Self-service analytics is an analytics setup that empowers employees across an organization to access data on their own. They don’t need help from IT, business intelligence (BI) analysts, or any technical experts to access, use, model, or export the data.
With a self-serve analytics platform, users in marketing, customer experience (CX), graphic design, and other areas can create their own dashboards. Does the creative manager need to export a spreadsheet on an A/B test? Is a digital marketer digging around for a heatmap of a landing page? What if the customer success manager wants to visualize some customer relationship management (CRM) intel? Self-service data analytics allows all these stakeholders to find what they need, when they need it.
In stodgier organizations, users have to put in an IT request or pester someone in business insights. The person who receives the request may not have time to get to the request quickly, or they may misunderstand what the requester was looking for.
Self-service data analytics matter because this kind of setup allows non-technical teams to find answers to their own data-related questions.
Think of the teams within an organization as customers. In a way, offering self-service data analytics is a way of providing customer service. According to HubSpot’s self-service statistics, 81% of customers try to take care of matters themselves before reaching out to someone for help. If you can empower your teammates—your internal customers—to generate their own BI reports, you have provided a service to them with no effort on IT’s part.
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.
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.
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
Have additional questions about self-service analytics or how to choose the best platform? We’re here with answers.
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.
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