Multi-Tenant Analytics for SaaS: The Top 10 Challenges

As a product manager, you’re constantly juggling never-ending feature requests while trying to deliver a high-performing, engaging, and innovative product that keeps customers happy.

Many of the product managers I talk to say the bulk of those requests relate to customized reports and dashboards. It’s no surprise that customer expectations are changing when it comes to analytics, and users need insights from their data quickly to make business decisions.

Your reporting experience can be a powerful competitive differentiator, but with hundreds or thousands of customers, it’s difficult to address the growing demand for customized reporting without self-service analytics capabilities.

How can you deliver personalized analytics quickly and cost-effectively while maintaining data privacy across tenants?

Here’s the short answer: embedded analytics powered by a solution that supports multi-tenant architecture.

To succeed, implement an embedded analytics platform that delivers a seamless and insightful reporting experience that keeps customers engaged and returning for more.

Let’s go through the top challenges of multi-tenant analytics for software as a service (SaaS) and how to navigate each.

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#1. Build or Buy?

Build multi-tenant analytics yourself or buy an embedded analytics platform? It’s a great debate.

SaaS companies often build everything in-house, and I get it. You have talented developers on staff already building the rest of the product, so it makes sense to do so with analytics as well.

But for companies looking for more interactive and advanced capabilities, such as self-service analytics, or those with complex or varying data formats, there are several reasons you shouldn’t build multi-tenant analytics yourself.

Building is costly (and unpredictable), provides limited functionality to customers and their end users, and restricts long-term growth in favor of having a short-term fix.

Solution: Buy an embedded analytics platform

Buying an embedded analytics platform removes the unpredictability associated with building multi-tenant analytics yourself, saving your organization from wasting already-limited resources.

Here are the advantages of buying an embedded analytics platform:

• Stay focused on your core roadmap: Developers can focus on core functionality and market requirements.

• More comprehensive analytic functionality: Analytics comprises far more than charts and reports. A third-party solution brings broad functionality right out of the box.

• Subject matter expertise: You have a skilled dev team, but do they know analytics? Gartner lists a shortage of data and analytics skills as one of five pitfalls when building data and analytics teams.

• Time-to-market and total investment: An embedded analytics platform reduces your time to market, minimizes the burden on dev teams, and significantly lowers total investment.

• Ongoing maintenance: Building in-house means you’re committing to maintaining in-house as well, which entails not just fixes, but also the entire module lifecycle.

• Predictable costs and reduced risk: Predicting total expenses to calculate ROI is particularly difficult. Third-party tools can provide more reliable cost projections and certainty of outcome.

Still not convinced that building multi-tenant analytics in-house is a bad idea? Compare the costs between buying and building with this return on investment (ROI) calculator.

#2. Satisfying Endless Customer Demands

Customers have high expectations, and product managers must ensure the product meets them today—and into the future.

Feature requests can be endless and run the risk of overwhelming your roadmap. Additionally, users often have varied skill levels and areas of responsibility, which may necessitate different interfaces that deliver the appropriate functionality.

Solution: Self-service analytics

With a practical and well-designed self-service interface, allow users to add custom enrichments to their data, such as building custom formulas or data visualizations, or creating personalized dashboards and reports on the fly.

All of those outcomes result in users interacting with the data inside your system more meaningfully. Self-service analytics also empower them with tools to discover new insights and collaborate with colleagues, helping users derive the highest value from your product and promoting increased user adoption and retention.

#3. Consistent User Experience (UX)

Even the most sophisticated functionality is worthless if your customers can’t easily use it. Product owners invest significant efforts into developing a user interface that’s consistent to use and aesthetically pleasing throughout the application.

Taking users to an interface or analytics process that doesn’t look or feel just like your product can substantially reduce the perceived value and diminish your brand.

Solution: White labeling for brand consistency

When you white label your embedded analytics, you make your charts, reports, and dashboards look like a seamless part of your software, instead of a third-party plugin. White labeling enables you to maintain the same look and feel as all of your app’s functions, which reinforces the brand identity that your team has carefully crafted and cultivated.

#4. Secure Data in Multi-Tenant Apps

Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate instances. It’s vital to prevent data leakage across tenants and ensure that within each tenant, users see only the data sets they’re authorized to see.

Solution: Inherit your Security Model

SaaS providers must grant access according to each user’s role. Record- and column-level security allows administrators to restrict data access at granular levels in a dataset, so each user gets only the information they are authorized to see.

Security tools and features must support multi-tenant SaaS applications and, ideally, will inherit your security model, including all rules and policies. 

If you choose to embed third-party components, selecting products that deploy directly into your own environment is important. With a self-hosted solution, your data never leaves your control; your analytics run entirely inside your environment, inheriting your security policies, which is ideal for data security and governance.

#5. Providing Users with Report Builders

SaaS companies don’t need to embed reports. Rather, they need to embed report builders. Traditional business intelligence (BI) software was never meant for developers. Legacy BI tools typically focus on dashboards and reports only, not the analytics creation process. These tools severely limit developers’ ability to create advanced experiences for end-users. 

Solution: API-first approach with no-code widgets

When it comes to the development of analytics functionality, your team must maintain productivity without traversing a mountainous learning curve. For developers to build analytics functionality, a third-party solution should have an API-first approach with no-code widgets that deliver real value in terms of time and cost savings.

#6. Iframe Problems

Iframes undermine trust in web interactions and are vulnerable to some common attacks, posing a security risk. Iframes also slow page loading and can harm search engine optimization (SEO) and marketing. In addition, they’re challenging to maintain and scale.

Solution: JavaScript-based embeds

Avoid iframe’s countless problems by using JavaScript-based widgets. Embeddable components can include dashboard and chart widgets, dashboard builders, data management, and automation rule management. Embedding should include seamless interaction with the different components inside another software or portal.

#7. Monetizing Analytics & Align Pricing with Value

Software monetization involves the complex processes of choosing pricing and packaging strategies to extract value from your intellectual property (IP) and maximize profitability.

It’s challenging to determine how to construct pricing models that best align value to dollars, but when adding new functionality such as analytics, you could offer it as a free value-add, increase overall cost, offer tiered pricing, or price based on metrics such as number of users or usage volume.

Solution: Flexible deployment options

SaaS monetization in general—and value-based pricing specifically—has great potential to increase revenue and reduce churn. To effectively monetize your app, being able to run market tests from a pricing standpoint is just as important as testing technical features.

Accommodating various pricing models, such as charging for analytics, shouldn’t require a heavy development effort. Your analytics technology should be flexible enough to accommodate various pricing models, including support for tiered subscription models that enable you to deploy different builds among different groups of subscribers.

#8. Maintaining Performance with Unpredictable Use

Embedded multi-tenant analytics often have unpredictable traffic patterns, unlike single-tenant applications. This happens because customers can access analytics independently, each with unique usage patterns and needs.

Users expect apps to be performant, so multi-tenant app providers must establish an architecture that allows every component to remain consistent and responsive, including analytic workflows across various data types and sources.

Solution: Modern architecture and self-hosting

Efficiently handling various tenant needs and large amounts of data requires advanced methods like microservices, container orchestration, and auto-scaling mechanisms. Serverless technology is also a significant development in this area which presents a relatively new and innovative pathway to scaling processes and systems.

With a self-hosted model, you can easily integrate directly with your multiple environments. These typically include development, testing, QA, and production in a manner that supports your specific SDLC.

#9. Integrating Disparate Data Sources

While some apps use only one database, many SaaS providers must combine different data sources. Complex analytics usually require data from multiple, diverse sources for multi-tenant scenarios.

Companies typically build separate pipelines with dedicated extract, transform, and load (ETL) for every data source, and with the extensive variety of sources, the task of data integration can become quite daunting.

Solution: Comprehensive 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 more flexibility, reducing the need for useless transformations and wasted processing.

#10. SDLC Integration

Releasing new apps and maintaining existing ones both require evaluation and testing. Embedded analytics must also fit into the software development lifecycles (SDLC). If you’re beholden to a vendor’s update schedule, it can create unnecessary hassles and delays.

Solution: Self-hosting

An embedded analytics platform that deploys directly into your environment is the solution. This way, you’re never at the mercy of another company’s release schedule or performance scaling.

Self-hosting enables easy integration directly with your multiple environments in a manner that supports your specific SDLC.

Dive Deeper Into Multi-Tenant Analytics

Building multi-tenant analytics in-house seems appealing initially, but as we’ve explored, it often leads to hidden costs, limitations, and ongoing maintenance headaches.

Choosing an embedded analytics platform, such as Qrvey, empowers your SaaS company to:

• Focus on core strengths: Free your developers to innovate and improve your core product.

• Delight customers: Provide powerful, user-friendly analytics that enhances user experience and drive adoption.

• Increase revenue: Monetize your analytics offering and align pricing with value.

• Ensure data security: Maintain complete control over your data and inherit your existing security model.

• Scale effortlessly: Handle unpredictable usage patterns and future growth with a modern, robust architecture.

By opting for a comprehensive embedded analytics platform, you unlock the true potential of your data and deliver exceptional value to customers.

Embedded Analytics Product Fit Guide

Discover how to plan for a successful embedded analytics project in this free guide.

GET THE GUIDE