What is self-service analytics?

Self-service analytics is a form of business intelligence (BI) that empowers business users—regardless of technical skill—to access, analyze, and visualize data on their own, without having to rely on IT or specialized data teams. Using intuitive, user-friendly tools, employees can explore data, generate reports, track KPIs, and uncover insights quickly. This independence fosters a stronger data-driven culture, speeds up decision-making, and reduces bottlenecks across the organization.

Importance of self-service analytics

Today’s self-service analytics platforms are built with ease-of-use in mind. They often include drag-and-drop functionality, pre-built dashboards, customizable reports, and even natural language processing (NLP), allowing users to ask questions in plain language and get immediate answers. Many also provide real-time data access, connect to multiple data sources, and leverage AI and machine learning to deliver recommendations, predictive analytics, and anomaly detection.

Think of your teams as internal customers. Just as external customers prefer to solve problems themselves before contacting support, internal users want quick, direct access to the data they need. By providing self-service analytics, you’re delivering that “customer service” experience—no ticket requests, no waiting, just answers when they’re needed.

Consider this: Forrester’s customer service research suggests that 53% of online shoppers will abandon their purchase if they can’t find quick answers to their questions. At work, people may act in similar ways: If your customer success team, for instance, can’t quickly find and organize the data they want, they may give up. Self-service analytics platforms are designed to close this loop: Non-technical employees can access and understand relevant data quickly, and their creative ideas are more likely to come to fruition and benefit the business.

Benefits of self-service analytics

Organizations can benefit from a self-service analytics platform in multiple ways, including:

• Improved efficiency. No more waiting for analyst reports or pestering the BI team for manually managed data. People who need data can access the analytics 24/7 without help.
• More accurate data. A self-serve data analytics platform serves as a single source of truth. All teams are accessing the same updated data. The fewer times data changes hands, the fewer chances for error due to manual uploads, incomplete downloads, or other processing.
• Less reliance on IT. Your IT team doesn’t have to intake requests or micromanage permissions when non-technical users have self-serve analytics.
• Higher data literacy. Self-service data analytics platforms make data democratic. More people have access to insights. With the help of the platform, employees can understand and use data without needing back-end, technical skills. This breaks down data silos, helping more teams understand trends.

Why self-service analytics matters

Organizations that embrace self-service analytics see benefits across the board:

• Faster insights: Real-time access to data enables timely identification of trends and opportunities.
  
• Improved decision-making: Data-driven insights empower teams to act with confidence.
  
• Higher efficiency: Eliminates wait times for reports and frees up IT to focus on strategic initiatives.
  
• Greater agility: Teams can respond quickly to changes in the market or business conditions.
  
• Increased data literacy: Broader access to analytics builds understanding and confidence in working with data.
• ‍More accurate data: A single source of truth reduces the risk of errors caused by multiple versions of reports.

Examples of self-service analytics in action

• Marketing teams: Monitor campaign performance, track audience engagement, and optimize marketing spend based on real-time insights.
• Sales teams: Identify trends in lead conversions, track individual and team performance, and spot new opportunities faster.
• Customer service teams: Track satisfaction scores, identify recurring issues, and improve service workflows through data-driven decisions.

By embedding self-service analytics into everyday workflows, every department can act quickly on insights, improving efficiency and outcomes.

Best practices for implementing self-service analytics

Implementing any new software requires thoughtful planning. As you implement your new self-service analytics platform, make sure you’re following best practices to give your team members and organization the best chance of success.

Start by streamlining your business.

‍Define the goals you want to achieve with this new platform. Know who will have access, and understand what your teams will use the data for. Make sure your IT has enough bandwidth to prioritize the implementation of the new BI platform, too.

As you plan the implementation, be proactive, not reactive.

‍Rather than waiting for users to struggle, be proactive in addressing their needs. For example, you could create video tutorials on how to navigate the software, or build a FAQ resource on how to create common dashboards. As mentioned before, People generally prefer to find answers to their own questions before asking others for help. You may want to host a hands-on workshop to try out the new software. You can also have a live demo of the product before anyone gets access.

