Embedded Analytics Defined

Barry Mostert | Senior Director, Analytics | October 25, 2023

Embedded analytics is an innovative approach to data analytics that makes it simpler for people to make better informed decisions. In this article, we will define embedded analytics, explore its benefits, and examine common implementation techniques. With embedded analytics, organizations can put analytical capabilities directly into business workflows and applications, allowing employees—or sometimes even customers—to access real-time insights in context and help them make data-driven decisions.

What Is Embedded Analytics?

Embedded analytics directly integrates data analyses and visualization capabilities into operational applications or other software, allowing people to access and analyze data without switching between different applications or systems. Embedded analytics helps businesses improve decision-making and spot new opportunities while reducing the complexity involved in analytics. It makes data-driven insights more accessible and actionable to more people—employees, and sometimes even customers—by putting relevant and actionable information right where they’re needed.

Embedded Analytics vs. Business Intelligence (BI)

Business analytics and embedded analytics both aim to help businesses make informed decisions with data-driven insights. The difference is in how they are used. Business analytics and business intelligence (BI) require users to switch away from the operational applications they use to do their jobs and use separate tools with different interfaces to get data insights.

Embedded analytics is designed for a specific location it will be integrated into, providing relevant information for the task. It offers an easy way for people to access the analysis and insights, whereas business analytics or business intelligence tools also offer information for consumption but require a technically savvy person to create or modify content using the tool and related data sources.

Key Differences

Unlike traditional analytics, embedded analytics eliminates the need to switch from the user interface you’re using to do your job and move to a specialized analytics tool to access the supporting information and insight. Internal users (employees) and external users (customers) don’t require advanced technical skills or access to the underlying analytics platform or data management systems to use embedded analytics. That access lets them make data-driven decisions without understanding the analytics process involved.

Embedded analytics provides a more efficient, accessible, and user-friendly approach, one that’s particularly well suited to support real-time business processes. Embedded analytics can deliver contextual insights to support business processes in real time. Without embedded analytics, the user would require access to a separate analytics platform, the skills to use that software, an understanding of the data definitions, and more time to interact, manipulate, and digest the presented information.

Key Takeaways

  • A successful embedded analytics project requires a clear understanding of the specific business objectives and user experience the project is trying to address. How will the business benefit, and how will their customer benefit?
  • Embedded analytics must be seamlessly integrated into the application or platform so that the embedded component can’t be recognized as something separate from the app. To do this, the embedded analytics system needs to integrate well with the existing infrastructure and tools.
  • An embedded analytics solution should scale to handle large volumes of data and user interactions without lag when rendering the embedded component. The system should handle complex queries and calculations, performing optimally even as data volumes grow.
  • The implementation team must ensure that data governance and security policies are in place to protect user data and comply with regulations. This includes controlling data access, monitoring data usage, and enforcing security policies.
  • It’s worth the effort to measure the return on investment from the embedded analytics platform. By incorporating usage tracking, companies can quantify the value of using metrics, such as time saved by customers.
Traditional Analytics Embedded Analytics
Switching Between Interfaces Users need to switch from their primary user interface to a specialized analytics tool. Users don't need to switch interfaces. They can access insights directly within their current interface.
Technical Skills Required Requires users to have advanced technical skills and knowledge of the underlying analytics platform or data management systems. Doesn't require users (both internal and external) to have advanced technical skills or understand the analytics platform or data management systems.
Efficiency and Accessibility Requires more time and effort to interact, manipulate, and understand the data presented. Offers a more efficient, accessible, and user-friendly approach. Particularly suitable for real-time business processes.
Real-Time Insights Not inherently designed for real-time business processes. Users often need to wait for data processing and analysis. Provides contextual insights in real-time to support business processes.
Need for Separate Platform Requires access to a separate analytics platform and the skills to use that software. Doesn't require a separate analytics platform. Insights are delivered contextually within the user interface.
Understanding Data Definitions Users need an understanding of the data definitions to effectively use the analytics. Users don't need a deep understanding of the analytics process or the data definitions.

Embedded Analytics Explained

Embedded analytics is a software platform that integrates data analysis capabilities into an organization’s operational applications in order to better support workflows and business processes. By using analytics embedded directly into operational applications, such as CRM or ERP systems, end users can access critical data insights without needing additional tools or support from IT or data analysts. This means users can make informed, data-driven decisions without leaving the context of their current workflow or application. When used broadly in a company, embedded analytics can improve productivity, increase ROI for the analytics platform, and foster an analytics-driven culture.

Figure 1: Analytics platform visual components embedded into an ERP application.

Why Is Embedded Analytics Important?

