In the competitive world of software, creating a data experience for your customers is a powerful way to deliver value to your customers and differentiate your product. Self-serve analytics, which enables end-users to generate their own reports without having to make a data request to your team, takes this one step further. 

It’s an approach that empowers customers to unlock the full potential of the data at their fingertips, which not only enhances engagement, but also increases retention and can lead to upsell opportunities. 

In this article, we deep dive into….

What is Self-Serve Embedded Analytics?

Self-serve embedded analytics refers to the integration of analytics tools directly into software applications, allowing end-users to not only interact with data but also to extract ad-hoc insights and build their own custom views on the data, without needing to leave the app. 

These tools provide customers with a seamless experience, enabling them to access real-time data, create custom dashboards, and perform data analysis without needing external support. This form of analytics is specifically designed to be user-friendly, ensuring that even non-technical users can leverage data insights to make informed decisions within the application.

Where embedded analytics refers to any analytics which are embedded into a customer-facing application, self-serve analytics is a sub-category which enables end-users to interact more flexibly with the data that’s presented. This is often achieved by enabling end users to modify the data that they see on the dashboard or analytics experience, and in some cases to query the underlying data using a no-code or SQL-based querying interface.

The Benefits of Self-Serve Analytics

The increased demand for self-serve embedded analytics is driven by the value that can be gained from putting the power in the hands of customers. There are operational efficiencies that can be gained for internal teams, whilst it can also deliver increased value to end users, which ultimately increases their perceived value of your solution and enables you to improve your products’ performance against KPIs like engagement, retention and revenue.

• Empowerment and Agility: Self-serve tools empower users by giving them direct access to data insights, enabling quicker decision-making and fostering a culture of data-driven innovation.
• Reduced IT Bottlenecks: By decentralizing data access, organizations can reduce the strain on data and engineering teams, allowing them to focus on building new features and innovating, while end-users handle routine queries themselves.
• Enhanced User Experience: When embedded directly into applications, analytics tools offer a seamless experience, enabling users to access insights within their everyday workflows without needing to switch platforms.
• Scalability: As businesses grow, the ability to scale analytics capabilities across various departments and teams becomes crucial. Self-serve analytics tools are inherently scalable, making them an ideal solution for growing enterprises.

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What Makes a Great Self-Serve Embedded Analytics Experience

Implementing high-quality embedded analytics is no trivial exercise - there’s a lot of requirements and, as a result, the amount of underlying complexity in analytics experiences is deceptively large.

Broadly, you’ll likely want to make sure that the end-user experience has the following features:

• Intuitive Interface: The user interface should be clean and easy to navigate, enabling non-technical users to create and customize reports without extensive training.
• Customizable Dashboards: Users should be able to personalize their dashboards to reflect the metrics and KPIs that matter most to them. These preferences should be storable, so that your user does not lose their work.
• Real-time Data Access: For timely decision-making, it's often important that users can access and analyze real-time data.
• Advanced Filtering and Segmentation: Users should have the ability to filter and segment data based on various parameters to uncover deeper insights.
• Collaborative Capabilities: The tool should allow users to easily share insights and collaborate with others within the organization.
• Security and Governance: While data access is democratized, it's critical that the platform has robust security and governance features to ensure data integrity and compliance.

This is a list of high-level key requirements only, and by no means exhaustive. As you look into stack compatibility, control and customisations preferences and data processing you’ll need to develop quite a broad range of requirements. Due to the complexity of delivering a first-rate self-serve analytics experience, there are a number of tools that have emerged to allow you to embed these kinds of experiences into your application.

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How to implement Self-Serve Embedded Analytics

If you’re put off by the complexity of developing a complex data interface in-house, which would be understandable, there are embedded analytics solutions that can help you deliver the user experience you have in mind, without the engineering build and maintenance overheads.

We’ll cover that in a bit more detail below but before you get started, you’ll want to make sure you have considered some key factors so that you can better judge the solutions on offer. 

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What to Consider when Building Self-Serve Embedded Analytics

How native do you want the experience to feel?

Most off-the-shelf tools will provide you with their charting components (line chart, bar chart, etc.) and some degree of control over the colors, fonts etc. - however it’s important to be aware that the degree of flexibility you have to modify and extend the charts provided is inherently limited. Using a headless embedded analytics tool may be a better solution if you want it to look and feel like your platform.

