What is Embedded Analytics?

What to Expect in This Article:

1. What is the definition of embedded analytics
2. What are the benefits of embedded analytics
3. What is the difference between embedded analytics and traditional business intelligence (BI)
4. What are some examples of embedded analytics use cases
5. What are the common features of a good embedded analytics solution
6. What are the common pitfalls of embedded analytics

 

What is Embedded Analytics?

Embedded analytics refers to analytic capabilities integrated directly into SaaS applications rather than requiring users to access separate business intelligence tools to analyze data created within a SaaS application. The primary objective of an embedded analytics solution is to enable users of a SaaS application to harness the power of business intelligence with the data created by them inside a particular SaaS application. This eliminates the need to export data only to import it into a separate business intelligence tool.

What Are The Benefits Of Embedded Analytics

Embedded analytics for SaaS applications can provide significant advantages for both the software vendor and end users. By embedding analytics capabilities like dashboards, reporting, and predictive insights into a SaaS application, vendors can enhance their offerings and provide additional value for customers.

Monetize your analytics

A major benefit for vendors is the ability to monetize embedded analytics in several ways, through premium user tiers that unlock more advanced capabilities, add-on products that extend functionality, and professional services to help customers analyze their data alongside professionals. This presents new revenue streams beyond standard software subscriptions.

Access Real-Time Data

Embedding analytics also allows for access to real-time data within the application environment users are already working in. Rather than exporting data to analyze in another tool, insights are available immediately within the workflow. This leads to stickier applications that users rely on more heavily as a single source of truth.

Higher Customer Satisfaction Rates

Higher customer satisfaction is another benefit, as users can get answers to their business questions quickly without the need for technical skills or leaving the software. The most users can do with a SaaS application, the more they come to rely on it. 

Avoid Building

Engineering teams that avoid building their own analytics tools and instead use vendor solutions also benefit those vendors. Vendors save on development costs and time to market when customers use their analytics SaaS applications rather than building their own. This increases the value of the vendors’ offerings. Read our 9 reasons why you should NOT build your own embedded analytics software.

What Are The Difference Between Embedded Analytics And Traditional Business Intelligence (BI)

The primary difference between the two is traditional BI software is for internal use cases and embedded analytics is for external use cases. Additional differences include:

 

Embedded Analytics is for: • External use cases • Multi-tenant data environments • SaaS apps managed by DevOps • Unlimited users

Traditional BI is for: • Internal use cases • Single-tenant data environments • Servers managed by IT • Single users

What Are Some Examples Of Embedded Analytics Use Cases

SaaS applications exist in all industries today, therefore embedded analytics serve a great need across any industry. Today, nearly all SaaS applications are expected to have a strong analytics offering, so if you only offer static, generic dashboards, your customers are likely left wanting more. Some popular industry use cases include:

• Healthtech companies that are generating healthcare-related data
• eCommerce platforms that need up-to-the-minute reporting on sales and inventory
• Cybersecurity platforms that need to combine reporting with real-time alerting workflows
• Financial technology solutions that want high-level transaction aggregation while being able to drill into individual transactions
• Operational analytics platforms that aim to help companies analyze workplace productivity

 

What Are the Features Of A Good Embedded Analytics Solution

Everything is Embeddable

A proper embedded analytics solution should provide multiple components that are fully embeddable using Javascript, avoiding iframes for a seamless user experience. Components include dashboard and chart widgets, dashboard and chart builders, data management, automation rule management, and more.

Components for Both Viewers and Builders

It should include both dashboard builder components to enable custom dashboards as well as viewer components to display standard and custom dashboards. The builder components are essential to unlock custom reporting within each tenant in a SaaS application.

White Label and UI Customization

The UI should be customizable to seamlessly blend into your SaaS application. You want the user experience to be consistent.

Native Multi-Tenancy

Out-of-the-box multi-tenancy is essential for SaaS use cases. This also directly affects the time to market as many solutions require extensive custom development to force multi-tenancy. 

Easy Data Integration

Pre-built database connectors and easy-to-use APIs are essential to rapid integration and fast time to market. Additionally, support for both structured (SQL) and semi-structured (NoSQL) natively means additional flexibility reducing the need for useless transformations and wasted processing.

Deployable to Your SaaS Platform Environment

Being able to deploy the embedded analytics platform to private environments for maximum data security is a crucial feature in maintaining control over data. Furthermore, this method will inherit your existing security policies rather than relying on a third party to manage your data.

Unlimited User Licensing

An ideal solution will provide unlimited user licensing as predicting usage within a SaaS application is nearly impossible. Most traditional business intelligence solutions can only offer user licensing and that tends to be a blocker to adoption.

Install Within Multiple Development Environments

The ability to deploy to code repositories and multiple development environments to fit into software development lifecycles (SDLC). 

What Are the Common Pitfalls Of Embedded Analytics

User-Based Licensing

This is a significant cost driver and often an underappreciated cost over time. Companies that try to “start small” rarely realize the ROI of their investment.

Server Fees

Some traditional BI solutions that began as a solution dependent on server installations may still require licensing for each server their software is installed on. Trying to integrate with a software development process or scale a cluster becomes cost-prohibitive over time.

Forcing Data Transfer To A Third-Party Cloud

An ideal solution keeps your data right where it is…in your environment under your control. You’ll need to do a comprehensive security audit if you send your customer’s data to a third-party cloud.

No Support For Development Environments

As a SaaS company, you have a development lifecycle that is different than an internal IT department in a large company. When you cannot install your embedded analytics software in several development environments, you’re taking chances with your production experience and ultimately your user experience. 

Lack of Built-in Scalability 

You want this part of your SaaS application to grow and expand, but embedded analytics solutions that don’t scale easily or natively often create a bottleneck that becomes expensive to fix. Be sure to always ask any prospective vendor how they achieve flexible scaling.

Not AI Ready

Many offer something related to AI, but is it useful? Many companies use the acronym AI loosely so be sure it’s something that adds value over the long run as AI technology is advancing fast.

iFrame Embeds 

Javascript-based widgets are the preferred method, but you would be surprised how often iFrames are in use today. And to make life harder during an evaluation, the type may be hidden or a vendor might use a combination of widget types adding unnecessary confusion about what type to use for certain use cases. 

Lacking Mobile Responsiveness

You likely spent extra time during design and development to create a SaaS application that is usable on both desktop and mobile devices. Be sure your embedded analytics components are equally responsive to avoid user experience pitfalls.