From the company perspective: Embedded analytics can help a business differentiate their products and services from competitors. For example, a bank may embed visual data insights into their customers’ online account overview that shows their savings progress and projects potential future wealth based on different savings and return scenarios. This value-add service is relatively low cost but high value to their customers since it directly enhances what the customers is trying to do. By embedding analytics directly into products, businesses can improve customer retention and even generate new revenue streams by charging for additional analytics features. Embedded analytics also allows companies to collect and analyze usage data, providing insights into customer behavior and preferences that inform future product development.

From the user perspective: Embedded analytics lets employees or customers access data insights directly within their daily tasks without needing additional tools or specialized knowledge. This helps people to make better-informed, data-driven decisions, improving productivity, accuracy, and efficiency. With embedded analytics, people can quickly and easily consume the presented data insights that would otherwise be difficult or time-consuming to obtain. Additionally, embedded analytics lets people take immediate action based on these insights, rather than having to put off a decision in order to gather more data and research. For example, when someone logs into their personal online bank account, embedded analytics could let them review their spending habits visually and additional tools could help them assess if a major purchase, such as a new car, is within their reach.

How Does Embedded Analytics Work?

There are various methods to embed insights into other systems, with increasing levels of technical skill required. The simplest involves just cutting and pasting links into an application. The next level uses low-code options that let a more technical person who doesn’t necessarily write code but who understands the systems in use. Finally, there are full-code developer frameworks that provide the most flexibility but depend on specialist skills. Here are more details on these three methods for embedding analytics.

  • Integration by copy and pasting a URL: In this approach, the operational system the user interacts with calls for the analytics platform to render the required analytics component, such as a chart or table, into a predetermined space on the user interface. To use this approach, that operational application must offer a method to embed content, typically from links, websites, or iFrames; that URL would come from the analytics software. Figure 2 shows analytics embedded into Microsoft Teams using Microsoft's website app.

    Figure 2: Embedding an Oracle Analytics visualization into Microsoft Teams.

    Learn how to embed Oracle Analytics Cloud into Microsoft Teams

  • Integration using low-code development platforms: The benefits of low-code application development include faster innovation compared with coding from scratch, which brings rapid delivery of business requirements with minimal cost. Various techniques can be used to embed analytics, depending on the use case and the skill level of the person doing the embedding. The low-code user is likely a tech-savvy business analyst or other “citizen developer” who’s comfortable with feature-rich tools but is not a full-on code writer. For example, low-code tools can offer a web component that makes it easy to embed analytics insights into applications. And some databases include low-code tools to more easily embed data visualizations and other analytics.

    For example, in Figure 3, Oracle APEX faceted searches can filter embedded analytics content, including tag clouds and natural language generation visualizations.

    Figure 3: Using an Oracle APEX faceted search to filter results of embedded Oracle Analytics Cloud tag cloud and natural language visualization.

    Figure 4 shows an analytics web component used within an Oracle Visual Builder application referencing an analytics project and the properties that the low-code application developer can configure.

    Figure 4: Embedding Oracle Analytics into Oracle Visual Builder using a web component.
  • Integration using full-code frameworks: Full-code frameworks offer the greatest flexibility but using them requires developers with the right skills. For example, a developer might use a JavaScript embedding framework along with HTML and CSS to create a fully custom web application experience. See in Figure 5—for each Oracle Analytics canvas, it’s possible to view and copy the code that’s required to reference that canvas in the web application.

    Figure 5: Copying the HMTL source code from Oracle Analytics to embed a visualization into a third-party web app.

    Embedding content in this way offers additional functionality for passing filters and invoking events, resulting in a rich user experience that can be tailored for responsive web design. See Figure 6.

    Figure 6: Oracle Analytics embedded into a Bootstrap template for a responsive, custom application experience.

7 Benefits of Embedded Analytics

Embedded analytics can make it easier and more efficient for employees and customers to get information they can act on without breaking away from the application or digital interface they’re using. That information might come in the form of a chart, table, map, or even just a text prompt. The point is that the information comes from some other data source but appears naturally in the digital activity a person is doing. Here are seven benefits that can bring.

7 Benefits of Embedded Analytics: Data-driven decision-making, Increased productivity, Customer satisfaction, Revenue growth, Self-service, A single, trusted data source, Composable applications
Benefits that embedded analytics can bring include data-driven decisions, increased productivity, better customer satisfaction, and increased revenue.