Are fast loading times important? 

Loading data really fast for customers can be a tricky technical problem. The database response times are one issue, but you’ll also need to consider the additional loading times that embedding via an iframe can introduce. It’s worth exploring tools that can offer a built-in caching layer and embed via a web component/JS code snippet (so you won’t have to load a third party application before the data request can be sent). This will also give you a more native-feeling experience and make it easier to handle user authentication. If you need real-time data then you’ll want to consider how fast your underlying database can respond to requests.

What degree of control do you want to offer your users? 

There’s a sliding scale here. On one end, you’ve got a static dashboard with some filtering options. At the other end of the scale is full embedded BI, which enables end users to query data in SQL or via a no-code query builder. If your end-users are non-technical, or if data exploration is not the main function of your solution, then you may want to find a middle ground that’s intuitive but still flexible and powerful.

Do you have data security and data sovereignty concerns?

Depending on whether you’re handling sensitive data, or have requirements from customers to manage and maintain their data in a particular location or a particular way - you may run into some issues with certain providers and their approach to hosting and processing data. This can be a complex topic, so try to look for certifications like SOC2 and ask about data hosting options.

Is the tool compatible with your stack?

While both iframes and web components (the two primary embedding options) can be embedded almost anywhere, you’ll also want to select a tool that is compatible with your database, whether it’s a multi-tenant or single-tenant architecture; ensure you’re able to dedicate appropriate engineering resource if you take a tool with a headless architectural approach; and whether the implementation method aligns with your normal engineering and CI/CD processes.

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How to choose an embedded self-serve embedded analytics solution 

The first thing you’ll want to do, is to decide if you’re going to go for an in-house build, a headless embedded analytics solution, or a traditional embedded solution. These are the three approaches for building customer facing analytics into your application.

An in-house build

Building a simple, static dashboard in-house can be a reasonable option if you need something bespoke and extensible. However, when you start to get customer requests for more insights, export options, scheduled email reports, the ability to self-serve data requests, etc. – this quickly becomes a daunting product, design, engineering, QA and data team task that is likely to distract them from innovating and building out the core value of your product.

Given that you’re here because your customers are asking for more power to explore and query the data, and not just looking for a simple static dashboard interface, we’ll assume for the sake of this article that you probably won’t go for the in-house build option.

If you are opting for a custom build you’ll want to consider using one or more charting libraries like amCharts or Material UI X Charts as a base for developing the charting components that your customers will be interacting with. 

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A traditional embedded analytics tool

Opting for an ‘off-the-shelf’ tool will save you on engineering time overall, and allow you to deliver much faster. Some of these tools have been around for a long time and often have extensive feature sets, however they do not all offer self-serve functionality so it’s worth narrowing down which ones do before you start researching. 

Those which offer self-serve functionality do so in different ways, with some offering the ability to add/remove/reorder charts and others offering full querying functionality. You may not be able to decide what degree of self-serve you offer to customers, so be sure to get a demo of this functionality to ensure it works in the way that you envision for your customers.

It’s also worth considering what degree of control you’ll get over the look and feel of the analytics, and whether it will need to be embedded via an iframe or a more progressive approach like embedding via a web component - as these factors can degrade your end users’ experience.

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Headless Embedded Analytics (Recommended)

A headless embedded analytics tool is a tool which handles the backend complexity and logic for you, whilst giving you control over the frontend UX and UI down to the pixel. This approach was conceived to give product and engineering teams the ‘best of both worlds’ - the full control and flexibility of delivering your dream customer experience that you get with an in-house build, but with all of the convenience of an off-the-shelf tool. 

Some of the main benefits of this approach are:

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• Full control over the UX/UI, down to the pixel
• Faster loading speeds
• Native-feeling user experience
• Low engineering effort for build and maintenance 
• Highly compatible and extensible

If this approach sounds like it could fit your project’s requirements, then you can find out more at Embeddable.com.

Final notes

Self-serve embedded analytics represents a significant shift in how organizations access and use data. By empowering users to generate their own insights, organizations can drive faster, more informed decision-making and save time for your internal teams. However, successful implementation requires careful consideration and planning.

Consider what you want your end user experience to be like, and use this as a guide for choosing the right approach for delivering your project. A headless approach may be the best option if you want fast loading, native-feeling embedded analytics.

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