Make templates to help onboard employees to the tool.

‍You can create templates in your new self-service data analytics platform to help people get started and get familiar with the software’s capabilities. Templates make it fast and easy for your employees to generate recurring reports. This also helps ensure consistent quality of data each time a template is used. Using templates can also improve data literacy. People don’t necessarily need to know how certain metrics were calculated, but they can still find the numbers they want. don’t necessarily need to know the complications of how certain data was calculated, but they can still obtain the metrics they’re looking for.

Focus on giving your employees autonomy during implementation.

‍With self-service, success depends on individual employees having the autonomy to use and customize the tool for their own role’s objectives. The beauty of self-service analytics is its decentralized reporting, but that’s only valuable if individuals know how to maximize the software’s capabilities.

Choosing the right self-service analytics platform

There’s a lot to consider when choosing a self-service analytics platform. The answer for your business will depend on how many people will have access to it, what your goals are, how you plan to use the data, and more. Regardless of the size of your teams, here are some rules of thumb to follow when it comes to selecting a self-service analytics platform:

• Customization. Users should be able to quickly accomplish their goals and customize the platform to their business objectives. Scalability should also be a factor, and the platform you choose should be able to flex with seasonal or economic business trends.
• Ease of use. Look for a clean user experience (UX). When considering self-service data analytics platforms, prioritize a platform that has easy navigation. Permissions should also be simple to set by team, by function, or by project.
• Integration abilities. The platform you select should work well with the systems you already have in place. Make sure your platform can integrate with any and all data sources, including data warehouses, cloud business systems, on-premises systems, and proprietary systems.
• Cost. About 93% of companies indicated that they plan to increase investments in the area of data and analytics, according to Ernst & Young’s survey of executives’ post-pandemic outlook. Use that investment wisely by choosing a cost-effective self-service data analytics platform. Know what features are a must-have and which ones you can cut from the budget. If you’re confident in a platform, you may be able to negotiate costs in a contract depending on the number of users and amount of features.
• Industry specialization. As you’re sorting through the many self-service analytics platforms available, it’s worth the effort to research ones that are tailored to your industry. For example, some platforms are designed for life sciences companies and have features that are particularly useful for clinical trials. Manufacturing companies may opt for a platform that has analytics good for drilling down into logistics. Industry-specific platforms can help you find a product that has all the features you need and none of the ones you don’t.
• AI-powered insights. Look for tools that automatically surface trends, detect anomalies, and offer predictive recommendations so teams can act faster.
• Real-time data access. Ensure the platform provides up-to-the-minute updates so your decisions are based on the latest available information.
• Natural Language Generation. Choose platforms that can automatically generate plain-language explanations for data visualizations, making insights easy to understand across the organization.

Could a self-service data analytics platform be the next step for you to free up both your IT team and your BI requestors? How would a self-service analytics platform help your role and team? If you’re interested in learning more, watch a demo to see how Domo’s self-service analytics addresses your specific pain point.

Self-service analytics FAQs

What are the objectives of self-service analytics?

‍The objective of self-service analytics is to empower non-technical team members to access data on their own without needing help from IT or BI analysts.

What is the difference between guided analytics and self-service analytics?

‍With guided analytics, users still need to rely on someone from IT or BI. Users have to request a report, and an analyst has to send the data to the requestor. Generally, guided analytics is a solution created by a developer. However, with self-service analytics, employees are free to explore, generate, and export the data they need, on their own, anytime they want.

What is a self-service data platform?

‍A self-service data platform is a business intelligence software that allows non-technical users to create, explore, and share data—without needing to contact anyone in IT. As the name implies, users can self-serve data to meet the needs of their specific goals.

Why is self-service analytics important?

‍Self-service analytics is important because it empowers non-technical team members to understand and share data easily. There’s no extensive technical training required, and employees don’t have to rely on IT to send them reports. This approach is more efficient for both end-users and for IT.