  1. Data-driven decision-making: By providing people with the right insights embedded into their decision process and with the necessary context, they are more likely to make the best data-driven decision faster because the information is readily available without the need to search for it.
  2. Increased productivity: When the right information is presented immediately, embedded within a business process, people save time by not having to search for analytics insights in separate tools. Switching between tools requires constant context switching, which negatively affects productivity.
  3. Customer satisfaction: Customers gain a more streamlined experience in their research or buying experience, since information relevant to their action or decision is immediately presented. This lets people complete their tasks without needing to pull data from other apps, make phone calls, or seek other help from experts.
  4. Revenue growth: If employees are making faster, more accurate, data-driven decisions, it improves the odds of a better business outcome and revenue growth as the company can act on new opportunities faster. Well-informed customers should face less indecision which hopefully also drives sales.
  5. Self-service: Embedded analytics improves self-service by giving people more relevant information to complete their work without additional tools, steps, or help from others.
  6. A single, trusted data source: Analytics platforms that provide a consistent and trusted enterprise data model also can provide embedded analytics to deliver metrics and insights that people trust. Information provided by other methods—phone calls, Slack messages, emails, paper statements—can quickly lead to inconsistent numbers.
  7. Composable applications: Embedded analytics is a key building block in the creation of composable applications since the analytics platform can provide a capability, such as data visualizations, that an application otherwise lacks. The advantage of composable apps is that the business can assemble existing components quickly to address a new business need.

Examples and Use Cases of Embedded Analytics

When considering how to best use embedded analytics, consider three broad integration scenarios: into public-facing websites or apps, in employee-facing company applications, and, in the case of independent software vendors, into your applications to increase their usefulness. Here are details on those three scenarios.

Public website and mobile app integration: Integrating data analysis and visualization tools directly into a website or web application lets users interact with and analyze data without leaving the webpage. For example, a vacation home rental company can provide hosts with insights into their property’s performance, such as occupancy rates, pricing, and reviews, within the context of the company’s website or mobile app for owners.

Internal operational systems integration: Integrating data analysis and visualization tools directly into internal corporate systems can drive significant employee time savings and give people more actionable information. For example, a supply chain management platform that incorporates analytics can provide real-time visibility into inventory levels, supplier performance, and delivery times, making those insights visible directly in a company’s supply chain management system without requiring an employee to switch to other company tools.

Third-party systems integration: Embedded analytics can be integrated into a third-party application in an independent software vendor arrangement to provide their users with real-time insights within their workflows. This allows the ISV organization to provide data-driven insights to their customers or partners, creating a competitive advantage. For example, an ISV could embed analytics into their ecommerce platform to provide their customers with real-time insights on consumer behavior, such as browsing and purchasing history.

Key Embedded Analytics Platform Features

An embedded analytics platform requires some fundamental features to meet companies’ needs. Those needs center foremost around how the user interacts with data—such as dashboards, visualizations, and interactivity—as well as the ability to access the right data. Here are key features an embedded analytics platform needs.

  • Dashboards: Software providers that excel at creating operational software may not necessarily possess expertise in analytics tools, so embedding analytics from a specialist software vendor can be beneficial. By integrating embedded dashboards, employees can more easily interact with their data in an intuitive and accessible way.
  • Data visualization: Embedded data visualizations, such as charts, language narratives, and tables, can be directly integrated within another web or mobile app, providing users with a clear and concise visual representation of their data to support decisions.
  • Self-service analytics: Embedded content is directly connected to the data source and provides the user a graphical way to interact with that data without asking IT to create a report for them. Self-service capabilities include the ability to modify their view through filtering, changing chart types, drilling into additional data elements, and creating new visualizations.
  • Data connectors: The embedded analytics platform should connect directly to all relevant data sources without having to move data or rely on proprietary data stores, since those approaches can result in stale and inconsistent data.
  • Interactivity: Embedded analytics should be able to interact with the host where it’s embedded. This can include accepting values passed as filters or sending data-driven events to the host page.

Future of Embedded Analytics

Embedded analytics will play an increasingly important role in helping employees and customers access the insights they need to take action, all within the application they’re using. This will solve major roadblocks for businesses so they can make better use of data and help people make smart decisions with confidence. Embedded analytics will provide much more than static or mildly interactive charts. Machine learning (ML) will be able to provide contextual visual predictions without the user having to ask for it. For example, an energy company can use ML to provide insights within their customers’ billing pages that predict their next six months of energy usage considering past use plus external factors, such as seasonality and weather forecasts.

Delivering personalized, real-time insights will become increasingly important as businesses seek to gain a competitive advantage through more timely and informed decision-making. Other factors that may shape the future of embedded analytics include the growing use of cloud-based solutions, the increased adoption of self-service analytics, and the integration of analytics solutions with emerging technologies, such as the Internet of Things (IoT) and blockchain.

