What is Self-Service Analytics?

Imagine a world where data flows as freely as water from a faucet. Where anyone, regardless of technical expertise, can turn an endless stream of raw information into insights as refreshing as a tall glass of lemonade on a sweltering day. A place where the power of analysis isn’t gatekept by data priesthoods but democratized for all to wield.

Welcome to the era of self-service analytics – the analytical equivalent of Uber meeting the sharing economy. With self-service, the mysteries of data are unlocked for the masses, empowering everyone from business analysts to busy executives to slake their thirst for knowledge. No longer must they await data dump deliveries from overworked IT departments. Like masterful bartenders, they can blend data from disparate sources, garnishing it with visualizations that tickle the senses and delight the intellect.

Self-Service Analytics Meaning

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

Self-service analytics can allow the user to select from a library of chart types, connect to the data they need, establish parameters, and add visualizations to a custom or pre-defined dashboard. End users should be able to easily make edits and customize elements to arrive at exactly what they need.

Self-service shouldn’t be limited to one thing like reporting, or limited to one group or department within an organization.

Why Self-Service Analytics?

Data by nature is a complex phenomenon. There are many ways to look at it and many techniques to analyze it. Every user approaches their analysis differently. The goal of self-service analytics is for users to serve their own specific needs and answer their own questions.

Self-service analytics remove technical barriers, so no technical skills or advanced data science insights are required. They are also the fastest way for your users to help themselves to the information and insights they require to create better, faster, more informed decisions.

Democratize Data & Reduce Churn

Self-service analytics empower non-technical users to access and analyze data independently. Rather than limiting access to a handful of executives or business analysts, self-service analytics enable everyone in an organization to access the most up-to-date information needed to have informed decisions.

Whether SaaS providers charge additional fees or not, embedded self-service analytics also boost customer retention, dramatically reducing churn. Self-service reporting delivers actionable insight to end users, optimized to their particular workflow. Users who have developed a series of reports to obtain the information they want are less likely to leave for another vendor where they’d be forced to start over.

Self-Service Relies on 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. No-code software usually has an easy-to-use interface and drag-and-drop capabilities, allowing customers to visualize the development process and define the overall business logic with ease. 

Benefits of Self-Service Analytics: Why Organizations Need It

Agility and Speed

To succeed in today’s marketplace, companies must be able to make quick, data-driven decisions. That requires the ability to collect, organize, analyze, and automate their data and then distribute insights and analysis in a quick-and-easy fashion to those who need them.

Self-service analytics puts the power into users’ hands to generate the reports that reveal insights needed to put new information into practice. Self-service enables faster response to critical questions, driving improved decision-making in near real-time.

Answer Unexpected Questions

Your app may have a strong feature set, including numerous reports and dashboards, but there are always capabilities you can’t foresee. John Sarver, Innovation and Technology Consultant at Saaslogic, spoke on the SaaS Scaled podcast and observed, “You can sit and dream and build, then as soon as your first customers start clicking, they never quite click the way you expected them to.”

Including self-service analytics in your app empowers users to answer unexpected questions quickly and easily, without submitting a feature request and waiting for the next release, or relying on in-house developers to build it.

Reduce IT Burden

Easy-to-use self-service tools allow people to create visualizations on their own without the need to place a request and wait for someone else to deliver it. Empowering customers with these tools frees up IT resources for more strategic tasks.

Self-service also amplifies productivity, considering thousands of empowered users can accomplish orders of magnitude more than even the largest and most skilled of development teams.

For SaaS Providers: Increase Revenue & Customer Retention

SaaS analytics vendors often charge a higher fee for these added capabilities, establishing additional revenue streams. Self-service functionality also enables product management teams to reduce the number of feature requests from customers. 

Product management teams are often overwhelmed by reporting feature requests, which can be solved with self-service SaaS analytics.

Self-Service Data Analytics Best Practices

1. Easy to Use & Accessible to Everyone

An easy-to-use interface enables all business users, not just analysts and Excel-savvy users, to work with the platform and build their own analytics.

Users should be able to simply point and click to build beautiful and feature-rich charts, reports, metrics and dashboards. A self-service chart builder should be easy to use and should include dashboard builder components to enable custom dashboards and reports.

