Embedded Analytics

Examples, benefits, and best practices. This guide provides practical advice to help you create world-class embedded analytics of your own.

What is Embedded Analytics? 

Embedded analytics refers to the seamless integration of analytical capabilities directly into software applications, enabling your users to access and analyze data within the context of the application they are already using. This allows for real-time insights and data-driven decision-making without requiring users to switch to a separate analytics tool. Embedded analytics is commonly used in business applications like CRMs and ERPs, making it easier for you to give employees, customers, suppliers and partners the data they need, where they need it, to make better decisions, and take action faster.

Benefits of Embedded Analytics

By giving users insights within their natural workflow, embedding a rich analytics experience gets you, your team, and your customers closer to the data that matters most. And it puts that data in context, which enhances its value. It also helps consolidate data sources and BI tools, making data more accessible, analyzable, and secure within one familiar interface. 

Enhanced user experience: By integrating data analytics directly into everyday applications such as ServiceNow, Miro, Workday, and Salesforce, users can access relevant data seamlessly, leading to a more intuitive and efficient workflow.

Improved decision-making: Embedding analytics provides insights within existing applications, enabling end users to make informed decisions quickly without switching to another tool.

Cost-effectiveness: It can be more cost-effective than building a standalone analytics solution, as it leverages existing infrastructure and resources. ISVs can improve their time to market and focus resources on uplifting their core product.

Scalability: Embedded analytics solutions should be able to scale alongside the application, accommodating growth and increasing data volumes without significant additional development effort.

Data security and governance: It can help ensure that data is accessed and used in a secure and compliant manner, as it can leverage the existing security measures of the hosting application.

Embedded Analytics Examples

By its nature, embedded analytics is usually deployed for specific processes such as financial planning, supply chain optimization, sales lead forecasting, or marketing optimization.

External portals

Business applications and processes

Public websites

Commercial software products

How to Embed Analytics

There are 3 main coding approaches–PRO, LOW, and NO code. For all three, your first step will be to define your requirements by identifying the specific data analysis needs of your application and users. Then evaluate which of the below best fits into your overall data strategy.

1. PRO: Custom build and deploy applications within any stack. You’ll need your analytics engine to provide public APIs and a native cloud analytics development platform. 

  • Select analytics engine: Choose an analytics engine that provides public APIs and a native cloud analytics development platform.

  • Integrate analytics engine: Incorporate the analytics engine into your data warehouse and application stack.

  • Develop analytics features: Use the analytics engine's APIs to build custom analytics features.

  • Test and iterate: Test the functionality and iterate based on user feedback.

  • Deploy and monitor: Deploy the application with embedded capabilities and monitor its performance.

2. LOW CODE: Some analytics tools allow you to create custom embedded analytics in applications using a collection of java script libraries, visualizations, and CLI tools such as nebula.js, within a common governance and security framework.

  • Select analytics tools: Choose analytics tools that support nebula.js and provide a common governance and security framework.

  • Integrate nebula.js: Incorporate nebula.js libraries and visualizations into your application.

  • Customize visualizations: Use CLI tools to customize visualizations to meet your application's requirements.

  • Implement governance and security: Ensure that the embedded analytics adhere to your application's governance and security framework.

  • Test and deploy: Test the features and deploy them within your application.

3. NO CODE: The best platforms enable "code-free” self-service embedded analytics. They use pre-built blocks and connectors to enable process automations for deeper integrations into your systems. This makes it easy for you to create and embed enterprise security, manageability, and re-usability.

  • Select no-code platform: Choose a platform that enables self-service analytics with API integrations without writing any SQL.

  • Create analytics content: Use the platform's interface to create and customize analytics content.

  • Implement security and manageability: Configure enterprise security and manageability features provided by the platform.

  • Embed analytics: Use the platform's embedding capabilities to integrate analytics into your application.

  • Test and deploy: Test and deploy within your application.

Your decision to build or buy depends on several factors, such as required product features, timing, budget, resource availability, and return on investment. Each organization has unique needs, but if you opt to buy, it's advisable to explore business intelligence (BI) solutions with API integrations for seamless embedding.

Key Features of an Embedded Analytics Platform

Either now or in the future, you’ll want your platform to be able to go beyond embedding visualizations, dashboards, and reporting. You and your customers will look for a long list of features such as alerts, AI-generated analysis, automations, and natural language search, data preparation, mobile reporting, and collaboration. Here we dig into a few key features:

Augmented analytics

Modern embedded analytics tools make it easy for you to leverage the power of machine learning (ML) algorithms and artificial intelligence (AI) in analytics. It automates analysis processes, analyzes and interprets data, derives insights, and makes predictions or recommendations. ML and AI can also automate much of the manual labor in BI and analytics.

Match the look and feel 

You’ll want to be able to exactly match the look and feel of the destination application (where it’s being embedded). This way, the information becomes better integrated into the application.

Flexible and powerful development capabilities

Your platform should have a broad set of RESTful APIs, built using modern standards like JSON to work with the latest web and application technologies. This way, developers can leverage these APIs with familiar frameworks, languages (e.g. HTML5, CSS3, JavaScript), charting libraries, and data integration options to build and extend embedded analytics solutions in almost any application or browser-based UI.

Optimized for security, scalability, and performance

You’ll need a single governance framework to allow for common security, manageability and re-usability. This includes single sign-on (SSO) authentication and allows the platform to be deployed on a single server and scale both vertically and horizontally to address the availability and processing requirements of your deployment, whether on premise or in the cloud. Plus, you’ll need high performance in-memory analytics to handle massive data sets, while aligning to your security model.

Embedded Analytics vs Business Intelligence

Embedded analytics is a subset of business intelligence, focusing specifically on integrating analytics into applications, while business intelligence (BI) is a broader discipline that includes various tools and practices for managing and analyzing business data. BI encompasses the technologies, applications, and practices for collecting, integrating, analyzing, and presenting business information. BI often involves the use of dashboards, reports, and data visualizations to help organizations make informed decisions based on historical and current data.

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Frequently Asked Questions

How Does Embedded Analytics Work?

It works by integrating analytical capabilities directly into software applications. This integration allows end users to access and analyze data within the context of the application they are using, without needing to switch to a separate analytics tool. Embedding analytics typically leverages APIs or SDKs to integrate with the application's existing infrastructure and user interface, providing seamless access to real-time insights and data visualizations.

How do you create embedded analytics?

You first need to identify the analytics needs of your application and business users. Then, select an analytics engine or tool that supports embedding and integrate it into your application. Finally, use the analytics engine's APIs or SDKs to develop custom analytics features and integrate them seamlessly into your application's user interface.

What are the use cases for embedded analytics?

It can be applied in a variety of use cases. For customer-facing applications, it can enhance user experience by providing graphs, data visualizations, and actionable insights. Within internal business applications, it empowers employees with data-driven decision-making capabilities directly within their workflow. In IoT devices, embedding analytics allows for the analysis of data at the edge, enabling immediate insights or actions. Partner applications can also benefit, as it improves collaboration and decision-making among partners.

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