Self-Service Embedded BI

Discover the benefits, implementation strategies, and impact on decision-making processes within organizations.

Self-Service Embedded BI
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In this era of informational abundance, organizations need faster access to accurate data to make informed business decisions. Traditional BI systems, often complex and reliant on IT, struggle to meet this demand. On the other hand, self-service embedded business intelligence (BI) tools are being designed to provide user-friendly interfaces and intuitive visualizations, allowing users to explore data independently within their natural workflow, accelerating time to insights and decision-making.

This whitepaper provides a comprehensive overview of self-service embedded BI and its transformative impact on business intelligence. We'll explore the challenges and functionalities of self-service embedded BI platforms, explore the benefits for organizations, and guide you through considerations for successful implementation.

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What Is Self-Service Embedded BI & Why It Matters? 

For a long time, traditional BI platforms have served as powerful tools for data analysis, often managed by IT teams or BI professionals. Historically, dashboards and reports were generated only after a request by a business user, which not only led to long wait times but also restricted access for those without technical expertise. This approach often left non-technical business users dependent on others to extract insights, hindering their ability to make timely, data-driven decisions. 

What is Self Service BI

As the demand for quicker and easier access to insights grew, the need for more user-centric BI solutions became apparent. Self-service BI emerged as a data analysis approach empowering business users to independently access, explore, and analyze data, eliminating the reliance on IT teams or BI specialists. 

Embedded Analytics explained

Today, while many BI solutions offer self-service capabilities, they often remain standalone systems that require separate operational efforts and specialized technical expertise for effective management and utilization. In contrast, self-service embedded BI solutions seamlessly integrate within existing applications, enabling users of all technical levels to create custom dashboards, slice and dice data, and uncover hidden patterns—all within the context of their daily workflow. 

Self-service embedded BI solutions like Reveal go beyond basic functionality. They come packed with advanced analytics features and user-friendly interfaces that empower business users to not only generate reports but also apply predictive analytics models to forecast outcomes, combine data from multiple data sources for a more comprehensive analysis, drill down into data, and much more. 

This empowerment enables business users to gain actionable insights that inform strategic, data-driven decisions, effectively transforming the way organizations leverage data to meet their business objectives. 

Traditional BI vs Self-Service Embedded BI

Choosing between traditional BI tools and self-service embedded BI solutions largely depends on your organization’s specific goals and operational needs. While traditional BI systems remain valuable for complex data analyses and controlled data environments, they can involve cumbersome processes that potentially slow down decision-making and typically require considerable IT oversight. For organizations aiming for agility, broader access to analytics, and quicker decision-making, self-service embedded BI offers a compelling alternative. 

Self-service embedded BI solutions specifically address these limitations by equipping end-users with powerful tools directly within their operational environments. These innovative solutions empower a wider range of users to perform data analysis without the need for extensive technical expertise or constant IT support.  

Here are the key differences between traditional BI and self-service embedded BI: 

Key point Self-Service Embedded BI Traditional BI Tool
Integration Seamless with business applications, minimizing workflow disruptions. Stand-alone, often causing workflow interruptions.
User Accessibility Intuitive and user-friendly design. Accessible to both technical and non-technical users. Typically requires specialized training for non-technical users.
Integration with Business Processes Seamlessly integrates with existing business applications, reducing workflow disruption. Often operates as a separate system, requiring users to switch between applications.
Customization and Flexibility Highly customizable to match the host application branding and user experience. Customization may be limited, and changes can be time-consuming.
Data Sharing and Collaboration Encourages real-time collaboration within the context of the host application. Collaboration may require switching to another system or exporting reports outside the BI system to share.
Cost and Resource Allocation Transparent pricing models with predictable costs, often eliminating the need for significant infrastructure investments. Costs may include per person software licenses, data storage and IT resources for maintenance.
Scalability Offers scalability without significant investments, allowing for quick adaptation to changing demands. Scalability may require additional hardware or resources leading to complexity and increased costs.
Access Control Provides role-based access control to safeguard data and maintain compliance. Role-based access control is a standard feature for ensuring data security.

