Choosing an embedded analytics solution

Embedded analytics platforms allow you to easily add insights capabilities to your product – often in less than thirty minutes. By leveraging the data visualization, ease of creation, row level security, and quick setup of a purpose-built analytics tool, you can add sophisticated analytics to your own product.

In this blog post, we’ll explore several popular embedded analytics solutions, including Sigma Computing, Power BI, Tableau, Metabase, Zing Data, and Looker.

We’ll compare their features, pricing models, and use cases to help you make an informed decision for your business.

Embedded analytic versus traditional Business Intelligence tools

Legacy BI tools often are a poor fit for embedded analytics use cases:

1. Limited customization: Many BI tools offer limited design flexibility, making it difficult to match your brand and user experience. Embedded analytics should feel native to your application to end users.
2. Complexity: Tools designed primarily for internal business users can be overwhelming for end customers if the full report authoring experience is embedded.
3. Performance issues: Embedding through iframes or similar methods can lead to slow load times and a disjointed user experience.
4. Scalability concerns: High per-user pricing can become prohibitively expensive as your customer base grows.
5. Lack of interactivity: Some solutions offer limited options for users to explore and interact with data.

Now, let’s look at options, including Zing Data, and how they score on core embedded analytics needs.

Embedded Analytics Solutions Comparison

Here’s a quick comparison of some popular embedded analytics solutions:

Now, let’s dive deeper into each solution:

1. Zing Data: A Flexible Embedded Analytics Solution

Zing Data has a unique offering in the embedded analytics space, with flexibility to embed individual questions, whole dashboards, and even full creation of analyses in-situ. Natural language querying provide a search-like experience, and saved questions provide a great starting point for going deeper.

Key Features:

• Javascript, React SDK and iframe embedding options for flexibility
• High level of customization to match your brand - including colors, fonts, gridlines, myriad chart types, labels, etc.
• Natural language querying for interactive searchbox querying
• Works on real-time data – no replication needed
• Support for row level security and complex tenancy environemnts (including per-tenant schema and data source for HIPPA and sensitive data environments)
• Self-service analytics for end-users
• User-based pricing model for better scalability with no high platform fees

What Users Say:

“Zing Data’s embedding options and customization capabilities have allowed us to create a seamless analytics experience for our users.” - Head of Product at a SaaS company

2. Sigma Computing

Sigma Computing is a cloud-based analytics platform known for its spreadsheet-like interface.

Key Features:

• Spreadsheet-like interface for familiar user experience
• Embeds through iframe
• Some customization options

Limitations:

• Limited design flexibility for embed formatting
• No natural language querying, meaning less modern user experiences and more need for pre-creating each question variant
• Costly, with high platform fee ($30,000) plus per-user costs that are much higher than other offerings
• Iframe embedding can lead to performance issues
• Limited data warehouse support

3. Tableau

Tableau is a popular legacy BI tool known for its powerful data visualization capabilities.

Key Features:

• Wide range of visualization options
• Connects to multiple data sources
• Embeds through API for more control

Limitations:

• Steep learning curve on creation
• Can be expensive, especially with per-viewer price point
• Limited customization for embedded analytics
• Slow to load in

4. Explo

Explo is a purpose-built embedded analytics platform designed with end-users in mind but isn’t well suited for serving internal BI needs.

Key Features:

• Modern interface
• Supports both iframe and web component embedding
• Configurable and downloadable reports

Limitations:

• Expensive - whitelabeling of dashboards starts at $1,995/mo
• Limited customization options compared to some alternatives

5. Metabase

Metabase is an open-source BI tool that offers embedded analytics capabilities.

Key Features:

• On-premise and cloud-hosted options
• SQL support for complex queries
• Interactive visualizations

Limitations:

• Limited customization options
• No natural langugage querying
• Not well suited for sensitive data given limitations on how multi-tenant data sources are handled
• Performance issues with larger datasets
• Basic visualizations compared to some alternatives

6. Embeddable

Embeddable is an SDK toolkit for building custom analytics experiences.

Key Features:

• High level of customization
• Web component embedding for native-feeling experience

Limitations:

• Requires more development resources to implement
• Not well suited to also serve internal BI use case
• May have a steeper learning curve for non-technical users

7. Looker Embedded

Looker Embedded is a comprehensive embedded analytics solution from Google Cloud.

Key Features:

• Highly customizable with JavaScript API
• Robust data modeling layer
• White-labeling options

Limitations:

• Can be complex to set up and maintain with every table and dataset requiring LookML modelling set up
• Pricing is very expensive (typically $50,000+/yr starting price plus high per-user fees)
• Requires significant technical expertise to fully utilize

8. Power BI Embedded

Power BI Embedded is Microsoft’s offering for embedding analytics into applications.

Key Features:

• Integrates well with other Microsoft products
• Supports both cloud and on-premises deployment
• Capacity-based pricing model

Limitations:

• Learning curve can be steep for non-Microsoft shops
• No em
• Poor fit for data environments that are not MS SQL or MS Fabric – such as Clickhouse, Postgres, MySQL, etc.
• Customization can be challenging and limited
• Performance can vary depending on data volume and complexity

Choosing the Right Solution for Your Needs

When selecting an embedded analytics solution, consider the following factors:

1. Customization Needs: For high levels of customization, consider Zing Data, Embeddable, Looker Embedded, or building a custom solution.
2. Pricing Model: Evaluate how each solution’s pricing model aligns with your growth projections.
3. Ease of Implementation: Consider your team’s technical capabilities and the resources required for implementation.
4. Data Complexity: For highly complex data needs, Tableau, Looker, or Power BI might be more suitable.
5. Integration Requirements: Consider how well the solution integrates with your existing tech stack.

Conclusion

While traditional BI tools like Sigma Computing and Tableau have their strengths, newer solutions like Zing Data are challenging the status quo by offering highly customizable and scalable embedded analytics options which also serve core internal business inteliigence use cases. Established players like Looker Embedded and Power BI Embedded also provide robust solutions, but are typically best suited for businesses already invested in their respective ecosystems.

Zing Data is somewhat unique among the companies discussed since it offers embedded analytics, core internal BI, and natural language AI querying which makes it more flexible than legacy tools which only serve a subset of these use cases. Further, a pricing model that doesn’t require a high platform fee to start using it allows for quick experimentation.