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Unlocking the Power of Data with an Embedded Analytics Platform

    Embedded analytics or embedded business intelligence software offer an entirely different perspective on how kpis and metrics are set, tracked, and used towards scalability.

    Of course, if you haven’t used a program or a platform that comes with embedded business intelligence capabilities, you may not know the full context of the technology, and whatever it’s capable of offering. 

    For that purpose, we decided to do an in-depth drill down on what embedded business analytics and some of the best-embedded analytics examples to take inspiration from. 

    Let’s get started.

    What is Embedded Analytics and How Are They Useful In Modern-Day Businesses?

     What is Embedded Analytics and How Are They Useful In Modern-Day Businesses

    Simply put, Embedded BI is an intelligence technology where bits or a detailed version of different actionable metrics can be integrated into customer-facing products. 

    For instance, a restaurant software working with a 3rd party vendor can request business intelligence tools to see what kind of sales they’re making, what kind of food menu optimizations need to be made, customers preferences and buying patterns, and vice versa.

    The interesting thing about embedded analytics is that customers don’t need to hinge on dedicated analytics software – such as Klipfolio, or Google Analytics, as long as they know what they’re looking for, and their expectations from an embedded analytics tool.

    In a way, these programs enable users to access and interact with data-driven insights within the context of their daily workflows, without having to switch to a separate analytics tool or interface. 

    6 Real-Life Benefits of Embedded Analytics Intelligence for Marketers

     6 Real-Life Benefits of Embedded Analytics Intelligence for Marketers

    The following benefits might vary from one use case to another, but they remain more or less the same. 

    Here’s what you need to know about this form of analytics.

    1. Enhanced Decision-Making: 

    By embedding different forms of analytics directly into business applications, users can make data-driven decisions without the need to switch between multiple tools or systems. 

    They can access real-time insights and visualizations, enabling quicker and more informed decision-making. Embedded analytics empower users to identify patterns, trends, and anomalies, leading to more effective problem-solving and strategic decision-making.

    2. Improved User Adoption: 

    As far as common embedded analytics examples go, they integrate into existing applications and workflows, hence eliminating the need for users to learn and navigate a separate analytics tool. 

    Users can also access relevant data and insights within the familiar interface of their preferred application, increasing user adoption and engagement with analytics capabilities. 

    Doing so can lead to broader usage of data-driven insights across the organization.

    3. Streamlined Workflows: 

    Embedded analytics seamlessly integrate data analysis into existing business processes and workflows, reducing the need for manual data extraction and analysis. 

    As a result, users can, then, access relevant information at the right time, eliminating the time-consuming and error-prone process of exporting data to external tools for analysis. 

    4. Customer-Facing Analytics: 

    As mentioned earlier, embedded analytics tools can be used to deliver data-driven insights to customers directly within the applications they use. 

    3rd party businesses working with different customer-centric vendors can provide them with interactive dashboards and visualizations that enable self-service analysis and reporting. 

    This factor also allows customers to monitor their KPIs, track progress, and gain deeper insights into their usage patterns or performance metrics. 

    5. Tons of Monetization Opportunities: 

    By embedding a given set of analytics into products or services can create additional revenue streams for businesses. 

    6. Data Governance and Security: 

    Lastly, this form of analytics can provide a centralized and controlled environment for data access and analysis. 

    Businesses can enforce data governance policies and ensure that users access only the relevant and authorized data within the embedded analytics interface. 

    First Timers Dilemma: Buying Vs Building Embedded Analytics Business Intelligence Tool

     First Timers Dilemma_ Buying Vs Building Embedded Analytics Business Intelligence Tool

    Whether it’s dedicated business intelligence software in the form of embedded technology, or building one from scratch, the predicament always baffles new business owners. 

    While some may argue that buying, or using embedded analytics technology is much better than creating one yourself, as the latter tends to incur tons of additional expenses, both aspects have their pros and cons. 

    We have worked with all kinds of businesses to confidently say that both approaches are better, given their pros and cons have been properly evaluated. Building an embedded analytics component is a good thing as long as your business especially requires that. 

    On the other hand, using something similar or buying services from a dedicated platform also isn’t such a bad option. If the scope of your business isn’t as wide as having a dedicated tool custom-built, then going for paid embedded analytics services is a viable option.

    Nonetheless, here’s a detailed breakdown of both aspects for your ready reference.