The Benefits of Self-Service Embedded BI for Organizations

So far, it has become clear that the self-service approach provides many benefits to organizations. The fast, independent access to data and empowerment of non-technical users are at the top of the list, but there’s more to self-service BI. Here’s a summary of the main benefits of self-service BI for organizations:   

  • Accelerated decision-making: The real-time, contextual access to data allows business users to make faster, more informed decisions. They can identify trends, track performance metrics, and react to market changes the moment they spot them.  
  • Empowering non-technical users: Self-service embedded BI’s user-friendly interfaces allow non-technical users to explore data, ask their own questions, and uncover valuable insights that inform their daily activities.   
  • Reduced dependency on IT: By empowering non-technical users to analyze data and generate reports on their own, self-service BI frees up time and resources from your IT team. So, instead of building dashboards and reports, your ITs can focus on other development tasks that bring many benefits to the business and the organization.  
  • Enhanced customer insights and experience: Integrating self-service BI helps organizations better understand customer behaviors and preferences, leading to improved customer experiences and satisfaction. This can drive more targeted marketing strategies, product improvements, and personalized customer interactions. 
  • Competitive advantage: Although, nowadays, everyone knows the importance of data in business, there are still many organizations that haven’t yet invested in a self-service embedded BI solution. This gives those organizations who have a tremendous advantage – while the competition is still trying to figure out spreadsheets with rows of numbers, your business can already act on insights promptly.  
  • Scalability: Modern self-service BI solutions are built to scale and adapt to accommodate increasing data volumes and user adoption without sacrificing performance. This ensures your BI solution can grow alongside your organization. 

Furthermore, self-service embedded BI tools can significantly increase ROI by reducing IT workload, providing easy access to accurate business information quickly, and encouraging a data-driven culture within the organization.   

Understanding Self-Service Embedded BI Features and Functionalities

Next, let’s go over the core features and functionalities of self-service embedded BI solutions for accessibility, data exploration, integration, and customization that power the benefits we just covered. Understanding these capabilities will help you evaluate self-service embedded BI solutions and choose the one that best aligns with your business needs. 

BI Self-Service Features  

  • Drag-and-drop interfaces: A robust What You See Is What You Get (WYSIWYG) drag-and-drop visualization editor ensures that users can intuitively design and customize visualizations regardless of their technical proficiency. This feature ensures agility in adapting to evolving analytical needs within the organizations and should be at the core of any BI solution that claims to be self-service.     
  • Pre-built dashboard templates: Pre-built dashboard templates simplify the creation of dashboards and reports for business users. Such pre-built elements act as building blocks, allowing users to quickly assemble reports and dashboards, reducing the time and effort required for development. 
  • Ease of data connectivity: A true self-service solution will make it easy for end business users to connect to a new data source independently. Of course, some data sources might be more complex, and in such cases, an IT team member might need to be looped in, but for most data sources, business users should be able to do it independently.    
  • Intuitive data visualization processes: It should take, at most, a couple of simple clicks for users to turn queried data into visualization and then add it to the dashboard they’re working on.    
Intuitive data visualization processes

BI Features for Integration, Data Exploration & Customization  

The above features and functionalities must be at the heart of any self-service embedded BI solution. Still, for it to be effective and a good investment, there are other things you need to consider, too, including:   

  • Seamless integration: Seamless integration is crucial for ensuring the self-service BI solution is impeccably embedded within your existing platforms. Utilizing SDK integration instead of iframes offers more robust integration options, flexible deployment, deeper customization, and greater control. 
  • Connectivity: A rich library of data connectors and APIs is essential for connecting to any out-of-the-box data sources. This allows for easy access to various data repositories, whether they are cloud-based, on-premises, or from third-party applications. 
  • Rich data visualization library: Offering a ready-to-use library of charts, graphs, and other visualizations is fundamental, but it’s equally important to have the capability to create custom data visualizations. This flexibility allows users to tailor their visual representations better to suit specific data sets and business needs. Creating custom visualizations requires coding, but once a custom visualization is made, it gets added to the library with the other charts and graphs, and everyone can use it.   
  • Advanced data analysis features: Features such as drill-down capabilities, dashboard linking, and data blending are important for advanced data analysis. These functionalities enable business users to perform in-depth analysis, navigate through different layers of data, combine data from various sources, and gain more detailed and actionable insights. 
  • White-labeling: Customizing the solution to match your brand’s look and feel is vital for self-service embedded BI solutions. White-labeling allows you to offer a seamless experience that aligns with your brand identity, enhancing user acceptance and satisfaction. 
  • Security: Ensure the self-service embedded BI solution offers robust security measures, including data encryption, user authentication, and role-based access control. Additionally, compliance with industry standards and regulations, such as GDPR or HIPAA, is crucial to protecting sensitive data and maintaining trust.   