How to Choose an Embedded Analytics Solution

When assessing whether an analytics platform can meet your needs for embedded analytics, consider the following factors, in priority order:

  1. Integration: It should integrate with the existing application or platform, with no customized workarounds needed.
  2. Data connectivity: It should be able to connect to a wide range of data sources, including databases, cloud storage, and other applications.
  3. Security: It should provide robust security features to protect sensitive data, including silent user authentication, data encryption, and access controls.
  4. Scalability: It should be scalable to handle large amounts of data and user interactions, while maintaining performance and reliability.
  5. Support and training: It should be backed by a knowledgeable support team, providing you access to training and resources to help users get the most out of the system.
  6. Total cost ownership: It should provide a positive ROI, with a transparent pricing model that considers all costs, including licensing, implementation, and ongoing maintenance and support.
  7. Customization: The system’s visualizations should be customizable so that the embedded component naturally appears to be part of the original app. It should be functionally customizable to the specific needs of the business with the ability to add or remove features as needed.
  8. Analytics functionality: It should provide a range of analytics functionality, including data modeling, data visualization, predictive analytics, and reporting.
  9. Self-service capabilities: It should allow users to modify their view of the data in real time, without requiring training or IT support.

Get the Benefits of Embedded Analytics with Oracle

Oracle Analytics offers a range of embedding capabilities, such as simply using a link to access a workbook, as well as the flexibility to use Oracle’s low-code development tools to provide components or to use a JavaScript embedding framework that full-code developers prefer.

The combination of embedded Oracle Analytics and the wealth of services available in Oracle Cloud Infrastructure (OCI) offers a unique way for companies to create tailored, composable applications. For example, they can build an application using Visual Builder that takes an image, performs analysis using the OCI Vision AI service, and generates suggestions for “labels” that act as filters for embedded analytics. Such a solution also makes use of Oracle Autonomous Data Warehouse, OCI Object Storage, and communication via OCI REST APIs. Business use cases abound for such an app—think of using AI Vision capabilities to detect how full carpark garages are or when inventory has dipped at a station on an assembly line.

Nucleus Research: Embedded Analytics Technology Value Matrix 2022—Nucleus names Oracle Analytics as a Leader

Embedding analytics using a wide range of techniques can help businesses of all sizes. Getting started with embedded analytics can be as easy as copying and pasting an analytics workbook link into one of your business applications. Or, if you’re a citizen developer, you can try something more advanced using low-code options for embedding. Or, for developers, the sky’s the limit using an embedding framework for full-code creation. Whichever you choose, you’ll quickly see how providing insights at the right time, in the flow of an employee’s work or a customer’s buying process, can result in better decisions and greater productivity or sales.

Embedding analytics allows each system to excel in its intended functions, which is the primary advantage. Software companies can let their host application—and its developers—concentrate entirely on optimizing that system’s functional operation. By using an analytics platform to provide embedded analytics, companies can avoid having to develop their own custom analytics, an area in which they may lack expertise.

Embedded Analytics FAQs

What is an embedded analytics example?

An example of embedded analytics are metrics and visualizations embedded within a CRM (customer relationship management) system. Sales representatives can view key performance metrics, such as sales revenue, conversion rates, and customer acquisition costs, directly within the CRM system they use every day. This allows them to make data-driven decisions, identify trends, and track progress toward their goals without having to switch between different applications.

What is the power of embedded analytics?

The power of embedded analytics lies in its ability to seamlessly integrate data analytics into existing business workflows, applications, and processes. By embedding analytics directly into their daily operations, businesses can gain real-time insights, make data-driven decisions, and quickly respond to changing market conditions. This can lead to improved efficiency, enhanced customer experiences, increased revenue, and reduced costs.

What is the value of embedded analytics?

First, embedded analytics enables businesses to make informed decisions based on real-time data insights, improving their ability to immediately address issues or market changes. With embedded analytics, employees can access and interpret data without relying on specialized skills or support from a data team. This democratizes data access, increasing the speed of decision-making and reducing the likelihood of errors caused by manual data processing.

Second, embedded analytics can provide a competitive advantage by improving the quality of customer experiences. With real-time insights, businesses can better understand their customers' behavior and preferences, enabling them to provide personalized recommendations and targeted marketing campaigns. This, in turn, can increase customer loyalty, reduce churn rates, and drive revenue growth.

Finally, embedded analytics can help reduce costs by identifying inefficiencies in business processes and allowing for continuous optimization. By tracking key performance indicators (KPIs) in real time, businesses can identify bottlenecks, adjust processes accordingly, and reduce waste, ultimately improving their bottom line.

