The pros and cons of self-service BI: What every industry leader should know

What is self-service business intelligence?  

Self-service business intelligence is a powerful solution that gives business users access to advanced data sets, analysis tools, and the ability to create visualizations. Unlike traditional BI approaches, which often require IT or data team support, self-service BI allows non-technical users to explore data directly, speeding up workflows and enabling faster decision-making.

Difference between self-service BI and centralized BI solutions  

While both self-service and centralized BI solutions provide similar results—such as reports and data analysis—the critical difference lies in user control and flexibility. In centralized BI, any change to a report or dataset, like adding a new visual or metric, typically requires a BI or IT team request. This process can introduce delays and miscommunication, as the request must be processed, implemented, and reviewed. 

In contrast, self-service BI empowers users to make those changes independently. Business users, such as sales or marketing managers, can create new reports, update visuals, and modify datasets without relying on the IT team. This autonomy accelerates the decision-making process, reduces the risk of misinterpreted requirements, and ensures that teams can act on their own data insights. 

Top benefits of self-service BI  

Companies are turning to a self-service BI approach to save valuable time. Faster decisions and better data literacy are cornerstone benefits, but there are more advantages for data-driven enterprises.  

Quality self-service BI approaches solve several key challenges:  

• Resolving data access bottlenecks through data democratization: unlike the centralized BI approach, where most of the time only technical staff had access to broad sets of valuable data, the self-service BI approach offers access to data for all relevant users. E.g. sales reps can now get reports and create visualizations without the help of the IT team. 
• Increasing data literacy: If all users have access to quality insights, the number of employees who can decipher and exploit the data sets can increase, leading to a more productive and informed workflow. For this scenario, a self-service BI platform must have a quality user interface and be easy to use.  
• Improved data discovery: unlike centralized BI approach where IT teams have created the output many times without involvement of end users a lot of data has been lying around without proper description or catalog for end-users to select from. The self-service approach brings more freedom but as well more responsibility and through proper governance the users are more likely to understand the content they create and thus more responsible to describe it so it can be found by others and peers. 
• Faster decision-making: in a scenario where executives or other decision-makers can access data at any time, informed decisions can be much quicker. Getting crucial insights on time is a game changer.  
• Better use of resources: without being the intermediary, the IT team can focus on tasks like improving performance, co-develop with business advanced solutions and work on adoption of BI solutions and data culture improvement. 

Challenges for self-service BI solutions  

Implementing a self-service BI approach brings undeniable advantages, but it also comes with its own set of challenges. The self-service BI approach brings more freedom, but also more responsibility. Based on our experience working with businesses across various industries, we’ve observed that many face similar hurdles when adopting self-service BI. Let’s dive into what these challenges look like – and, more importantly, how to turn them into opportunities. 

One potential issue is losing control of critical data as more users access essential company information, increasing the risk of data leaks. However, most BI platforms have convenient ways around this challenge, such as different permission levels, so executives can have broader access than lower-level managers. 

Despite the massive boost in data availability, most users need a learning curve to create queries and understand data. However, the motivation will outweigh the challenge once you get the first quality report to help you with your core task. As ease of use is required for a BI platform to support a self-service BI approach, the learning curve is much easier to overcome.  

Other potential downfalls for self-service approach are user adoption issues and incorrect analytics results. Some executives could refrain from using such platforms, as they had previous success without self-service BI solutions. Similarly, the analysis results can be wrong if data sets are incomplete or produce unmanaged errors. Data quality implementation and support are one of many to resolve this issue.  

Support with the transition to self-service BI 

While these challenges are common, having the right guidance ensures they’re easily navigated. With proper support, the transition to self-service BI becomes smooth and rewarding. 

Another proven approach to self-service BI adoption is using the Microsoft Fabric adoption roadmap mastered by our team responsible for data strategy. 

One more thing that could be incredibly important in self-service is automated data testing. The user knows the data and what the resulting numbers should be, so he can easily define a test. However, he lacks data literacy and knowledge of what could go wrong, so it is good to have this covered by testing. 

Quicker informed decisions  

Integrating the self-service BI approach into modern enterprises can unlock another level of potential, allowing quicker access to crucial data, analytics, and insights. With all decision-makers in the company using such a powerful tool without needing a third party to compile reports, the time for making informed decisions is reduced significantly. Such an approach can boost productivity or increase the company’s competitive edge in heavily saturated markets in any industry.