2. Workflow & Automation

Automations can send alerts when conditions are met, and workflows can be triggered if thresholds are exceeded. With self-service workflow builders, even non-technical users can easily automate common tasks and make things happen the moment conditions change. 

Workflows can be triggered automatically as new data is received or when user-defined metrics and thresholds are met, allowing all-new types of data-driven applications to be created. Add powerful business logic to your workflows and applications with conditional rules and ML models.

3. Shareable Insights & Collaboration Tools

Once users have obtained valuable insights, they should be able to easily share and disseminate them. Look for features such as the ability to create multi-page and multi-tab reports that include full interactivity and data security built right in. With content deployment features, you can roll out templates and dashboards to specific tenants at your own pace.

4. Interactivity such as Drill Down & Drill Through

Users should be able to interact with reports to easily access additional info as needed. Drill down takes users from a high level to a more granular one, allowing users to go deeper into the data, 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. Finally, advanced filtering options enable users to refine the data that is displayed in reports.

5. Combines All the Necessary Components

To provide useful insights, a tool must include data collection with analytics. Ideally, powerful workflow automation is included as well. An effective tool should combine all of this in one stack.

6. 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. Additionally, this method will inherit your existing security policies rather than relying on a third party to manage your data.

By deploying into YOUR cloud, your data never leaves your account, enabling you to keep your data in your environment under your control.

To fit into software development lifecycles (SDLC), you should also be able to deploy to code repositories and multiple development environments.

7. 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 more flexibility, reducing the need for useless transformations and wasted processing.

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 also need multi-tenancy out-of-the-box, without extensive custom development.

8. Any Data Source

Powerful functionality that enables users to build custom reports is great, but it’s worth diddly squat if they can’t analyze all the data they need. More than 70% of all business data is never used for analysis because most traditional analytics tools only work with structured data. 

To gain vital insights, you must be able to integrate all of your data, including semi and unstructured data sources like forms and images.

How to Set-Up and Deploy Self-Service Analytics? 

The following describes the initial onboarding process for new customers of the Qrvey platform running AWS as the infrastructure platform.

Install Qrvey Software

1. Configure AWS environments
2. Install and configure the Qrvey platform on AWS
3. Build your first Qrvey application in Qrvey Composer, a web-based application used by data analysts to create and manage datasets, visualizations, and dashboards to share with external users.

Create a new application

The Qrvey platform offers a wide range of features that can be used in a Qrvey application, including web forms, data connections, analytics, and automation.

• Create a connection to a data source
• Create a data set
• Build a dashboard with charts
• Publish the application
• Share the application with the organization
• Embed the Qrvey application into your host application

Qrvey is deployed to your AWS environment, enabling you to keep the Qrvey system within your desired AWS region and VPC.

Deployed to Cloud Platform Environment

Qrvey is deployed to your cloud environment, enabling you to keep the Qrvey system within your cloud platform. As a cloud-native and 100% serverless analytics platform, Qrvey is both scalable and cost-effective. 

And because Qrvey is deployed, customers can install Qrvey on an unlimited number of environments, both in international regions and in testing/development environments.

Self-Service Analytics Challenges and Risks

While the benefits of self-service analytics are vast, SaaS providers can encounter various obstacles along their implementation journey.

Performance Shortfalls

Connecting to all your data is only half of the equation. You also need the horsepower to be able to process all of that information and give your users the answers they need, when they need it.

If you want to empower your users with self-service analytics, then you need a tool powerful enough to sort, filter, segment, and analyze hundreds of millions of rows in near real-time. Of course, allocating resources to constantly be able to meet peak demand means paying for unused excess the vast majority of the time. Apps need both power and automated scaling.

Time-Consuming Development & Maintenance

Many SaaS providers start by building their own analytics features, thinking it will be faster and cheaper than buying a third-party solution. However, as your product and customer base grow, you may spend more and more time and resources on developing and maintaining the analytics features that your customers demand.

Lack of Reporting on Customer-Created Data

Anyone who’s ever worked in a CRM such as Salesforce.com, or a project management app like Asana or Monday.com knows that adding custom data fields is par for the course. Likewise, you may allow your customers to add custom fields to your SaaS app. Sure, you might already have fields for tracking many data points, but creating custom fields seems to be an irresistible urge. 

