Embedded Analytics

What is Embedded Analytics?

Embedded analytics software delivers real-time reporting, interactive data visualization and/or advanced analytics, including machine learning, directly into an enterprise business application. The data is managed by an analytics platform, and the visualizations and reports are placed directly within the application user interface (UI) to improve the context and usability of the data for business users.

What is the Difference Between Embedded Analytics
and Traditional BI?

In contrast to traditional BI, which requires users to leave their workflow applications to look at data insights in a separate set of tools, embedded analytics lets users view data visualizations or dashboards in context—while in the application itself. This immediacy makes embedded analytics much more intuitive and likely to be viewed by users.

A December 2016 report from Nucleus Research found that using BI tools, which require toggling between applications, can take up as much as 1-2 hours of an employee’s time each week, whereas embedded analytics eliminate the need to toggle between apps.

What is a Modern Embedded Analytics Platform?

Not all analytic products have been designed to be embedded. Many of today’s embedded analytics and BI vendors built their standalone applications first so they are not purposely designed to be embedded into applications.

Modern embedded analytics platforms don’t deliver a set of monolithic tools. Instead, they support a full stack of integrated analytic functions — from reporting and dashboards to self-service analytics, alerts, collaboration, data preparation and machine learning on a unified, scalable architecture with common administrative and management functions. And unlike more restricted analytics platforms of the past that limited what users could do, newer embedded platforms give end users the freedom to edit visualizations or dashboards or to create their own.

Also, they are designed from the ground up for the web, cloud and mobile delivery. Modern embedded analytics platforms also make it easier for developers to create custom analytic applications.

What are the Key Elements of Embedded Analytics?

These are some of the key functions that are included with embedded analytics software:


Dashboards are tools that visually display data patterns for analysis, presentation and easy understanding. Dashboards can consist of pie graphs or charts, bar or line graphs, scatter plots, color-coded maps, or any other kind of visual data presentations.

Key features include:

Drill-down takes a user from general overviews to more detailed analysis with a single click.

Reveal embedded analytics dashboard for manufacturing productivity.

Data Connectors

Modern embedded analytics software lets users connect seamlessly to many different data sources and then combine these data in one place for comprehensive analysis. Data sources may include Azure Synapse, Google BigQuery, Box, Sharepoint, Google Drive, One Drive, Microsoft Analysis Services, Microsoft SQL Server, CRM, and many more.

Some embedded analytics vendors also offer a feature called “in-memory data source”, which lets users directly connect to a data source that the software doesn’t support out of the box.


Visualizations refer to a range of chart types and the best embedded analytics software lets you choose from many pre-built templates. These range from column, doughnut, and funnel to bubble, scatter or sparkline charts, to more advanced ones such as tree map or geospatial mapping. Users can also combine these various visualizations to make a beautiful integrated dashboard.

Reveal embedded analytics visualizations types.

What are Embedded Analytics Statistical Functions?

Wikipedia defines statistics as the study of the collection, analysis, interpretation, presentation, and organization of data. In terms of data analytics, this can include key statistical functions such as outliers detection, time series forecasting and linear regression, as well as the ability to embed these interactions into visualizations or allow features such as dashboard drill-downs and dashboard linking.

Outliers Detection

Outliers Detection lets users easily detect points in their data that are anomalies and differ from much of a data set. They can show or hide these outliers from view, so they’re always showing or so they don’t interfere with an analysis.

Time Series Forecasting

Using Time Series Forecasting, users can make predictions on future values based on historical data and trends. This is useful in any number of applications, such as sales and revenue forecasting, inventory management, and many others.

Reveal embedded analytics time series forecasting for new vs renewal sales.

Linear Regression

Linear Regression lets users visually see trends in their data by finding the relationship between two variables and seeing a linear approximation of the data – including future trends. Along with Linear Regression, other algorithm trend lines include Linear Fit, Quadratic Fit, Cubic Fit, Quartic Fit, Logarithmic Fit, Exponential Fit, Power Law Fit, Simple Average, Exponential Average, Modified Average, Cumulative Average, and Weighted Average.

Examples of Embedded Analytics

A recent 2019 survey report produced by Infragistics found that the most popular applications that development teams were either actively embedding analytics into or were planning to do so soon are shown in the graphic below:

Reveal embedded analytics report depicting top reasons why companies opt to embed

Some leading vertical sectors include:

Financial – Allows users at financial companies to aggregate vast volumes of data about borrowers for benchmarking and to better assess risk through intuitive visual dashboards that can be sliced, diced, and explored to granular levels.

Healthcare – Hospitals, doctors groups, and other healthcare groups use embedded analytics to Improve performance by delivering data-based quality care. They’ve been able to:

Manufacturing – A plant floor manager is responsible for a manufacturing plant’s entire production process, from when raw materials enter the plant to when the product exits the plant for distribution. The plant manager could use data analytics to see operational KPIs related to how the plant is performing, such as: