Discover the nuances of building a white-labeled customer facing reporting module with enterprise-grade embedded anywhere in your app.
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Build a white-labeled, customer-facing reporting module with enterprise-grade scale and security, seamlessly embedded in your app. With Embedded BI, you can empower your users to access advanced reporting and analytics directly within your application, enhancing their experience and driving data-driven decisions.
Your customers are now generating more data than ever in your app. They love your product, and are demanding more from it in order to improve their experience on your app. However, you are struggling to go-to-market in a timely manner to serve their data needs.
A white labeled embedded analytics product for your end customers with an enterprise-grade BI experience.
Here are key components of the ideal solution:
The goal here is to empower your end-users to gain insights from their data sitting in your app with a powerful analytics experience they are used to.
You’d want to satisfy your customers’ request in a timely manner, so you don’t lose new deals or experience churn otherwise.
Tangentially, you will experience higher retention with better reporting.
Before we get into how DataBrain works, here are some challenges you will face as you think about building a customer-facing reporting module:
Let’s visit each one.
You’ve decided to build your reporting module and as a designer or product manager, here are questions you will encounter:
The goal here is think deeply about what jobs can be done for your end customers using data they’ve generated within your app. You should also think about how to make the dashboard visually appealing, so that your customers can quickly understand their data. Finally, consider how to make the dashboard flexible, so that it can be easily modified as customer needs change.
As a product person this is where you will spend most of your time. This is also one of the most important steps, because if you don’t get the data correct, your end-users are going to lose trust in your product and brand. Here are some basic questions you will need to answer:
Product, Data Analyst
Engineering
While the above list is not exhaustive, there are other complex things to research like versioning metrics, choosing the right data format for the charting library etc. The goal here is to ensure your data is ready for analytics purposes, without needing to hire a data engineer, a business analyst or waiting on your data team. You also don’t want to derail your existing engineering roadmap by sending your engineering team on a research hunt.
Now this is where things start getting serious. You’ve decided you are going to build something but from research, you suddenly realized that there is more than what meets the eye:
Engineering
The goal here is to make sure your customers’ data is secure, and that their data is not accessible or shared with anyone else. You also want to securely provide personalized dashboards based on roles within an org - eg: an Admin dashboard view will be very different to a staff or a member’s view.
If you want to give your customers the ability to create their own reports and download data, you’ll need to ensure you have the right backend engineering infrastructure. Here are some questions you’ll need to answer:
Engineering
Product
The goal here is to enable your customers to gain insights from their data quickly and easily. This will enable them to make decisions faster and improve their experience on your app, without depending on a data analyst or pinging you for data requests.
This is the last piece of engineering that you need to consider when building your customer facing analytics module. Here are some questions you’ll need to answer:
Engineering
The goal here is to ensure your customers can query and explore large datasets quickly, without suffering performance bottlenecks. You also want to ensure as more data is created, performance does not degrade and everyone has the same consistent experience.