It’s great to enable users to add fields, but you must also enable them to include the resulting data in reports. Unfortunately, such data can be a nightmare to analyze. Traditional BI doesn’t provide any way to even know if a user added custom fields, let alone pull the data into reports in a usable manner. Additionally, automatically updating the data model on the fly is a unique, complex data requirement that traditional BI solutions can’t meet.

Limitations on Data Types

More than 70% of all business data is never used for analysis because most traditional analytics tools only work with structured data. To gain vital insights, you must be able to integrate all of your data, including semi and unstructured data sources like forms and images.

Empowering users with self-service analytics, only for them to subsequently hit roadblocks, “Oops, you can’t analyze that data,” is not a recipe for satisfied customers.

Data Leaving Your SaaS Platform

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

Deficient Analytics Functionality 

Broad utility is crucial, and for embedded analytics that includes:

• Variety of visualization types, such as KPIs, tabular data tables, and heatmaps
• Interactivity such as filtering and drilling down
• Reporting on a broad scope of data sets for full visibility 
• Ability to set alerts and notifications, and schedule reports
• Customizations, such as personalized dashboard configurations and inclusion of custom fields
• Data write-back capabilities to build workflows

When building in-house, early-stage startups are restricted by limitations in budget, time, and staff, as well as expertise of their team. Constrained by all these limitations, early-stage startups must be ruthless in determining the scope of their MVP. 

It’s inevitable that analytics functionality developed in-house will be limited relative to embedding a third-party product. 

Inadequate In-House Expertise

Back in the age of monolithic client-server apps, software providers needed to have the internal expertise to create and manage every bit of functionality they wanted to provide. 

But the explosion of approaches to service-oriented architecture has evolved the way you can build SaaS applications. Cloud native apps are a distinct, superior approach. As CNCF states, “Companies are also discovering that the cloud is not just an easier, more cost-effective way to run their applications, it unlocks new opportunities.” 

Integrating sophisticated functionality without needing in-house expertise to build it is a key advantage of cloud native development.

And if you opt to embed analytics, make sure the vendor you’re considering has broad utility now, and is also continually innovating moving forward, which brings us to our next point.

Data Governance for Self-Service Analytics

Record- and column-level security allow administrators to restrict data access at granular levels in a dataset, so each user gets only the information they are authorized to see. You can grant access to data, insights, and applications according to each user’s role.

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

Best Self-Service Analytics Tools

The best self-service analytics tool will be purpose-built software for SaaS companies. Here are the top 5 reasons SaaS companies choose Qrvey as the best embedded analytics software:

1. Complete embedded solution

• Everything is embeddable across the entire suite
• Simple JS embedding of every widget
• Native multi-tenant security controls

2. Deployed to your AWS environment

• Your data stays in your cloud
• It inherits your security policies
• Complies with GDPR, HIPPA, CCPA and more

3. Deploy to unlimited environments

• DevOps can deploy to all dev environments
• It inherits your security policies
• Integrates seamlessly to your SDLC process

4. Support for a SaaS company

• Updates on your schedule, not Qrvey’s
• Content deployment options can target tenants
• Built on serverless tech for cost efficiency

5. Flat rate licensing

• Unlimited users
• Unlimited dashboards
• Unlimited deployments

Things to Look for in a Self-Service Analytics Platform

White Labeling

When you embed and white-label analytics software, you make your charts, reports, and dashboards look like a seamless part of your software instead of a third-party plugin.

In, “How to Select an Embedded Analytics Product,” author Wayne Eckerson writes, “By inserting charts, dashboards, and entire authoring and administrative environments inside other applications, embedded analytics empowers business users with insights and dramatically increases BI adoption. The catch is that most business users don’t know they’re ‘using BI’—it’s just part of the application they already use. The best BI tools are invisible.”

Look for a platform that can be easily branded and customized, allowing you to change colors, logos, and other elements to make them match your unique brand.

End-User Ease of Use

Self-service analytics must not only enable users to create their own dashboards and reports but also enable all business users – including non-technical users. 

To meet this vital requirement, look for a platform that will 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.

Scalability to Handle Concurrency

Self-service tools will empower users with a broader expanse of analytics, making your app useful to more employees within each customer’s organization. As use expands, your app must keep up and scale securely. Look for a platform that can analyze hundreds of millions of rows in seconds, and provides data augmentation, analysis, and predictions.

