Top 3 Self-Service Analytics Mistakes by SaaS Companies

Despite analytics being a vital component of SaaS applications, vendors often make several self-service analytics mistakes. We’ll look at the top three and explain the best practices to steer clear of these mistakes and do it right.

Why do businesses need self-service analytics in their applications?

There are many ways to look at data. Users approach their analysis differently. The goal of self-service analytics is for users to serve their own specific needs and answer their own questions in order to make better, faster, more informed decisions.

End users, even non-technical ones, should be able to easily make edits and customize elements to arrive at exactly what they need. Ideally, self-service analytics solutions enable everyone in an organization to access the most up-to-date information rather than limiting access to a handful of executives or business analysts.

While these are all laudable goals, too often, SaaS providers stumble in achieving them. The reality, however, is that it helps create a competitive advantage that most product managers struggle to understand.

1. Inaccurate Data and Reliability Issues

The only thing worse than no information would be incorrect information. Inaccuracy is particularly detrimental if users aren’t even aware of which data points are incorrect. It’s one thing to be forced to work with incomplete or out-of-date info, but if the user doesn’t even know which data sets or date ranges are omitted, they’re stuck flying blind.

It’s even worse if data is included when it shouldn’t be, such as displaying detailed salary information to everyone, instead of only HR leadership.

Reporting begins with data and unfortunately, building the necessary data sets often requires the work of technical staff. This requirement effectively eliminates access to self-service analytics for those without the requisite data science expertise.

These mistakes can effectively be avoided with the following best practices.

Data Validation and Cleansing

Maintaining data integrity can be challenging due to less-defined structures. It’s important to validate data, checking the accuracy and quality of source data, before generating analytics. A unified data pipeline that includes data transformation substantially eases this process.

Integrate 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 must build separate pipelines with dedicated ETL for every data source. With the extensive variety of sources, the task of data integration can become quite daunting. 

Most data sources require independent data ingestion and ETL pipelines creating a large overhead of independent data pipelines. Without Qrvey, engineering teams must build:

• a data pipeline for any necessary data transformations
• another database or data warehouse specifically for analytics
• a semantic layer that adds multi-tenant security logic
• an entitlements database that maps to user security in the overall SaaS application
• and finally, an API layer so the front end can make calls to its back end.

That’s a lot to build and maintain over time.

Check out our with and without Qrvey graphic.

Clear Definitions and Context for Each Metric

Put simply, people need to know what they’re looking at. Our first two best practices dealt with data engineering behind the scenes and the resulting impact on what’s displayed to the users. This is a best practice pertaining to what the user sees on their screen. Some ways to ensure analytics are crystal clear to users include:

• Clear labels, including date range of data being analyzed
• Mouse-over capabilities to reveal additional details such as the datasets included in the graphic
• Interactivity, so users can click and go deeper into the data
  • Drill down enables users to go from a high level to a more granular one, for example from country to state.
  • Drill through takes the user to a report relevant to the data being analyzed, passing to another report while still analyzing the same data set.
• Label chart modules appropriately
• Enable custom labels, including graph titles, axes, and footnotes
• Eliminate visual clutter, consider removing non-essential elements and use whitespace

Multi-Tenant Data Security

If you’re deploying a multi-tenant SaaS application, you’ll need the proper configuration to ensure tenant data security. In particular, prevents data leakage across tenants and ensures that within each tenant, users see only the data sets they’re authorized to see.

Many SaaS apps include sensitive information such as PHI, salaries, FHIR analytics and intellectual property. While building self-service analytics, SaaS providers must grant access to data, insights, and applications according to each user’s role.

Record- and column-level security allows administrators to restrict data access at a granular level 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 of your rules and policies. 

2. Complicated User Experiences

Self-service analytics deliver “DIY” functionality to non-technical end users. Users can interact with analytics, combine data in different ways, and ultimately, build their own forms, charts, reports, metrics, or even workflow automation.

Self-service analytics should remove technical barriers so that no technical skills or advanced data science insights are required.

Simple, Drag-and-Drop Ease of Use

Self-service analytics must enable all business users –including non-technical users– to create their own dashboards and reports. To meet this vital requirement, provide your end users with easy-to-use, drag-and-drop simplicity. Users should be able to simply point and click to build feature-rich charts, reports, and dashboards.

Specifically, your analytics should be “no-code.” No-code software is a category of tools and platforms that allow users to create and customize applications, websites, and workflows without the need for traditional coding knowledge or skills. No-code builders are a requisite for successful self-service analytics functionality.

Check out our interactive demo center to see our no-code builder in action.

Intuitive, Interactive Interfaces

Seamless, intuitive self-service analytics can enhance customer satisfaction and engagement. Analytics should visualize data, allowing for easier comprehension and interpretation of complex data sets. Instead of deciphering numbers and tables, visual representations provide a more intuitive understanding.

Guided Pre-Built Templates 

It may sound like a contradiction to advocate offering both the capacity for users to build whatever they want, while also providing pre-built templates, but both are important. Self-service analytics should empower every user to answer questions that arise, with functionality they’re fully comfortable using.

Some will prefer a blank slate, whereas others will appreciate the jumpstart of a template they can modify. To avoid saddling those users with a more challenging experience, offer the option of a prefab approach, with templates and easy-to-follow instructions, perhaps with guided instructions, “wizard” style.

White Labeling

Since we’re talking about embedded analytics, white labeling analytics features are crucial for user experience. You do not want users to think they left your application.

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. Of course, if you’re not actually using a third party, this is unlikely to be a problem, but it’s still important to ensure a consistent, seamless user experience throughout your app.

Product owners invest significant efforts into developing a user interface that’s easy to use, aesthetically pleasing, and visually cohesive. White labeling enables you to maintain that same look and feel, delivering a consistent user experience.

3. Too Much Flexibility

Similar to our recommendation to provide pre-built templates, we seem to be contradicting ourselves. It may sound strange for us to advocate empowering every user with the ability to slice and dice data as they see fit… but then claim it’s a mistake to offer “too much flexibility.” However, the fact remains that it’s vital to establish guardrails within your self-service analytics.

Failure to include controls thwarts your ability to restrict access to data based on user roles. Additionally, too much flexibility could hinder you from charging additional fees and impede users’ ability to save and share the reports they build.

Control Access to Self-Service Features

SaaS vendors often charge a higher fee for self-service capabilities, establishing additional revenue streams. Even if you choose to provide self-service analytics without additional fees, your customers may need to control access based on user roles.

Users who can create new visualizations, dashboards, and reports are often “power users.” Your customer organizations may benefit from the ability to hide self-service from certain users. Exposing that functionality to all users could result in confusion and an unnecessarily steeper learning curve.

Check out our guide to monetization strategies.

Enable Users to Save and Share Custom Reports

Enabling users to build custom reports is great… but they shouldn’t have to constantly rebuild them, nor should every user in the organization have to build their own personal version. Once a user builds a custom report, your app must empower them to save it for future reference and easily share it, so their colleagues can benefit from the insights.

Self-service analytics can boost customer retention, dramatically reducing churn. Users who have developed a series of reports to obtain the information they need are less likely to leave for another vendor where they’d need to start over.

Enable Granular Control of Data Access

It’s vital that each user can access only the information they are authorized to see. Self-service analytics must maintain record- and column-level security to restrict data access at granular levels in a dataset. Role-based access control is also vital to assure proper security.

Security tools and features must support multi-tenant SaaS applications and ideally will inherit your security model, including all of your rules and policies. SaaS providers shouldn’t be forced to rely on a third party to manage their data.

BONUS: Failure to Integrate AI-Powered Insights

To be fair, few companies are failing altogether to incorporate AI in their apps. AI is the hot tech buzzword today like the cloud was in the ‘00s. But many are failing to execute well. Again, these missteps are reminiscent of the common errors in the early cloud days, like poorly architected “lift and shift” migrations. Consider employing AI in the following ways.

Automate Data Processing

AI algorithms can streamline the entire analytics workflow handling tasks such as data cleaning and transformation and model selection, allowing data analysts to focus on data-driven decision making.

Predictive Analytics and Personalization

By analyzing historical data and identifying patterns, AI and machine learning models can make informed predictions.

Ease and Accessibility of Analytics

AI can help equip all users with powerful insights, including those who lack a deep understanding of data structures and queries. NLP can interpret questions posed in ordinary language.

Monitoring and Alerts

AI-powered anomaly detection algorithms can automatically identify unusual patterns or outliers in data, such as fraudulent transactions, supply chain problems, network intrusions, or equipment failures.

With Qrvey

Qrvey transforms self-service analytics within SaaS applications by providing a comprehensive, turnkey solution that accelerates feature deployment. With Qrvey, SaaS development teams can overcome common pitfalls and deliver robust, user-friendly analytics features that empower users to make informed decisions. See why Qrvey is the best embedded analytics tool.

By streamlining data integration, ensuring data accuracy, and offering intuitive interfaces, Qrvey enables faster and more efficient feature releases. This not only enhances the user experience but also gives SaaS providers a competitive edge in the market.

With Qrvey, self-service analytics become an integral, seamless part of your application, driving success for both your product and your users.

Let’s chat and we’ll show you how.