Implementing Self-Service Embedded BI Solution

Implementing a self-service embedded BI solution involves several critical considerations to ensure successful deployment and long-term effectiveness. Here’s a glimpse into these critical considerations and best practices for implementing self-service embedded BI into your platforms:   

Key Considerations:  

  • Data security and privacy: Security stands as a paramount concern in the implementation of any embedded BI solution. Safeguarding sensitive data and ensuring authorized access are crucial components in building user trust. Ensure the solution complies with relevant regulations such as GDPR, CCPA, SOC2, HIPAA, etc. Additionally, look for security measures for protecting sensitive information, such as data encryption, role-based access APIs, and authentication features.   
  • Scalability and performance: As your organization grows, so will your data and the demand for BI insights. Choose a self-service embedded BI solution that can scale with your needs, both in terms of data volume and number of users. The solution should be able to support efficient data processing and retrieval, ensuring fast query responses and real-time analytics. 
  • User adoption and training: The degree of complexity in understanding and utilizing the tool can significantly impact its adoption across diverse user roles within an organization. Comprehensive onboarding resources, including documentation, tutorials, and training materials, play a pivotal role in easing the adoption process. These resources provide users with the necessary guidance and support to quickly familiarize themselves with the embedded BI solution, thereby accelerating the integration into their workflow. 
Banking dashboard example with self service bi

Best Practices:  

  • Choosing the right platform: Evaluate potential self-service embedded BI solutions based on their integration capabilities, self-service capabilities, scalability, and security features. Look for a solution that offers a rich library of data connectors, advanced data visualization options, analysis features, and customization capabilities. Additionally, consider the vendor’s reputation, customer support, and the solution’s pricing model. To help you navigate the market and make well-informed investment decisions, we have created a detailed checklist for choosing the right solution tailored to your business needs
  • Integrating with existing systems: Ensure the self-service embedded BI solution seamlessly integrates with your existing systems and data sources. This includes enterprise applications, CRM platforms, ERP systems, and diverse data sources, such as APIs, SQL databases, cloud storage, and more. Effective integration minimizes data silos and ensures a unified view of your organizational data.   
  • Ensuring data quality and governance: Data quality is the cornerstone of effective BI. To ensure high-quality, accurate data, make sure to implement data governance policies to standardize data definitions, ensure data accuracy, and maintain consistency. Regularly cleanse and validate data to remove duplicates and correct errors. Establish clear data ownership, access control, and quality standards to maintain data integrity. Furthermore, track the origin and transformation of data throughout its lifecycle to ensure traceability and trust in your insights. 

Comparing Self-Service Embedded BI Platforms

Choosing the right self-service embedded BI solution is essential for empowering everyone within your organization with effective data analysis tools and insights. In this chapter, we compare the top self-service embedded BI solutions on the market to further help you narrow your search and find the right solution for your organization sooner.   


Reveal is built from the ground up for embedded analytics and provides a fully customizable self-service BI solution. Unlike most embedded analytics solutions that rely on simple iFrames within a Web View, Reveal provides a true embedded analytics SDK, giving you complete control over the app experience, including feature visibility, branding, security, and deployment. With its powerful API and native SDK support, Reveal ensures seamless integration, making data exploration effortless with an intuitive drag-and-drop interface, customizable themes, and simplified dashboard editing. 

Partnering with Reveal guarantees fast and seamless integration into any platform or tech stack and a straightforward pricing model that enables you to reach unlimited users per application, making it a cost-effective and powerful solution for modern businesses. 

See Reveal in Action


Tableau is a widely recognized BI solution known for its powerful data visualization capabilities and robust analytics features. As a self-service embedded BI tool, Tableau enables users to create interactive dashboards and detailed reports without extensive technical expertise. It integrates well with a variety of data sources and offers a highly customizable experience. 

However, Tableau can be resource-intensive and may require significant IT involvement for initial setup and ongoing management. Additionally, the cost can be a consideration, especially for smaller organizations. 

Compare: See how Tableau compares against Reveal on main features, capabilities and pricing models.


Domo is an all-in-one BI platform that combines data integration, visualization, and analytics into a single solution. It offers user-friendly dashboards and real-time data insights, empowering business users to explore data and make data-driven decisions. Domo’s extensive data connectivity and cloud-based architecture support seamless integration and scalability. 

While Domo is strong in providing comprehensive BI capabilities, it can be complex to implement and manage. The platform’s pricing model is another factor to consider, as it can become expensive as your data and user base grow. 

Compare: See how Domo compares against Reveal on main features, capabilities and pricing models.


Sisense is an embedded BI solution that emphasizes ease of use and powerful analytics capabilities. It offers a full-stack analytics platform that combines data preparation, analysis, and visualization. Sisense’s unique In-Chip technology ensures fast data processing and real-time analytics, making it suitable for large datasets. 

Sisense is highly customizable and supports a wide range of data sources, but it may require significant IT involvement for initial deployment and ongoing maintenance. Additionally, while Sisense offers a robust feature set, its complexity can pose challenges for non-technical users. 

Compare: See how Sisense compares against Reveal on main features, capabilities and pricing models.


