Development teams are getting more requests to implement embedded analytics projects. It’s a trend that’s growing the global embedded analytics market from $22.93 billion in value in 2025 to a projected $74.98 billion by 2032.
The calls stem from a realisation that while analytics can be a force for business success, they aren’t always available when needed.
Too many decision-makers can’t access the necessary data or insights – or don’t have the resources to deploy another platform. When developers integrate business intelligence modules in a host app, which is what embedded analytics platforms enable, people are far more likely to consult their data when making decisions.
New research shows that 76% of business professionals admit to making business decisions without consulting data simply because it wasn’t accessible, and the average employee in 75% of organisations spends two to ten hours each week searching for the right data.
When done right, embedded analytics solutions are customisable and user-friendly, increasing access to AI-powered insights for every line-of-business (LOB) decision-maker. But not all embedded analytics projects are equally effective. Some miss the mark, involve too much user experience friction, require too much custom coding and maintenance, don’t match the way that employees use their tools, or can’t stand up to the demands placed on them.
To help your project succeed, we’ve gathered five best practices for dev teams to build embedded analytics capabilities that meet the needs of business users.
1. Take advantage of modularised components
A modular approach to embedded analytics projects helps to speed up development and ensure consistency in the app, and prevent downtime by making maintenance easier. Component-based architecture breaks down dashboards and visualisations into discrete, reusable UI components like chart widgets, filters, and KPI progress displays, which can be embedded in different parts of the app according to need.
Look for SDKs or embedding frameworks that support modular integration, because they’ll enable easier updates and more consistency. For example, ThoughtSpot Everywhere’s SDK offers pre-built, customisable components that can be plugged into apps, with full API access. By decoupling data-fetching logic, rendering, and layout, you can adjust specific elements like a new API or an updated chart library without reworking the entire app.
It’s best to centralise configuration and theme-ing for elements like colour schemes, labels, and behaviour. Updates will go more smoothly and be less risky once you can drive them all through a single config pipeline. Modularisation works to reduce duplication overall and make it easier to scale analytics in your host apps.
2. Optimise for performance and scalability
To be successful, embedded analytics have to match the performance of the rest of the app. This requires steps like using query caching, lazy loading, and pre-fetching to optimise queries and maintain high speeds. Indexed views, limiting data scope, and avoiding expensive joins whenever possible also help to minimise compute and data transfer.
Embedded analytics also need to be as responsive as possible. Visualisations like charts, dashboards, and graphs should adapt to different devices, screen sizes, interaction patterns, which might require lightweight libraries or server-side rendering.
At the same time, plan for both user traffic and data volume to scale, using steps like sharding or load balancing, or moving from embedded static reports to dynamic, cloud-based analytics platforms.
3. Ensure deep integration
It can be tempting to use iframes for your embedded analytics project. While this approach can be fast and easy for dev teams, it supports only limited customisation and lacks advanced features like dynamic filtering or real-time updates.
iframe-based embeds may not scale well as data volumes grow, and don’t deliver full integration between the analytics and the host app’s interface; the whole point of embedded analytics.
In the long term, it’s much better to use APIs and SDKs that embed analytics at the code level, like Pyramid Analytics‘s integrated offering. SDKs deliver integration and personalised user experiences without unsteady workarounds, while Pyramid’s APIs connect to every data source to pull data in real time, building a fast, diverse decision intelligence pipeline.
4. Focus on user needs
User-friendliness is the bottom line in driving adoption, so it needs to be your focus, from the visualisations and data sources offered to suggestions pushed. Design an intuitive interface around how users operate, not which data is available, matching actions to existing workflows. All analytics should be embedded in context to support tasks rather than just showing dashboards.
It’s often best to limit functions only to those which are most needed, so as not to overwhelm LOB users. As people grow familiar with the interface and see what is possible, their requirements will change and you can add more functionalities. On-site usability studies, surveys, telemetry and use tracking can point to how users actually use the app and analytics, and guide you to what to fix and where to improve.
As well as using modular components to build a flexible system that’s easy for you to adjust, give users customisation options too. A drag and drop editor interface, for example, allows people to adapt their views and workflows according to need.
5. Prioritise security and compliance
Robust security and watertight regulatory compliance are crucial to any data project. These are areas that should never be skipped or skimped, especially since the data needs to travel in so many environments.
Enforce role-based access and row-level security, and plan breach and incident response protocols. Ideally, urgent breach response actions should be automated.
Build in compliance with data protection laws like GDPR and HIPAA from the very beginning, with data encryption in transit and at rest. Think carefully about data access, as users need access to data to make decisions, but not be able to compromise sensitive data. In some situations, dynamic data masking using a tool like Privacera can help you walk this tightrope.
Build embedded analytics that advance business goals
Ultimately, dev and business leadership teams want embedded analytics solutions that drive better decision-making. That means making sure the results are easy to use, secure, and deliver insights quickly and reliably. By following these best practices, dev teams can meet business expectations and avoid the frustration of endless tweaks and fixes.
Guest author: Asim Rahal, Evangelist of cloud security, data protection and cyber risk awareness.
Image source: Unsplash