Can The Embedded Analytics Software Properly Integrate With My Existing Data Model?

Can the Embedded Analytics Software Properly Integrate with My Existing Data Model?

In our last chapter, we broke down how to determine if an embedded analytics software product can support your app’s existing tech stack and architecture. This chapter will explore how to effectively and seamlessly integrate your chosen software with your existing data model.

Can The Embedded Analytics Software Properly Integrate With My Existing Data Model?


In this chapter, it might help to brush up on the following definitions:

APIApplication programming interface. Refers to the definitions and protocols developed for integrating app software with other software.
Schema – A design that essentially represents your data stored within your database.
Data Warehouse – A data warehouse tends to be more organized way to store data that is well-structured, archived, and ordered.

Here’s What You Need To Know

We’re not talking about integrating with your data source in this chapter.

When one thinks of integration with embedded analytics software, one will often immediately wonder whether the product will integrate with where the actual data is stored and how it is stored within the application. We dove into this a bit in the previous chapter on architecture and tech stack support. By the time it comes to integration, you will likely already have vetted the business intelligence tool you’ve chosen and understand that it can communicate and query data from the location of your choice, be that a database, a NoSQL database, or via an API.

Do you have a multi-tenant or single-tenant data structure?

There are pros and cons for both multi-tenant and single-tenant data structures. Single-tenant databases offer added security, substantial customization, added dependability, and an easier time restoring backed-up data. Unfortunately, single-tenant databases are often more expensive, cost a lot in terms of maintenance, and are complex for setup and ongoing management. Scaling can also be an issue.

On the other hand, multi-tenant databases are very affordable, are built to be simple to integrate with other apps, leverage APIs, and are easy to scale and maintain. These databases aren’t perfect, though. Multi-tenant databases boast very limited customization options, less effective security, and several issues regarding forced updates.

In the end, many business intelligence platforms are made to query from only one database. In contrast, others can dynamically adjust data source connection strings in real-time, thus working quite well for single-tenant models.

It’s vital to have a good grip on how your chosen BI platform supports your stored application data, so there will be no need to invest a ton of time and energy into forcing a round peg into a square hole.

Do you have your data prepped?

A majority of BI platforms work best with data stored and organized in a new data warehouse. This, naturally, makes it easier to generate reports. In reality, most vendors have data all over the place with few organization processes in place. If this sounds like your situation, try looking into a BI platform that offers features like data prepping, extracting, loading, transforming, etc. If you want your data structures to remain intact, look for a platform that puts minimal pressure on rearranging your existing structures.

How does database change management affect the reports?

Database schemas change and update as the application you are building grows and changes through development. Consider how your databases could affect your chosen BI platform and if the platform has change management features in place.


It will always save you a lot of time and possibly resources if you gather this information about a prospective embedded analytics software product as quickly as possible. The last thing anyone wants to deal with is a potential roadblock with data storage and integration later on down the road after the platform has already been invested in.

Focus on aligning your chosen product with your application’s database storage silos, schemas, and overall app framework. Also, ensure that your chosen product is extremely compatible with business scaling.

The next chapter aims to discuss how you can embed dashboards inside your application as effectively as possible.

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