Highlights:

  • To gain deeper business insights, customers need tools to explore data, identify patterns, and share findings. Data discovery in embedded analytics enables insight-sharing within the app, while augmented analytics now automates this process.
  • AI integration into embedded BI tools adds a new dimension to embedded analytics.

Embedded analytics involves the seamless incorporation of analytical features directly into software applications, allowing users to access and analyze data within their existing workflow. This provides real-time insights and facilitates data-driven decision-making without any need of switching to separate analytics tools.

Commonly found in business applications such as CRM and ERP, embedded analytics enables you to deliver essential data to employees, customers, suppliers, and partners, empowering them with informed decision making and quick execution.

How to Embed Analytics into Your Software Application?

How to embed analytics effectively, and who is capable of doing so, depends on the chosen embedding method and the level of technical expertise available. Depending on your skill level, you may work with varying amounts of code, impacting both the outcome and the depth of integration.

There are several ways of embedding analytics in modern applications, each offering unique advantages suited to different use cases. Typically, embedded analytics dashboards and visualizations involve using pre-built code from the analytics platform, which you adjust and add to your application. Analytics tools uniquely allow for a fully customized solution, where the analytics engineer defines both appearance and functionality. Notably, you don’t need to sign into your modern analytics platform; everything is handled through code.

What is Embedded Analytics Platform Capable of Doing?

Most platforms offer standard features, but leading ones go further with automated options and AI capabilities.

Here are some common capabilities in embedded analytics platforms:

  • Data preparation

Successful analytics projects require data readiness for analysis. Data preparation tools enable connection to multiple sources and ensure accurate data access, allowing end users to rely on clean data. In embedded analytics architecture, software vendors leverage this capability to offer reliable and precise data to their users.

  • Dashboarding

Dashboards offer a centralized view of multiple reports, helping clients monitor business performance and security. Modern dashboards even support coded actions, like order buttons, allowing users to complete tasks directly within the dashboard. Embedded analytics tools should let users build and customize dashboards to align with their brand.

  • Data discovery

To gain deeper business insights, customers need tools to explore data, identify patterns, and share findings. Data discovery in embedded advanced analytics enables insight-sharing within the app, while augmented analytics now automates this process. Machine learning algorithms detect data patterns and alert users to significant changes automatically.

  • Mobile reporting

Providing mobile access to dashboards and reports is essential in certain industries. For instance, viewing virtual machine performance and production rates from a tablet on the factory floor is invaluable. Likewise, receiving automated alerts on mobile can be crucial in fast-paced, reactive environments.

  • Collaboration

Governed collaboration on data insights and report creation within the embedded analytics application saves clients time and enhances user engagement. When clients can collaborate directly within the platform instead of copying screenshots into emails or messages, they stay longer, gain real-time visibility into their data, and can act instantly.

Difference Between Embedded Analytics and Business Intelligence

Embedded analytics and business intelligence are often used interchangeably, but they differ. A scalable business intelligence is a process involving people, systems, and tools that collect and prepare data for analysis to support data-driven decision-making. In contrast, embedded analytics integrates data analytics directly into another application, providing greater context for decisions.

Business intelligence can sometimes fall short by lacking the necessary context for users, as data is often accessed through separate software, leading to underutilization. Embedded analytics, however, offers easy access within the company’s software, promoting a data-focused mindset and encouraging daily consideration of data for informed decision-making.

Artificial Intelligence and Embedded Analytics

AI has become essential in data analytics in B2B, enhancing existing solutions by generating actionable insights, enabling forecasting and predictive analytics, and automating processes.

AI incorporation into embedded BI platforms offers a new dimension to embedded analytics. Instead of merely asking “what happened,” you can now explore “why it happened.” AI provides contextual understanding, helping you predict “what will happen” in the future based on data insights. With AI-accelerated embedded analytics, you gain clear, actionable insights directly within your application.

How Much does Embedded Analytics Cost?

Pricing is a crucial factor when assessing data analytics tools. Various embedded analytics pricing strategies and models are offered by vendors, so it’s essential to consider which aligns best with the company’s approach.

  • Cost per query

The company is billed for each query made to generate analytical insights or explore data in a self-service tool. While it may seem flexible, this option is quite unpredictable.

  • Cost per user

The platform charges based on the number of active users, making it a straightforward option that avoids costs for inactive users. This solution works well for internal teams with a clear understanding of user access needs. However, for B2B customer service, estimating user involvement can be challenging, making it difficult to predict the costs of embedded business analytics.

  • Cost per workspace

The company is billed for each workspace, allowing for an unlimited number of users within a single workspace. This option is highly predictable, as pricing can be easily determined by counting the number of workspaces and any additional requirements.

Monetizing Your Data with Embedded Analytics

Embedded analytics capabilities allow companies to monetize their data in various ways. For instance, if you collect data on consumer behavior from your B2B e-commerce site—such as user navigation, saved items, and purchase history—you can share this information with clients or retail brands through dashboards and visualizations in your client portal.

This data can be structured into tiers, offering basic insights for free and charging for more advanced, detailed insights. As clients recognize the value of this data in enhancing their operations and strategies, they are likely to request more in-depth reports and be willing to pay for them. By implementing these free and paid tiers, you can create a new revenue stream and achieve measurable ROI on your embedded analytics investment.

Takeaway

embedded analytics in business processes transforms data usage by integrating insights directly into applications. This enhances decision-making with real-time information and allows companies to monetize their data through innovative pricing models. By adopting embedded analytics, businesses can boost efficiency, improve customer engagement, and gain a competitive edge in a data-driven landscape.

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