When Choosing An Embedded Analytics Solution, Scalability Is Key

In our last chapter on choosing the best embedded analytics software to suit your application, we broke down the various customizations to consider when it comes to the ongoing customization of your brand. In this chapter, we’ll explore a few key things to consider when choosing an embedded analytics software solution that scales with your expected number of end-users.

Does the embedded analytics software scale to the expected number of users?

Definitions

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

Scaling – The process of growing a business, either through revenue, users, or other means.

Here’s What You Need To Know

What size of deployment does the analytics solution support?

The most critical question to consider when choosing an embedded analytics software solution that is designed to scale with your users is this: Can your chosen solution physically support the user volume you’re expecting, as well as the overall deployment scale of your application?

Before you begin to look into the overall scalability of your chosen embedded analytics solution, you should look into a few other important pieces of information about your app. No matter what stage your startup is in nor how much data you have, it would be best if you still had a decent estimate of how many end-users will be running your embedded dashboards.

With this number in mind, consider the overall functional expectations of your BI solution. Will it be used to render relatively simple dashboards? Or will it be generating complex reports? Will your solution be able to take the time to allow ad-hoc report creation from large volumes of data? All of these factors will impact the ongoing load that your embedded analytics software will be able to take on.

Keep these key features in mind when determining the kind of load your solution will have to take on:

• Your server specs.
• The parameters of usage that drive performance, such as the number of concurrent users, the number of queries, the amount of data in your reports and dashboards, etc.
• Your current server infrastructure
• The server infrastructure needed to scale your embedded analytics solution, including new hardware, software, networks, etc.
• How your failover or high availability is achieved.

How does scaling affect user performance?

Your chosen embedded solution will likely work well when your application and startup are small. However, that doesn’t mean it will be ideal as your company scales. That’s why a growth-friendly solution is necessary from the very beginning of implementation.

Look into the caching that exists in your solution. If your app boasts a large volume of users and report generation, you might overload your database. This stress could also cause lag and slow displays, which will affect the user experience. Ask your sales agent what your potential embedded solution can do in instances of high usage. If their solution can do very little in such scenarios, it may be worth looking elsewhere.

Will your users see real-time data or cached data? Is this information vital to your product use case? If performance is a priority and needs to scale, you’ll need to know how your embedded analytics solution performs caching and when and how the solution updates its cache. How is the cache regenerated in the event of a system restart?

How does pricing change with increased (or decreased) end-users?

With many embedded analytics solutions, the cost of the product will change following the volume of users. We covered this issue in our chapter on licensing, but it’s still an important factor to consider in the context of scalability. When you embed a product within your application, it’s often difficult and can disrupt your users. You certainly will not want to be in a situation where you need to opt for a new solution once your user base begins to grow, simply because the BI tool costs of your chosen product have skyrocketed.

Nail down exactly how your increase in users will reflect in the cost of the embedded solution. Conduct as much internal research as possible to make an informed decision about how likely your app is to steadily or abruptly increase in users. This is vital to know to choose a BI tool price point that fits your application budget.

Conclusion

Before you even begin to consider the scalability of your product, you’ll need to consider the actual function of your embedded analytics solution in the context of scalability and user needs. Your solution may need to handle a massive volume of users and complex reports and factual data.

It’s vital to ensure that your chosen solution is designed to handle your unique app’s workload and user requirements.

As the last part of this complete buyers’ guide to embedded analytics, the last chapter discusses everything you must know in integrating the solution with your existing billing strategies to ensure a smooth flow.