Embedded analytics is an effective way to provide actionable insights to business users without them leaving your website or app. It has countless benefits, from improving data literacy, increasing the value of your product, helping business users make data-driven decisions, driving expansion revenue, and many others. But with so many tools in the market, how do you choose the right embedded analytics platform?
Today, we're showing you the most important embedded analytics requirements you should look for to make your end-users and developers happy.
What is embedded analytics?
Embedded analytics is the integration of data analysis and/or business intelligence (BI) capabilities directly within business applications, products, or workflows.
Instead of accessing insights from standalone analytics platforms, business users view reports, dashboards, visualizations, and other analytical content within the apps they already use every day.
There are several ways to get access to embedded analytics:
- Building embedded analytics dashboards on your own from scratch
- Using your existing BI tool (Tableau, Power BI, Looker) and their embedded analytics offer
- Using a dedicated embedded analytics tool
Whichever method you choose, the embedded analytics requirements stay the same. The end goal is to provide seamless access to data to business users and help them make data driven decisions.
PS. We have a separate article on the differences between embedded analytics and business intelligence.
Top embedded analytics requirements to look for in 2025
Whether you want to use a separate analytics tool or embed your existing BI platform in your product, the core set of requirements stay the same. These are some of the must-haves if you want to provide a superb user experience, all the while making it easy for your dev team to make embedded analytics work.
1. An SDK or API for easy embedding
If you ask your dev team, one of the most important embedded analytics requirements is the ability to embed via API or a dedicated SDK. The one option you want to avoid is iframes, because...
- It can introduce security risks, especially cross-site scripting (XSS) and clickjacking attacks
- It increases the loading time because they load an entirely separate page or application, leading to slower rendering
- It's harder to achieve responsive rendering with iframes, as they do not automatically adjust to the parent container’s size
- The CSS and styling customizations are severely limited, making it nearly impossible to fit the embedded BI dashboard seamlessly in an app or website
- There are potential authentication and session management issues
On the flip side, the best embedded analytics tools use an API or an SDK, giving your developers full flexibility over the look and feel of your embedded dashboards and reports.
2. Integrations with various data sources and business platforms
To get to the data driven decision making part, you first need some data as input. Your ideal embedded analytics platform should play well with your existing tech stack and allow your data analysts to connect the data sources, clean and model the data, and prepare it for visualization.
Look for some of the following integrations:
Cloud data warehouses: Snowflake, Amazon Redshift, BigQuery, Azure Synapse, and others.
ETL and data pipeline tools: Fivetran, Stitch, Matillion, Airbyte, etc.
Databases: MySQL, PostgreSQL, MongoDB, and SQL Server.
SSO providers: Okta, Auth0, or Azure AD.
CRM systems: Hubspot, Salesforce, Zoho and others.
"Last mile" data sources: Google Sheets, Excel files, or CSVs in Amazon S3
3. A great variety of data visualizations
Depending on the data set you're working with and the end goal you want to achieve, you could use any number of data visualizations. Bar charts, scatter plots, box plots, Sankey diagrams, you name it. A great embedded analytics platform has a visualization for every reporting need out there.
Most traditional business intelligence tools have a basic set of chart types to use. However, you should ideally look for an analytics solution that supports using your favorite charting libraries. This gives you the flexibility to show your business data in the most impactful way possible. Using a headless embedded analytics tool like Embeddable gives you infinite flexibility on the charts, styling, and functionalities.
4. Self service capabilities
Sure, your data scientists and engineers know how to do data analytics. However, the ideal embedded analytics solution is one that is simple enough so that your end-users can use it too. For example, your product is a CRM, and you want to allow end-users to create dashboards focusing on exploring pipeline and generated sales.
A great embedded analytics platform helps businesses create self service capabilities for their own customers. This way, the end user can, e.g., engage in predictive analytics and forecast future sales based on past performance.
Self service analytics is often one of the most important requirements for truly integrating analytics in your app or website.
5. Access to real time insights
For internal reporting, access to real-time data is not always a necessity. Dashboards and reports are pulled on a regular basis (e.g., daily, weekly, monthly), so you don't need access to data 24/7.
On the other hand, embedded dashboards typically require the most accurate and up-to-date information for the analytics functionality to work properly. For example, if your customer runs an e-commerce store, they'll need the most up-to-date sales data to make decisions on the spot.
6. Scalability in features and pricing
Most software companies want to grow their user numbers, and that carries over to embedded data visualizations. The right embedded analytics platform should easily scale and allow you to add more users and features with a few clicks.
There is also the embedded analytics cost, as you're typically charged per user, whether it's someone who creates data visualizations and dashboards or someone who just views them. Adding new users should be more than just easy. It should also be relatively affordable without forcing you to opt for extremely expensive plans that can easily go into six figures per year.
7. Security and compliance
Your embedded analytics platform doesn't only handle your data. More importantly, it handles your customers' information, which is why data access is one of the most crucial embedded analytics requirements.
Here is why it should be a concern when choosing your embedded analytics platform:
- Protecting sensitive data ensures that when you analyze data, you don’t expose business-critical or personal information to unauthorized individuals.
