of the effective embedded analytics use cases are:
Embedded analytics is finding applications in many domains like supply chain management, logistics and shipping, banking and finance, software and technology, and event management. Modern data platforms like MongoDB have a flexible schema and offer a powerful aggregation framework. This means you can:
With MongoDB Atlas, you can access updated data anywhere across the world over the cloud and get real-time insights to make strategic business decisions.
Embedded analytics enables analysts, developers, and stakeholders to integrate data from multiple sources, filter the required data, perform real-time analytics, and visualize insights through charts and dashboards. This all happens inside an enterprise application itself, rather than having a separate application for analytics. All of this can be done with an intuitive UI and without writing any extensive code, saving time, cost, and resources.
Data analytics finds applications in many domains like healthcare, retail, education, finance, banking, logistics, and supply chain management. Some typical applications are: