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What is Embedded Analytics?

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.


Embedded Analytics Maturity ModelFrom 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.

How?

Embedded analytics integrates intelligence and visualization inside your applications and workflows for a more proactive analytical and visual experience through:

  • Interactive reporting in the form of tables with filters and scheduling options.
  • Charts and dashboards for displaying performance metrics.
  • Mobile reporting for better functionality on mobile devices.
  • Comparing current performance metrics and best practices for benchmarking.
  • Ad-hoc querying and self-service to explore datasets and create custom reports and dashboards.
  • Creating visual workflows using a drag-and-drop interface.

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.


Example of embedded analytics dashboardInteractive 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.

Why is embedded analytics important for business?

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:

  • Discover and prepare data, and build interactive reports, dashboards, and visualizations.
  • Provide a better user experience by showing customized content.
  • Make strategic business decisions based on the analysis.

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.

Benefits of embedded analytics

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.

  • Competitive advantage: Embedded analytics has expanded its wings to many domains today. For example, adding tools and plugins to websites helps businesses understand the usage patterns of their customers and suggest similar content. This means increased personalization and more potential customers.
  • Data-driven strategic decisions: As the number of people who use digital media for educational and transaction purposes is increasing, it makes sense to make use of all of that data in the best way possible. With embedded analytics, you don't have to go outside the application. All the insights you need are in one view for you to make quick strategic decisions.
  • Increased sales and profits: The most common example of this is product recommendations and customized offers and discounts. Embedded analytics also enables real-time monitoring, allowing, for example, support teams to better understand the difficulties faced by customers in placing their order, or if they have items left in their cart, and so on.
  • Get more value from data: Having analytics embedded into existing applications gives users in-context data insights within the existing platforms, rather than learning a new analytics platform. Also, most embedded analytics tools provide self-service intelligence capabilities, thus making life easier for developers and analysts.

Embedded analytics platform capabilities

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:

  • Data visualizations and dashboards: Users can get data from any source, group data, and filter, aggregate, and create custom charts. All the charts and graphs can be added in a single dashboard, which makes it easy for monitoring performance and gathering the necessary insights.
  • Reporting: Users can get static reports for historic data or dynamic reports for transactional data, helping organizations view required data in an organized manner whenever they need.
  • Self-service: Apart from dashboards, visualization, and reporting, some platforms offer ad-hoc reporting, mashup, and full self-service capabilities. This allows users to create personalized reports without much assistance, and also gives them a sense of independence.
  • Visual workflows: Many embedded analytics tools have an intuitive interface, like a drag-and-drop facility, which makes it easy for business analysts. Some tools, like MongoDB Charts, allow you to embed analytics into applications using iframe or JavaScript SDK by generating the code for embedding. This means the developers can focus more on the application logic than worrying about embedding analytics.
  • Real-time insights: Access to fresh data gives the most accurate insights, enabling businesses to make the right decisions at the right time.
  • Collaboration and data safety: Rather than copying and pasting data and insights into other applications, users can collaborate over data and insights in a governed platform. This keeps data safe and accessible only to the required persons.

Embedded Analytics Platform Capabilities

Core capabilities of embedded analytics platforms

Use cases for embedded analytics

Some of the effective embedded analytics use cases are:

  • Consolidating data from multiple sources, and integrating and filtering data to get the desired reports and insights—for example, event management systems and patient health records.
  • Better visualizations and immediate access to fresh (real-time) data, enabling quick and informed decision making. Examples are accounting services, failed transactions, and fraud detection.
  • Intuitive dashboards with desktop and mobile access allowing users to drill down data from anywhere, at any point, visualize and create reports on the go, and collaborate easily. Examples are financial services and team collaboration.
  • Increased efficiency, reduced time to market for products, increased user adoption for analytics, fewer errors, and intuitive UI, allowing for faster release of products and services—for example, new features on a website and developing a new product using agile methodologies.

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:

  • Safely store all the data collected from multiple sources, apply advanced filtering, and visualize data using MongoDB Charts all inside one frame.
  • Build fully custom embedded analytics applications.

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.

FAQ

What is embedded analytics?

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.

Why is embedded analytics important for business?

Embedded analytics is important because it allows businesses to generate actionable insights in real-time, which enhances business productivity and reduces time to market for products and services. Embedded analytics also saves a lot of time, cost, and resources because of its intuitive user interface and ability to integrate data sources and suggest valuable insights.

What is embedded analytics software?

Some examples of embedded analytics software are modern platforms like MongoDB, Tableau, and Sisense, which integrate analytical capabilities and visualizations into the enterprise application itself. Embedded analytics software has many capabilities, including intuitive UI, dynamic dashboards, and real-time insights.

What are the applications of data analytics?

Data analytics finds applications in many domains like healthcare, retail, education, finance, banking, logistics, and supply chain management. Some typical applications are:

  • Fraud and risk detection.
  • Product recommendation and customer segment analysis.
  • Predicting illness, discovering new potential diseases, and patient health monitoring.
  • Cost-effective data transportation and live tracking.
  • Energy optimization, distribution, and management.