What is embedded analytics?

How does embedded analytics work?

Many workers who use embedded analytics as part of their jobs might not recognize it as a separate capability. That's because, in most cases, embedded analytics is fully integrated into both the software and the workflow.

Yet getting to that level of seamlessness takes a lot of work by the organization, the technology team tasked with implementing the embedded analytics capabilities and the business users who will benefit from them.

An organization must identify those processes and use cases where embedded analytics capabilities will bring the most value. Then it must create and execute a strategy to ensure the following:

• The right amount of quality data is available to flow into the embedded analytics tool.
• The embedded analytics tool selected or developed for the use case is the appropriate one.
• Workers are trained to use the new analytics capabilities to inform their decision-making.

As part of that strategy, the technology team and its business unit counterparts need to determine which of the following actions they should take:

• Buy software that comes with embedded analytics capabilities.
• Buy embedded capabilities to integrate into the software being used.
• Build their own embedded capabilities to integrate into the software.

Embedded analytics capabilities work by taking data from the system where they're embedded and also pulling data -- typically via application programming interfaces -- from other systems, databases or data warehouses. Embedded analytics then analyzes the data to create insights that can be displayed in data visualizations, BI dashboards and reports.

Because they're embedded, the data collection, analysis and visualization happen as part of the processes enabled by the software that hosts the embedded analytics capabilities. That means users don't see the analytics as a separate component, but rather as part of the workflow or task they're performing.

To ensure embedded analytics delivers optimal value, the IT and BI teams deploying the technology should ensure users understand how to use the technology's capabilities as part of the processes or tasks they're performing. They should also ensure embedded analytics capabilities are truly integrated and seamless for users.

Potential benefits of using embedded analytics tools

Organizations can reap numerous benefits when they use BI and analytics technologies, including embedded analytics, to glean insights from the data they've accumulated. The biggest benefit is improved decision-making, as analytics lets people make fact-based decisions rather than rely on intuition, guesswork or personal experience. The follow-on benefits from this often include improved operational efficiency, better risk mitigation and optimized operations. These, in turn, can lead to a competitive advantage and, ultimately, higher revenue.

Like other forms of analytics, embedded analytics can provide those benefits. However, embedded analytics also delivers some unique benefits that come from its placement within other software systems and platforms. Specific benefits of embedded analytics include the following:

• Improved experiences for users. Embedded analytics can streamline the BI and analytics process, resulting in a better user experience with less potential for frustration and aggravation.
• Seamless access to insights. Because embedded analytics is integrated into the software people use to perform necessary tasks, it doesn't require users to move from one software system to a separate one for data analytics.
• No interruption of workflow. The integrated nature of embedded analytics means users can stay on task but still get the data insights that help them perform more effectively.
• Faster application of data-driven insights. Similarly, because embedded analytics happens as part of a workflow, users can immediately act on the insights it delivers.
• Increased productivity. Because users can more quickly analyze and visualize data, they can boost their overall productivity.

Business use cases for embedded analytics

Embedded analytics use cases can be found among standard business operations -- such as human resources and finance processes -- that are common among organizations and industry-specific processes. Illustrative embedded analytics use cases include the following examples:

• Sales teams use embedded analytics within their CRMs not only to generate reports on past sales figures, but to identify current and future opportunities using customer behavior data to tease out patterns.
• Supply chain managers can use such capabilities within their existing supply chain management systems to run real-time analysis on market conditions and generate demand forecasts that are more accurate and quickly produced than those created by human planners.
• HR departments can use embedded analytics to delve into employee demographics, study the success of retention policies and gain insights into the company's workforce needs. They can also access such analyses more easily and readily because the capabilities are built into the software.
• Healthcare providers can use analytics capabilities embedded in their electronic health record software to pull together multiple data points about a patient -- such as age and blood pressure -- to better understand their overall health and possible health risks.
• Companies can add embedded analytics capabilities to their customer-facing software products or digital tools, thereby improving the value those products bring to their clients. Companies that offer digital tools as part of other services, such as financial planning, could provide these capabilities within their web portal to create self-service analytics capabilities for their customers.

Embedded analytics vs. conventional BI

Embedded analytics and conventional BI are similar in that they both collect, analyze and visualize data to convey insights individuals can use to make more informed decisions. However, BI is a standalone process, both in its traditional form led by skilled BI professionals and in self-service BI environments that let business users analyze the data themselves. BI typically uses data generated by the various systems in an organization, but the tools and processes used in the business intelligence function are separate from those systems.

Embedded analytics, on the other hand, puts the BI and analytics process within the systems that workers -- and in some cases, customers -- use to accomplish the tasks they need to do as part of their jobs. As such, embedded analytics users don't need to switch out of the application they're using or tab over to another screen to get analytical insights.

Key embedded analytics features and capabilities

Organizations seeking embedded analytics to bring into software already in use or as part of a new software purchase should look for the following capabilities and features:

• Data discovery and data preparation to ensure the data used by the embedded analytics is accurate, clean and complete.
• Automated options.
• Collaboration features.
• Interactive reports so that users can drill down into insights for additional information, query the data, compare data points and create various visuals.
• Integration capabilities that match the organization's needs.
• Mobile capabilities, such as the ability to display reports on mobile devices.
• Security features.
• User-friendly features, including toolkits that let users customize the user interface.
• Data visualization options.

In addition, organizations should look for embedded analytics platforms and tools that have AI capabilities, including augmented analytics features and machine learning algorithms.

Embedded analytics vendors

Figures from Verified Market Research put the value of the embedded analytics market at $54.95 billion in 2024, and the firm has projected that the market will reach $149 billion by 2031. Figures from IMARC Group show an even larger market, as the firm put the global embedded analytics market at $62 billion in 2023 and has estimated it will reach $175.3 billion by 2032.

The following is an alphabetical sample of vendors providing embedded analytics:

• Domo.
• Entrinsik.
• GoodData
• Infor.
• Infragistics.
• Logi Analytics, owned by Insightsoftware.
• Looker, owned by Google.
• Luzmo.
• Metabase.
• Microsoft.
• MicroStrategy.
• Preset.
• Qlik.
• Qrvey.
• Sigma Computing.
• Sisense.
• Tableau, owned by Salesforce.
• ThoughtSpot.
• Yellowfin, owned by Idera.
• Zoho.