- Ensuring regulatory compliance helps businesses avoid costly fines and legal repercussions from laws and frameworks like GDPR, SOC2, HIPAA, or CCPA.
- Maintaining user and customer trust safeguards brand reputation by preventing breaches or data misuse incidents.
- Gaining competitive advantage positions a product favorably against competitors by clearly demonstrating strong security and compliance.
- Preparing for audits simplifies regulatory assessments through built-in audit trails, logging, and transparency tools.
- Reducing risk exposure proactively defends against cyber threats that could compromise embedded analytics and your entire application.
Top 10 signs you need a new embedded solution
You may be wondering if your business intelligence or embedded analytics platform is meeting the needs of your end users. Here are some tell-tale signs to look for something new.
1. Low user adoption
- Users rarely access analytics features or they don't even open their embedded dashboards.
- End users prefer to export data into external tools rather than using the embedded analytics solution you provide them.
2. User frustration and complaints
- There are frequent complaints about unclear or complicated dashboards. Users can't access the analytical capabilities of your tool because the user interface is confusing or requires advanced analytics knowledge.
- Users regularly request training or documentation to understand the analytics provided. They are miles away from data driven decision making because they're still struggling with the basics, e.g. how to analyze data, connect data sources and similar.
3. High volume of custom report requests
- Users constantly ask for custom data views or reports, suggesting the current analytics don’t meet their needs. This defeats the purpose of self service analytics and requires constant time and attention from your dev team.
- Overreliance on internal teams to generate analytics manually indicates poor usability or inadequate functionality. Instead of working on internal analytics, your data analysts, engineers and other team members are constantly pulled to the side. This impacts both employee and customer satisfaction.
4. Slow performance and load times
- Users experience noticeable delays when loading or interacting with reports. When users interact with your embedded analytics project, it takes a long time to refresh the data, preventing users from real time analytics insights.
- Analytics responsiveness fails to meet expectations, causing users to lose patience or abandon usage. Users expect their embedded dashboards to load as quickly as your website or their Netflix app and they simply leave because of frustration.
5. Data inaccuracies or inconsistencies
- Users report discrepancies or mistrust the data accuracy. The analytics functionality doesn't work because of errors with your data sources or different versions of the same data set.
- Confusion arises due to multiple conflicting sources of truth. Different users or company departments create dashboards and reports, with different outcomes, leading to frustration and poor user satisfaction.
- Using a semantic modeling layer like Cube can solve this solution for you - this comes built-in with Embeddable, or you can connect to your own Cube instance.
6. Lack of actionable insights
- Users struggle to make meaningful decisions based on provided analytics. They don't understand the key performance indicators because of reasons like poor choice of chart types, lack of tool tips and clear insights and similar.
- The solution provides raw data but lacks clear visualizations or summaries that directly aid decision-making. The embedded analytics software should allow them to choose the best dashboard type for their chosen data set and end goal.
7. Limited interactivity or poor user experience
- Users find the dashboards static, limited in drill-down capabilities, or hard to navigate. They can't do their own data analysis and they're forced to merely interpret the static data.
- Outdated visuals and poor design negatively impact user engagement. Users compare your embedded dashboards to similar products and realize that your design and UX is outdated.
8. High churn or abandonment of analytics feature
- End users try the analytics once or twice, then rarely return. They don't see the value in having readily available data insights.
- Analytics features have declining engagement metrics despite steady usage of other product features. Users don't know how to use the analytical capabilities offered.
9. Technical support overload
- Excessive tickets or customer support requests related to analytics indicate usability or reliability issues. Instead of end users handling your embedded BI software on their own, they flock to your data team.
10. Difficulty scaling or updating reports
- Internal teams spend significant effort maintaining or updating analytics, reducing agility and stealing time away from other business processes.
- The solution fails to accommodate evolving business requirements or new use cases quickly.
Choose Embeddable as your embedded analytics solution
The right embedded analytics solution is one that doesn't compromise on key features, is fully flexible and customizable and has simple, transparent pricing. Instead of looking at traditional BI tools, choose dedicated embedded analytics software instead.
Here are some of the reasons to choose Embeddable as your next embedded analytics solution:
- Easy integrations: with flexible APIs and detailed documentation, your developers will have an easy time integrating analytics in your product or app, saving you time and resources.
- Highly customizable: our SDK lets you create embedded dashboards that feel like a natural part of your product. It’s infinitely extendable, and you can even bring in your own charts from your chosen charting library.
- Secure and compliant: Embeddable comes with built-in data security protocols, role-based access control, and compliance with industry standards.
- Fully responsive: our embedded analytics platform has great user experience no matter which device or browser you use to access your dashboards.
- Exceptional performance: Supported by a performant data service and configurable caching layer, Embeddable dashboards load quickly and handle large volumes of data without compromising speed or reliability. Your users benefit from responsive and smooth analytics interactions, no matter the data volumes.
- Advanced interactivity: Embeddable comes out of the box with intuitive features like filters, exports, localization and a self-serve dashboard builder for your end users. Embeddable’s interactive capabilities enable users to explore data deeply and discover critical insights effortlessly.
Ready to give your users the embedded analytics experience they deserve?