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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 DifferencesUnlike 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. |
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