Embedded analytics is an innovative approach to data analytics that makes it simpler for people to make better informed decisions. In this article, we will define embedded analytics, explore its benefits, and examine common implementation techniques. With embedded analytics, organizations can put analytical capabilities directly into business workflows and applications, allowing employees—or sometimes even customers—to access real-time insights in context and help them make data-driven decisions.

What Is Embedded Analytics?

Embedded analytics directly integrates data analyses and visualization capabilities into operational applications or other software, allowing people to access and analyze data without switching between different applications or systems. Embedded analytics helps businesses improve decision-making and spot new opportunities while reducing the complexity involved in analytics. It makes data-driven insights more accessible and actionable to more people—employees, and sometimes even customers—by putting relevant and actionable information right where they’re needed.

Embedded Analytics vs. Business Intelligence (BI)

Business analytics and embedded analytics both aim to help businesses make informed decisions with data-driven insights. The difference is in how they are used. Business analytics and business intelligence (BI) require users to switch away from the operational applications they use to do their jobs and use separate tools with different interfaces to get data insights.

Embedded analytics is designed for a specific location it will be integrated into, providing relevant information for the task. It offers an easy way for people to access the analysis and insights, whereas business analytics or business intelligence tools also offer information for consumption but require a technically savvy person to create or modify content using the tool and related data sources.

Key Differences

Unlike traditional analytics, embedded analytics eliminates the need to switch from the user interface you’re using to do your job and move to a specialized analytics tool to access the supporting information and insight. Internal users (employees) and external users (customers) don’t require advanced technical skills or access to the underlying analytics platform or data management systems to use embedded analytics. That access lets them make data-driven decisions without understanding the analytics process involved.

Embedded analytics provides a more efficient, accessible, and user-friendly approach, one that’s particularly well suited to support real-time business processes. Embedded analytics can deliver contextual insights to support business processes in real time. Without embedded analytics, the user would require access to a separate analytics platform, the skills to use that software, an understanding of the data definitions, and more time to interact, manipulate, and digest the presented information.

Key Takeaways

  • A successful embedded analytics project requires a clear understanding of the specific business objectives and user experience the project is trying to address. How will the business benefit, and how will their customer benefit?
  • Embedded analytics must be seamlessly integrated into the application or platform so that the embedded component can’t be recognized as something separate from the app. To do this, the embedded analytics system needs to integrate well with the existing infrastructure and tools.
  • An embedded analytics solution should scale to handle large volumes of data and user interactions without lag when rendering the embedded component. The system should handle complex queries and calculations, performing optimally even as data volumes grow.
  • The implementation team must ensure that data governance and security policies are in place to protect user data and comply with regulations. This includes controlling data access, monitoring data usage, and enforcing security policies.
  • It’s worth the effort to measure the return on investment from the embedded analytics platform. By incorporating usage tracking, companies can quantify the value of using metrics, such as time saved by customers.
Traditional Analytics Embedded Analytics
Switching Between Interfaces Users need to switch from their primary user interface to a specialized analytics tool. Users don't need to switch interfaces. They can access insights directly within their current interface.
Technical Skills Required Requires users to have advanced technical skills and knowledge of the underlying analytics platform or data management systems. Doesn't require users (both internal and external) to have advanced technical skills or understand the analytics platform or data management systems.
Efficiency and Accessibility Requires more time and effort to interact, manipulate, and understand the data presented. Offers a more efficient, accessible, and user-friendly approach. Particularly suitable for real-time business processes.
Real-Time Insights Not inherently designed for real-time business processes. Users often need to wait for data processing and analysis. Provides contextual insights in real-time to support business processes.
Need for Separate Platform Requires access to a separate analytics platform and the skills to use that software. Doesn't require a separate analytics platform. Insights are delivered contextually within the user interface.
Understanding Data Definitions Users need an understanding of the data definitions to effectively use the analytics. Users don't need a deep understanding of the analytics process or the data definitions.

Embedded Analytics Explained

Embedded analytics is a software platform that integrates data analysis capabilities into an organization’s operational applications in order to better support workflows and business processes. By using analytics embedded directly into operational applications, such as CRM or ERP systems, end users can access critical data insights without needing additional tools or support from IT or data analysts. This means users can make informed, data-driven decisions without leaving the context of their current workflow or application. When used broadly in a company, embedded analytics can improve productivity, increase ROI for the analytics platform, and foster an analytics-driven culture.

Figure 1: Analytics platform visual components embedded into an ERP application.

Why Is Embedded Analytics Important?

