What is self-service analytics? Advantages, tools, and more

How will the modern data analyst empower their team—and their company—to dominate the decade of data? This is a question today’s industry-leading companies like Walmart, General Motors, Hulu, and Schneider Electric are asking. 

The answer? Self-service analytics.

Table of contents:

What is self-service analytics?

Self-service analytics is a type of business intelligence that allows users to access and analyze data without relying on IT or BI specialists for support. This means less manual reporting work for your analytics department, freeing  up more time to invest in strategic initiatives. 

Self-service gives front-line business users access to data and fuels insights, so every business user can make data-driven decisions rather than relying on opinions or gut feelings. Given its advantages, self-service analytics is quickly becoming a must-have for businesses of all sizes. 

So what are these advantages, and what are some best practices to follow? Read on to find out.

Self-service analytics example of a person drilling down into bar graph data.

Advantages of self-service analytics

There are many advantages to using self-service analytics, including:

Increased efficiency

Users can quickly and easily access data, deriving answers and creating reports without waiting for someone else to do it for them.

This is especially true for factory14, a European consumer goods company focused on increasing the profitability for brands sold on Amazon. They’re brand management and operations teams were drowning in spreadsheets. These data silos were impacting their ability to keep up with product demand.

That’s when they decided to implement a true self-service analytics solution. Here’s how then Vice President of Analytics, Leon Tang, described the increased efficiency of self-service analytics:

“For someone like [the CBO] to just go into ThoughtSpot, type a few words into the search bar and pull the exact information he needs, just like that — is nothing short of amazing. Now people are using Excel a lot less. Instead, they’re finding the exact data they need with ThoughtSpot.”

factory14 has since undergone a successful acquisition, and Leon Tang has moved on to another opportunity as the Director of Data for Jobandtalent. However, the impact that self-service analytics had on their team is undeniable.

Improved accuracy

By allowing users to access data themselves, self-service analytics reduces the chance of errors that can occur when data is manually entered, downloaded to a machine, or processed. While this is true in every industry, the accuracy and reliability of data is extremely relevant in healthcare. 

For instance, consider Gilead Sciences, a research-based biopharmaceutical company. Here’s what Murali Vridhachalam, Head of Enterprise Data and Analytics, had to say about how self-service analytics has helped to improve the impact of their insights:

“I noticed there were a lot of different teams working on siloed data and analytics. Now, we have a holistic approach of initiating data-driven [insights] at Gilead. It's not a cliche….For example, a lot of things were dependent on a single BI team to create reports. We are trying to disrupt that culture by enabling self-service analytics.”

The democratization of data and rise of self-service analytics has the power to improve patient outcomes in healthcare.

Greater customization

With self-service analytics, users have greater control over their data and can create reports and dashboards that meet their specific needs.

Data Cloud leader, Snowflake, has seen first-hand the improved customization that self-service analytics can provide. That’s because Snowflake doesn’t just deliver best-in-class data products; it runs its entire business on data, too.

“As a company, we believe that measuring the right data drives good business outcomes. Being a data platform company, we have to live up to that promise,”

-Sunny Bedi, CIO and CDO at Snowflake

To support the growing demands of their highly successful business, Sunny knew he would either need to bring in additional resources or find a way to work smarter. That’s when he turned to ThoughtSpot for self-service analytics. Customization was one of the driving factors behind that decision. 

See how self-service BI and customization have helped Snowflake decrease their IT backlog by 20%—read the full case study here.

What to look for in a self-service analytics platform?

When choosing a self-service analytics platform, there are a few things to keep in mind, such as:


A self-service analytics platform should be easy to use and allow users to access and analyze data quickly. It should have an intuitive interface that is easy to navigate. Ease-of-use is essential for users to access data without having to go through a lot of steps.

Quote from Jaya Kolhatkar on why she likes ThoughtSpot for self-service analytics.


As your business grows, it is important that your self-service analytics platform can scale with you. Platforms that lack scalability can quickly become obsolete, as they are unable to keep up with the changing needs of businesses as they grow.


A self-service analytics platform should be flexible enough for users to create highly customizable reports, without relying on IT or other departments for help. This level of flexibility is essential to making data-driven decisions quickly.

Daimler gives procurement pros deep self-service analytics for strong supply chain negotiations.


Unauthorized access to data can spell disaster for any business. That's why it's essential for your self-service analytics platform to have robust security measures in place. At a minimum self-service analytics platforms should have SOC 2 Type II, ISO 27001, Privacy Shield Framework, and GDPR certifications in place. By ensuring that your platform meets these standards, you can help to protect your data and prevent unauthorized access.

Self-service analytics tools

1. ThoughtSpot

We may be biased, but we’re confident that ThoughtSpot’s AI-Powered Analytics is the best self-service analytics tool in the market. Not only our entire team but our growing list of world-class customers make strategic decisions using ThoughtSpot.

Whether you are a marketing leader or a customer support agent, ThoughtSpot’s AI-powered search-based interface allows any user to ask questions in natural language and gain instant insights. With just a few clicks, you can easily integrate ThoughtSpot with multiple cloud databases and generate real-time, interactive Liveboards. Moreover, ThoughtSpot is not only friendly to business users, it is just as powerful for data teams. Analysts can leverage AI-assisted data modeling and SpotIQ to explore data and confidently view what’s happening in your business.


