5 Best Practices for Choosing an Embedded Analytics Platform Provider

5 Best Practices for Choosing an Embedded Analytics Platform Provider

5 Best Practices for Choosing an Embedded Analytics Platform Provider More and more users now expect modern analytics and visualizations that come embedded along with enterprise applications. This does not mean analytics platforms have been created built so that they could be embedded. Product management leaders need to use a few best practices to select the correct embedded analytics provider for their business.

Key Challenges

  • A lot of enterprise application providers try creating analytics in-house. This can possibly impact time to market. A better option would be to source these options from embedded analytics providers.
  • All the stand-alone analytics which are currently offered by software providers sell mostly to end-user organizations. The problem with this is that they are not designed to be embedded into any other enterprise applications, just their own.
  • The usability and the visualization of the embedded analytics solution can have a direct impact on how the overall enterprise application is perceived and the customer experience.
  • More than just basic reporting and dashboards, what end-user organizations are now looking for are enterprise applications that come with embedded machine learning.


All technology product management leaders who are planning to build and market enterprise applications need to keep these recommendations in mind:
  • Keep user needs and skills in mind when creating embedded analytics capabilities. This can be done understanding the users and their decision making.
  • The first priority should always be embedded solutions which lead to a seamless experience. This can be done if the look and feel of the application is taken into consideration rather than two different products.
  • Assess the application's capabilities. Look for more than just the visualizations. Benchmark and test how the analytics perform and how well it integrates with the application.
  • Ensure you add machine learning. This will increase automation. It will also augment decision making. Check for all platforms incorporating machine learning across all of data and analytics pipeline. This will help speed up the process of gathering insights which you can give users for contextualized recommendations to guide decisions.
  • Keep in mind that your service provider has a similar culture as yours. The motivation level needs to match so that your embedded analytics initiative is successful.

Key Takeaways:

  • Enterprise application software needs to have modern analytics and visualizations placed in recent times
  • Users need machine learning and AI incorporated in the software to help generate better and more accurate insights

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