From the company perspective: Embedded analytics can help a business differentiate their products and services from competitors. For example, a bank may embed visual data insights into their customers’ online account overview that shows their savings progress and projects potential future wealth based on different savings and return scenarios. This value-add service is relatively low cost but high value to their customers since it directly enhances what the customers is trying to do. By embedding analytics directly into products, businesses can improve customer retention and even generate new revenue streams by charging for additional analytics features. Embedded analytics also allows companies to collect and analyze usage data, providing insights into customer behavior and preferences that inform future product development.

From the user perspective: Embedded analytics lets employees or customers access data insights directly within their daily tasks without needing additional tools or specialized knowledge. This helps people to make better-informed, data-driven decisions, improving productivity, accuracy, and efficiency. With embedded analytics, people can quickly and easily consume the presented data insights that would otherwise be difficult or time-consuming to obtain. Additionally, embedded analytics lets people take immediate action based on these insights, rather than having to put off a decision in order to gather more data and research. For example, when someone logs into their personal online bank account, embedded analytics could let them review their spending habits visually and additional tools could help them assess if a major purchase, such as a new car, is within their reach.

How Does Embedded Analytics Work?

There are various methods to embed insights into other systems, with increasing levels of technical skill required. The simplest involves just cutting and pasting links into an application. The next level uses low-code options that let a more technical person who doesn’t necessarily write code but who understands the systems in use. Finally, there are full-code developer frameworks that provide the most flexibility but depend on specialist skills. Here are more details on these three methods for embedding analytics.

  • Integration by copy and pasting a URL: In this approach, the operational system the user interacts with calls for the analytics platform to render the required analytics component, such as a chart or table, into a predetermined space on the user interface. To use this approach, that operational application must offer a method to embed content, typically from links, websites, or iFrames; that URL would come from the analytics software. Figure 2 shows analytics embedded into Microsoft Teams using Microsoft's website app.

    Figure 2: Embedding an Oracle Analytics visualization into Microsoft Teams.

    Learn how to embed Oracle Analytics Cloud into Microsoft Teams

  • Integration using low-code development platforms: The benefits of low-code application development include faster innovation compared with coding from scratch, which brings rapid delivery of business requirements with minimal cost. Various techniques can be used to embed analytics, depending on the use case and the skill level of the person doing the embedding. The low-code user is likely a tech-savvy business analyst or other “citizen developer” who’s comfortable with feature-rich tools but is not a full-on code writer. For example, low-code tools can offer a web component that makes it easy to embed analytics insights into applications. And some databases include low-code tools to more easily embed data visualizations and other analytics.

    For example, in Figure 3, Oracle APEX faceted searches can filter embedded analytics content, including tag clouds and natural language generation visualizations.

    Figure 3: Using an Oracle APEX faceted search to filter results of embedded Oracle Analytics Cloud tag cloud and natural language visualization.

    Figure 4 shows an analytics web component used within an Oracle Visual Builder application referencing an analytics project and the properties that the low-code application developer can configure.

    Figure 4: Embedding Oracle Analytics into Oracle Visual Builder using a web component.
  • Integration using full-code frameworks: Full-code frameworks offer the greatest flexibility but using them requires developers with the right skills. For example, a developer might use a JavaScript embedding framework along with HTML and CSS to create a fully custom web application experience. See in Figure 5—for each Oracle Analytics canvas, it’s possible to view and copy the code that’s required to reference that canvas in the web application.

    Figure 5: Copying the HMTL source code from Oracle Analytics to embed a visualization into a third-party web app.

    Embedding content in this way offers additional functionality for passing filters and invoking events, resulting in a rich user experience that can be tailored for responsive web design. See Figure 6.

    Figure 6: Oracle Analytics embedded into a Bootstrap template for a responsive, custom application experience.

7 Benefits of Embedded Analytics

Embedded analytics can make it easier and more efficient for employees and customers to get information they can act on without breaking away from the application or digital interface they’re using. That information might come in the form of a chart, table, map, or even just a text prompt. The point is that the information comes from some other data source but appears naturally in the digital activity a person is doing. Here are seven benefits that can bring.

7 Benefits of Embedded Analytics: Data-driven decision-making, Increased productivity, Customer satisfaction, Revenue growth, Self-service, A single, trusted data source, Composable applications
Benefits that embedded analytics can bring include data-driven decisions, increased productivity, better customer satisfaction, and increased revenue.