Core features: 

  • Intuitive interface that supports the data needs of all users

  • AI-powered, natural language search on your business data

  • Interactive Liveboard and reporting   

  • Mobile app availability 

  • Seamless integration with cloud data sources

  • Data modeling and exploration

  • Security and governance controls

Let’s explore the real-world example of how Northmill leverages ThoughtSpot to make strategic decisions at scale: 

Legacy tools made accessing data and creating reports a complex affair for data teams, as it took them endless hours to build reports. Due to this technical bottleneck, teams were not able to gain real-time insights into customer data and focus on strategic initiatives. 

With ThoughtSpot’s self-service analytics, connecting to Northmill’s diverse data sources was easy, allowing everyone to access customer data. Users across finance, marketing, and customer service could seamlessly build their own Liveboards and track critical KPIs in real time. By analyzing user data, Northmill was able to identify the exact steps where users tend to drop off in the onboarding process, leading to a 30% increase in the conversion rate. 

See for yourself how ThoughtSpot makes finding insights easy for everyone in your organization—sign up for a free trial today. 

2. Tableau

Tableau simplifies complex data with an end-to-end BI tool that allows business users to organize and prep data for visualization. With a drag-and-drop interface and a well-connected system for integration, users can create interactive dashboards, track KPIs, and model their data. Moreover, Tableau also offers AI tools that help users, even those without technical expertise, explore their business data, forecast outcomes, and perform trend analysis. While Tableau is known for its pixel-perfect visualizations, setting up the platform and using advanced functions is practically impossible for business users as they have to learn technical skills. 

Core features: 

  • Connects to multiple cloud data sources

  • Data visualization

  • Trend analysis and predictive analytics

  • Real-time monitoring

  • App-level security

  • Available on desktop and mobile devices

3. Power BI

Power BI is Microsoft’s self-service BI tool that connects with all Microsoft products and offers AI capabilities, helping non-technical users easily prep data and create interactive visualizations. The drag-and-drop interface coupled with a custom visual library helps Power BI users build live dashboards and uncover valuable insights. Additionally, users can keep a pulse on important KPIs through intelligent alerts and real-time monitoring. 

Core features: 

  • Interactive dashboards 

  • Change analysis and data modeling

  • Data connectors

  • Report sharing

  • Microsoft Copilot (AI feature)

  • Mobile and desktop connectivity

4. Qlik Sense

Qlik Sense is another self-service analytics tool that helps users understand and explore insights. With a low-code interface and a well-connected integration system, Qlik’s users can quickly organize and visualize large datasets to make data-driven decisions. Moreover, the platform offers a chat interface with AI features. allowing users to ask complex questions, receive guidance, and create interactive charts and graphs. 

Core features: 

  • Associative data modeling

  • Real-time monitoring

  • Connectivity with cloud data sources

  • AI capabilities

  • Flexible deployment options

  • Predictive analytics

5. Domo

Domo is a cloud-based self-service analytics platform that offers AI-driven, interactive capabilities that let non-technical users analyze and visualize large datasets. With a low-code interface and a pre-built visualization library, business users can build dashboards, track KPIs, drill into data, and even run what-if scenarios. The platform also boasts AI capabilities that help users get fast answers to complex questions, share reports, and monitor insights on the go. 

Core features: 

  • Interactive visualizations

  • Pre-built data connectors

  • Predictive analytics capabilities2 like ‘what-if scenarios’ and forecasting. 

  • Data modeling

  • Available on desktop and mobile devices.

Five self-service analytics best practices to follow

There are a few key best practices to keep in mind when it comes to self-service analytics:

1. Make sure data is easily accessible

The first step to launching self-service analytics is ensuring that all of your data is easily accessible to those who need it. This means having a central repository—like a cloud data warehouse—where data can be stored and accessed, ensuring that data is properly tagged and organized. 

2. Ensure a user-friendly interface

Another important aspect of self-service analytics is ensuring users have an intuitive and engaging interface to explore data. It should be easy to use and navigate, and provide the user with the ability to customize their experience.

3. Encourage collaboration

With so many users creating insights from data, it’s important to encourage collaboration so people aren’t reinventing the wheel. This means empowering business users to share reports and push insights to tools like Google Sheets, Microsoft Teams, Slack, and email so that insights can be circulated more widely.

The platforms and apps that sync with ThoughtSpot to operationalize self-service analytics.

4. Follow best practices for data governance

Self-service analytics relies on quality data, so it's important to follow best practices for data governance. This means ensuring that your data is well-managed and controlled. Without proper data governance, your self-service analytics efforts will be at risk.

5. Create feedback loops

Finally, it is important to iterate on self-service analytics programs continuously. This means intaking feedback and making changes and improvements on an ongoing basis so that the program can continue to meet users' needs. Additionally, it is important to track metrics so that progress can be monitored and issues can be addressed in real-time.

Transform your business with self-service analytics

If you want to take your business to the next level, self-service analytics is a must. Being able to analyze data quickly and create personalized, actionable insights at the point of impact is what will separate you from competitors. 

ThoughtSpot provides powerful search and AI-driven self-service analytics tools that make it easy for you to find and create insights. Ready to get started? Start your free trial of ThoughtSpot today!