Granular Data Security

With a multi-tenant architecture, tenants access the same database instance, and strict access controls must prevent visibility into other tenants’ data. Maintaining security at scale is paramount. 

Also look for the ability to restrict data access at a granular level in a dataset with record- and column-level security. Your customers will need to grant access to data according to each user’s role.

Prefab + Blank Canvases 

Many users will want to start entirely from scratch, adding various elements to create charts and combine KPIs into an entirely custom dashboard. Other users will prefer a “prefab” approach, starting with templates that they can modify to suit their unique needs.

Examples of Self-Service Data Analytics in Action

The following are real-world examples of how SaaS providers have achieved success with self-service analytics.

JobNimbus Delivers Customizable Analytics for Thousands of Customers

JobNimbus, a CRM and project management platform tailored for exterior home renovation contractors, was struggling with churn due to inflexible legacy reporting modules within its platform. Large enterprise customers in particular needed more visibility into key business metrics to run their projects effectively.

The self-service, drag-and-drop simplicity of the Qrvey platform allowed the JobNimbus product team to quickly build custom reports and dashboards addressing their customer pain points. Within months of deploying Qrvey, JobNimbus achieved:

• 70% adoption among targeted large enterprise users
• Meaningful increase in product market fit score
• Significant reduction in customer churn due to reporting limitations

Read the full embedded analytics case study.

Impexium Quickly Delivers Self-Service Capabilities

Impexium, a provider of Association Management Solutions, needed to replace their legacy membership analytics platform. Among other requirements, their users wanted self-service capabilities so they could perform their own data analysis. Without self-service, Impexium was forced to build all of the charts, reports, dashboards, and metrics their customers needed on an ad hoc basis, a daunting task that wasn’t scalable.

By embedding Qrvey, Impexium was able to provide their customers with advanced functionality in a fraction of the time it would have taken them to develop solutions in-house. Thanks to Qrvey’s self-service capabilities, Impexium’s customers can build forms and analytics to discover insights that were previously impossible.

Read the full case study.

Self-Service Analytics in the AI Era

AI Greenfield

On the SaaS Scaled podcast, Devvret Rishi, CEO & Co-Founder at Predibase, discussed the democratization of LLM and AI. “According to a survey conducted by Predibase, 85% of organizations are either actively using LLMs or have immediate plans to do so, but only 13% of those organizations have an LLM application running in production.” Many people are ready to invest funds in machine learning, but the tooling in the platforms or whatever else it is that they need to fill that last mile have not caught up yet.

By embedding AI-powered analytics in your app, you could meet this widespread market need.

AI to Recognize + Solve the Problem

Sid Banerjee, Chief XM Strategy Officer, Qualtrics, was a guest on the SaaS Scaled podcast and shared how Qualtrics is using AI to deliver more value. “We found that AI allowed us to quickly scale out a wide range of different models to make sense of language and customer experiences. 

And where we are now is we’re finding that the current generation of AI, which, GenAI, the ChatGPT-type of technologies, is uniquely good not just at recognizing and tagging patterns in language. 

It’s actually very good at synthesizing insights and even recommendations so that an organization doesn’t have to find something in a traditional analytic view or report. They can have the AI look at all the data and say, ‘This is what you should do. This is the problem. This is the solution.’ So, we’re effectively letting the machine do an even better job of not just finding but remedying a problem.”

Natural Language Processing (NLP) Improvements

AWS microservices are constantly being improved, and in many cases, your app automatically inherits those improvements and without any extra work. Qrvey is architected around AWS services, meaning our offering improves automatically as AWS releases improvements. 

For example, when AWS launched a new version of their Natural Language Processing (NLP) service, our app had improved NLP with no effort on our part. Of course, if you embed Qrvey analytics into your app, those types of improvements appear in the functionality you provide to your users.

About Qrvey

Qrvey enables SaaS companies to create richer analytics experiences and bring them to market faster, while lowering development costs. 

Qrvey is the only complete approach to embedded analytics for SaaS, offering both embedded data visualizations and a multi-tenant data lake designed for advanced analytics — deployed to your cloud environment for maximum data security.

Build Less. Deliver More. 

Book your custom demo today.