  1. Data-driven decision-making: By providing people with the right insights embedded into their decision process and with the necessary context, they are more likely to make the best data-driven decision faster because the information is readily available without the need to search for it.
  2. Increased productivity: When the right information is presented immediately, embedded within a business process, people save time by not having to search for analytics insights in separate tools. Switching between tools requires constant context switching, which negatively affects productivity.
  3. Customer satisfaction: Customers gain a more streamlined experience in their research or buying experience, since information relevant to their action or decision is immediately presented. This lets people complete their tasks without needing to pull data from other apps, make phone calls, or seek other help from experts.
  4. Revenue growth: If employees are making faster, more accurate, data-driven decisions, it improves the odds of a better business outcome and revenue growth as the company can act on new opportunities faster. Well-informed customers should face less indecision which hopefully also drives sales.
  5. Self-service: Embedded analytics improves self-service by giving people more relevant information to complete their work without additional tools, steps, or help from others.
  6. A single, trusted data source: Analytics platforms that provide a consistent and trusted enterprise data model also can provide embedded analytics to deliver metrics and insights that people trust. Information provided by other methods—phone calls, Slack messages, emails, paper statements—can quickly lead to inconsistent numbers.
  7. Composable applications: Embedded analytics is a key building block in the creation of composable applications since the analytics platform can provide a capability, such as data visualizations, that an application otherwise lacks. The advantage of composable apps is that the business can assemble existing components quickly to address a new business need.

Examples and Use Cases of Embedded Analytics

When considering how to best use embedded analytics, consider three broad integration scenarios: into public-facing websites or apps, in employee-facing company applications, and, in the case of independent software vendors, into your applications to increase their usefulness. Here are details on those three scenarios.

Public website and mobile app integration: Integrating data analysis and visualization tools directly into a website or web application lets users interact with and analyze data without leaving the webpage. For example, a vacation home rental company can provide hosts with insights into their property’s performance, such as occupancy rates, pricing, and reviews, within the context of the company’s website or mobile app for owners.

Internal operational systems integration: Integrating data analysis and visualization tools directly into internal corporate systems can drive significant employee time savings and give people more actionable information. For example, a supply chain management platform that incorporates analytics can provide real-time visibility into inventory levels, supplier performance, and delivery times, making those insights visible directly in a company’s supply chain management system without requiring an employee to switch to other company tools.

Third-party systems integration: Embedded analytics can be integrated into a third-party application in an independent software vendor arrangement to provide their users with real-time insights within their workflows. This allows the ISV organization to provide data-driven insights to their customers or partners, creating a competitive advantage. For example, an ISV could embed analytics into their ecommerce platform to provide their customers with real-time insights on consumer behavior, such as browsing and purchasing history.

Key Embedded Analytics Platform Features

An embedded analytics platform requires some fundamental features to meet companies’ needs. Those needs center foremost around how the user interacts with data—such as dashboards, visualizations, and interactivity—as well as the ability to access the right data. Here are key features an embedded analytics platform needs.

  • Dashboards: Software providers that excel at creating operational software may not necessarily possess expertise in analytics tools, so embedding analytics from a specialist software vendor can be beneficial. By integrating embedded dashboards, employees can more easily interact with their data in an intuitive and accessible way.
  • Data visualization: Embedded data visualizations, such as charts, language narratives, and tables, can be directly integrated within another web or mobile app, providing users with a clear and concise visual representation of their data to support decisions.
  • Self-service analytics: Embedded content is directly connected to the data source and provides the user a graphical way to interact with that data without asking IT to create a report for them. Self-service capabilities include the ability to modify their view through filtering, changing chart types, drilling into additional data elements, and creating new visualizations.
  • Data connectors: The embedded analytics platform should connect directly to all relevant data sources without having to move data or rely on proprietary data stores, since those approaches can result in stale and inconsistent data.
  • Interactivity: Embedded analytics should be able to interact with the host where it’s embedded. This can include accepting values passed as filters or sending data-driven events to the host page.

Future of Embedded Analytics

Embedded analytics will play an increasingly important role in helping employees and customers access the insights they need to take action, all within the application they’re using. This will solve major roadblocks for businesses so they can make better use of data and help people make smart decisions with confidence. Embedded analytics will provide much more than static or mildly interactive charts. Machine learning (ML) will be able to provide contextual visual predictions without the user having to ask for it. For example, an energy company can use ML to provide insights within their customers’ billing pages that predict their next six months of energy usage considering past use plus external factors, such as seasonality and weather forecasts.

Delivering personalized, real-time insights will become increasingly important as businesses seek to gain a competitive advantage through more timely and informed decision-making. Other factors that may shape the future of embedded analytics include the growing use of cloud-based solutions, the increased adoption of self-service analytics, and the integration of analytics solutions with emerging technologies, such as the Internet of Things (IoT) and blockchain.