    Buying an Embedded Business Analytics Tool

    Time and Speed: 

    Buying an embedded analytics tool allows you to quickly implement the solution and start leveraging its features. 

    The vendor has already invested time and resources into developing the tool, so you can save time by avoiding the development process.

    Expertise and Support: 

    By purchasing an established embedded analytics tool, you gain access to the expertise and support of the vendor. 

    They have likely refined their product through customer feedback and have a dedicated support team to assist with any issues or questions that may arise.

    Feature Set: 

    Commercial embedded analytics tools often come with a wide range of features and functionalities. 

    These tools are designed to address common analytics needs, including data integration, visualization, and reporting. 

    By buying a tool, you can benefit from a comprehensive set of features without the need for extensive development efforts.


    While buying an embedded analytics tool involves upfront costs in the form of licensing or subscription fees, it may still be more cost-effective compared to building a tool from scratch. 

    Consider the total cost of ownership, including development, maintenance, and support, when evaluating the financial impact.

    Building an Embedded Business Analytics Tool:

    Customization and Flexibility: 

    Building your own embedded analytics tool provides maximum flexibility in terms of customization. 

    You can tailor the tool to fit your specific requirements and integrate it seamlessly with your existing systems. 

    This option is ideal if you have unique business needs or if you want complete control over the features and functionalities of the tool.

    Competitive Advantage: 

    If analytics is a core competency of your organization and a differentiating factor in your industry, building a custom tool can give you a competitive advantage. 

    It allows you to create a solution that aligns precisely with your business strategy and unique value proposition.

    Scalability and Long-Term Control: 

    Building your embedded analytics tool provides you with long-term control over the solution. 

    As your needs evolve, you can scale and adapt the tool to accommodate changing requirements. You are not reliant on a vendor’s roadmap or limitations.

    Development Resources and Expertise: 

    Building an embedded analytics tool requires a skilled development team with expertise in data analytics, user interface design, and integration. 

    Consider whether you have the necessary resources and expertise in-house or if you are willing to invest in building that capability.

    Ultimately, as we said, it’s a matter of perspective and actual business requirements. On that note, new business owners sometimes fall victim to the “buyer’s impulse” syndrome, where they sign up for a paid product without any prior research.

    Doing so usually ends up in two outcomes. 

    1. Either the said business owner regrets having started development on the tool. By then, it’s too late and there’s no backing away from it. 
    2. The business owner decides to throw in the towel and go for an alternative solution. The financial injury incurred on previous “experience” is left as is, and added to the fiscal year’s expenses. 

    5 Best Embedded Analytics Tools and Their Key Features

    Here’s a quick overview of some of the best embedded analytics tools that you can use for your business. Some of them are paid, while others are free for as long as using the basic version of the tool is concerned. 

    1. Looker by Google Cloud:

    Looker offers a powerful data modeling layer, allowing users to create and define data relationships and calculations. 

    The program also provides interactive and customizable visualizations, dashboards, and reports. Looker also includes advanced analytics capabilities, data exploration, and embedded analytics SDK for seamless integration.

    Popular Use Cases: 

    Looker is suitable for organizations looking to embed data-driven insights into their products, applications, or customer-facing portals. It is commonly used for sales analytics, marketing analytics, customer analytics, and operational analytics.

    2. Tableau:

    Tableau provides a user-friendly drag-and-drop interface for data visualization and exploration. 

    One of the best features of this program is the intuitive drag-and-drop system where data exploration can be done without any hitches whatsoever. 

    Tableau includes advanced analytics capabilities, data blending, and a robust set of APIs for embedding analytics.

    Use Cases: 

    Popular Tableau is widely used for embedded analytics in various industries such as retail, finance, healthcare, and manufacturing. It is suitable for applications involving data exploration, data discovery, and interactive data analysis.

    3. Microsoft Power BI:

    Moving on with robust embedded analytics examples, we’ve got Microsoft Power Bi. 

    For those who’ve already used this platform, reportedly recognize it for its comprehensive set of data visualization and reporting capabilities. 

    Power Bi also provides a wide range of visualizations, customizable dashboards, and natural language query capabilities. 

    Lastly, it includes advanced analytics features, data modeling, and integration with Microsoft products and services.

    Use Cases: 

    Power BI is commonly used for embedded analytics in business applications, customer portals, and internal reporting systems. It is suitable for sales analytics, financial reporting, operational analytics, and self-service business intelligence.