Operating as a hosted SaaS solution using an iFrame approach, Qrvey is a versatile self-service embedded analytics solution that empowers users to analyze data and derive actionable insights. It offers intuitive data visualizations, advanced analytics capabilities, data filtering options, support for big data processing, machine learning, and self-service functionalities for users of all skill levels. 

Qrvey is tailored to organizations looking to deploy self-service BI within an AWS environment. So, despite the higher costs due to its hosted nature, Qrvey is a great analytics solution for organizations that rely heavily on AWS. 

Compare: See how Qrvey compares against Reveal on main features, capabilities and pricing models.


Formerly known as, Luzmo is an embedded analytics platform designed for SaaS companies. It enhances data analysis and visualization capabilities with features like interactive data visualization, intuitive drag-and-drop, automated data modeling, and self-service functionalities. Luzmo also provides a wide range of data connectors for seamless data integration and data column filtering for precise analysis. 

Luzmo’s strength lies in the support of multi-tenant integrations. It also allows the integration of AI solutions such as OpenAI GPT-4 and PaLM for automated data preparation, which can benefit organizations leveraging these AI tools. 

Compare: See how Luzmo compares against Reveal on main features, capabilities and pricing models.

These are the top self-service embedded BI solutions on the market. However, the ideal one is not a one-size-fits-all solution. Carefully evaluate your needs by comparing the ease of use, customization capabilities, integration options, and cost of these solutions to make an informed decision.   

The Future of Self-Service BI in AI-Guided

Whether we like it or not, AI and machine learning are already everywhere around us. The advancement of these technologies is only going to grow and, in the context of BI, is already driving significant enhancements, allowing for more sophisticated data analysis and predictive capabilities. Many self-service embedded BI are already on top of AI and machine learning, empowering customers to: 

  • Automate data preparation.  
  • Generate insights with less human interaction. 
  • Streamline the process of integrating and preparing data from disparate sources. 
  • Uncover hidden patterns, correlations, and trends within datasets. 
  • Forecast future trends, anticipate customer behavior, and recommend optimal courses of action based on historical data and predictive models.  

Moreover, AI-powered natural language processing (NLP) makes BI more accessible. NLP capabilities enable users to interact with BI tools using everyday language. Users can ask questions and receive actionable insights without writing complex queries. 

All of these AI-supported capabilities are leading to real-time insights, more accurate and faster forecasting, and data-driven decision-making. As AI continues to evolve, we can expect BI to become even more intuitive and predictive. 

AI supported capabilities in self service bi

In the near future, AI will help BI solutions to become even more user-friendly and accessible. Expect more BI platforms to become able to deliver customized dashboards, reports, and visualizations based on user roles, interests, and objectives in the relevant business context. Also, to start automatically selecting the most appropriate visualizations, highlighting key insights, and guiding users through the story of the data. 

Gartner predicts that by 2025, AI-driven analytics will be a standard component of 90% of corporate strategies. Customers will transition from “It would be nice if you had an NLP integration or this and that AI feature” to “Since you are not able to provide us with NLP integration or this or that AI feature, we will go to another solution provider that can.”   

This also leads us back to the chapter in which we discussed the key considerations for choosing a self-service embedded BI solution. Such solutions have to keep up with AI trends to remain competitive in the analytics space. When evaluating different solutions, don’t miss having that discussion as well, which will help you understand the vendor’s plans for the future and how they plan to accommodate the demand for AI integrations in the analytics space.   

Explore More of Reveal  

Reveal is a leading embedded analytics solution that is purpose-built to provide ease of use for integrating powerful analytics capabilities into applications. With Reveal, you control branding, feature customization, security implementation, and deployment.  

We bring industry knowledge, robust IT infrastructure, and domain expertise, allowing you to focus on your business growth while we handle the rest. 

Get to know our product better by:  

  • Book a Demo: See Reveal in action and discover how it can accelerate your business. 
  • Download our SDK: Experience firsthand how simple it is to integrate reliable self-service analytics into your existing website or application. 
  • Connect with our Senior Product Manager: Casey Ciniello, Reveal’s PM, is ready to address all your product-related inquiries. Contact her at  
  • Join our Discord Channel: Our product team is available to assist with any questions or roadblocks you may encounter while using Reveal. 

About the Author

Casey Ciniello

Casey Ciniello

Casey has a BA in mathematics and an MBA, bringing a data analytics and business perspective to Infragistics. Casey is the Product Manager for the Reveal Embedded analytics product and was instrumental in product development, market analysis and the product's go-to-market strategy. She’s been at Infragistics since 2013 and when she’s not in the office, she enjoys playing soccer and attending concerts.

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