How to Choose an Embedded Analytics Solution

When assessing whether an analytics platform can meet your needs for embedded analytics, consider the following factors, in priority order:

  1. Integration: It should integrate with the existing application or platform, with no customized workarounds needed.
  2. Data connectivity: It should be able to connect to a wide range of data sources, including databases, cloud storage, and other applications.
  3. Security: It should provide robust security features to protect sensitive data, including silent user authentication, data encryption, and access controls.
  4. Scalability: It should be scalable to handle large amounts of data and user interactions, while maintaining performance and reliability.
  5. Support and training: It should be backed by a knowledgeable support team, providing you access to training and resources to help users get the most out of the system.
  6. Total cost ownership: It should provide a positive ROI, with a transparent pricing model that considers all costs, including licensing, implementation, and ongoing maintenance and support.
  7. Customization: The system’s visualizations should be customizable so that the embedded component naturally appears to be part of the original app. It should be functionally customizable to the specific needs of the business with the ability to add or remove features as needed.
  8. Analytics functionality: It should provide a range of analytics functionality, including data modeling, data visualization, predictive analytics, and reporting.
  9. Self-service capabilities: It should allow users to modify their view of the data in real time, without requiring training or IT support.

Get the Benefits of Embedded Analytics with Oracle

Oracle Analytics offers a range of embedding capabilities, such as simply using a link to access a workbook, as well as the flexibility to use Oracle’s low-code development tools to provide components or to use a JavaScript embedding framework that full-code developers prefer.

The combination of embedded Oracle Analytics and the wealth of services available in Oracle Cloud Infrastructure (OCI) offers a unique way for companies to create tailored, composable applications. For example, they can build an application using Visual Builder that takes an image, performs analysis using the OCI Vision AI service, and generates suggestions for “labels” that act as filters for embedded analytics. Such a solution also makes use of Oracle Autonomous Data Warehouse, OCI Object Storage, and communication via OCI REST APIs. Business use cases abound for such an app—think of using AI Vision capabilities to detect how full carpark garages are or when inventory has dipped at a station on an assembly line.

Nucleus Research: Embedded Analytics Technology Value Matrix 2022—Nucleus names Oracle Analytics as a Leader

Embedding analytics using a wide range of techniques can help businesses of all sizes. Getting started with embedded analytics can be as easy as copying and pasting an analytics workbook link into one of your business applications. Or, if you’re a citizen developer, you can try something more advanced using low-code options for embedding. Or, for developers, the sky’s the limit using an embedding framework for full-code creation. Whichever you choose, you’ll quickly see how providing insights at the right time, in the flow of an employee’s work or a customer’s buying process, can result in better decisions and greater productivity or sales.

Embedding analytics allows each system to excel in its intended functions, which is the primary advantage. Software companies can let their host application—and its developers—concentrate entirely on optimizing that system’s functional operation. By using an analytics platform to provide embedded analytics, companies can avoid having to develop their own custom analytics, an area in which they may lack expertise.

Embedded Analytics FAQs

What is an embedded analytics example?

An example of embedded analytics are metrics and visualizations embedded within a CRM (customer relationship management) system. Sales representatives can view key performance metrics, such as sales revenue, conversion rates, and customer acquisition costs, directly within the CRM system they use every day. This allows them to make data-driven decisions, identify trends, and track progress toward their goals without having to switch between different applications.

What is the power of embedded analytics?

The power of embedded analytics lies in its ability to seamlessly integrate data analytics into existing business workflows, applications, and processes. By embedding analytics directly into their daily operations, businesses can gain real-time insights, make data-driven decisions, and quickly respond to changing market conditions. This can lead to improved efficiency, enhanced customer experiences, increased revenue, and reduced costs.

What is the value of embedded analytics?

First, embedded analytics enables businesses to make informed decisions based on real-time data insights, improving their ability to immediately address issues or market changes. With embedded analytics, employees can access and interpret data without relying on specialized skills or support from a data team. This democratizes data access, increasing the speed of decision-making and reducing the likelihood of errors caused by manual data processing.

Second, embedded analytics can provide a competitive advantage by improving the quality of customer experiences. With real-time insights, businesses can better understand their customers' behavior and preferences, enabling them to provide personalized recommendations and targeted marketing campaigns. This, in turn, can increase customer loyalty, reduce churn rates, and drive revenue growth.

Finally, embedded analytics can help reduce costs by identifying inefficiencies in business processes and allowing for continuous optimization. By tracking key performance indicators (KPIs) in real time, businesses can identify bottlenecks, adjust processes accordingly, and reduce waste, ultimately improving their bottom line.

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