    4. Qlik Sense:

    Key Features: 

    Qlik Sense is another embedded business intelligence analytics software that offers various types of data exploration campaigns and discovery variations.

    The platform provides intuitive drag-and-drop visualizations, interactive dashboards, and self-service data preparation. 

    The robust collaboration tools and integration support for APIs make it one of the best choices for veteran marketers with a growth mindset.

    Popular Use Cases: 

    Qlik Sense is suitable for organizations seeking self-service analytics and embedded analytics in applications or portals. 

    It is commonly used for business performance monitoring, supply chain analytics, sales analytics, and customer analytics.

    5. Sisense:

    If it’s scalability through a dedicated solution that you’re looking for, none of them does it better than Sisense. 

    The program provides advanced data modeling and blending capabilities, interactive visualizations, and dashboards. 

    Just like Qlik Sense, Sisense also has APIs for seamless integration and white-labeling options.

    Popular Use Cases: 

    Sisense is suitable for businesses that require complex data analysis and want to embed analytics into their products, applications, or customer portals. It is commonly used for IoT analytics, healthcare analytics, e-commerce analytics, and business performance monitoring.

    What To Look for In an Embedded Analytics Program?

    If it’s your first time trying integrated analytics, here are a couple of pointers you need to keep in mind. 

    Depending on your business requirements, some things might go a little differently, but they remain the same, more or less.

    • Customization and Branding: 

    Ensure that the embedded analytics software allows you to customize the look and feel to align with your brand identity. 

    Look for features like white labeling, custom themes, and the ability to embed analytics seamlessly within your existing applications or portals.

    • Data Integration: 

    Check if the software can connect to various data sources, such as databases, cloud services, APIs, and spreadsheets. 

    The ability to integrate data from multiple sources is crucial for comprehensive analytics. Additionally, consider whether the software supports real-time data updates and automated data refreshes.

    • Visualization and Reporting Capabilities:

    Assess the range and flexibility of visualizations and reporting options provided by the software. 

    Look for a variety of chart types, interactive dashboards, drill-down capabilities, and the ability to create custom reports. 

    While doing so, don’t forget to ensure that the software supports the visual representations necessary to convey your key metrics effectively.

    • Security and Data Governance: 

    Data security is paramount, especially when embedding analytics into your applications or sharing it with external users. 

    Check if the software offers robust security features such as user authentication, role-based access control, data encryption, and compliance with industry regulations like GDPR or HIPAA.

    • Scalability and Performance: 

    Consider the scalability of the embedded analytics software to handle increasing data volumes and user loads. 

    Ensure that it can handle complex queries and deliver responsive performance even with large datasets. Look for features like caching, data indexing, and load balancing to optimize performance.

    • Ease of Use and Self-Service Capabilities: 

    The software should have a user-friendly interface that allows non-technical users to easily explore and analyze data. 

    Keep an eye out for features, such as drag-and-drop functionality, and self-service capabilities. Aside from these two, the ability to create ad-hoc queries and perform data manipulations without heavy reliance on IT or data experts is valuable.

    • Mobile Accessibility: 

    With the increasing use of mobile devices, ensure that the embedded analytics software provides a responsive and mobile-friendly interface. 

    It should enable users to access and interact with analytics on smartphones and tablets without compromising functionality or user experience

    • Embedding and Integration Options: 

    Consider the embedding options and integration capabilities of the software. It should provide robust APIs, SDKs, or widgets that allow seamless integration into your existing applications, portals, or workflows. Evaluate the ease of integration and the level of customization supported

    • Support and Documentation: 

    Evaluate the level of support provided by the software vendor. Look for comprehensive documentation, knowledge base, tutorials, and user forums. Consider the availability of technical support, including responsiveness, expertise, and the vendor’s reputation for customer service

    • Total Cost of Ownership: 

    Finally, assess the cost of the software, including licensing fees, maintenance, and any additional costs for features like data connectors, user licenses, or premium support. 

    Compare the pricing structure with your budget and the expected return on investment (ROI) from the embedded analytics solution

    Over To You

    Whether it’s dedicated analytics tools or embedded analytics apps that you prefer to use, we’d like to hear more about this from you.

    What are the pros and cons, in your opinion that helped you to go for the tool in the first place? Share your thoughts in the comments section below.

    Good luck!

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