With just about every analytics vendor talking about how their platform is self-service BI, buyers often assume “self-service” equals “user friendly.” That isn’t always the case. And in many cases, the tools that vendors boast as self-service are in reality not be even approachable by the average, non-technical employee.
Self-service BI, defined broadly, means business intelligence users have the ability to support their own needs for insight and experimentation with the tool without involvement from IT or a product specialist every time they’d like to bring in a new data source or create a new analysis. But self-service is an elastic term. What is considered self-service for some is certainly not self-service for all.
A true self-service BI solution focuses on both centralized and de-centralized BI. In other words, self-service is a part of the full BI lifecycle of insight discovery, insight testing, and execution.
When searching for a business intelligence platform with self-service capabilities worth their salt, it’s important to determine specifically who should be able to take advantage of any self-service capabilities and in which ways. That’s why categorizing your BI user personas is an important step that must be taken before implementing BI.
Typically, there are three levels of capabilities that fall under the umbrella of self-service that a BI solution worthy of the words should entail. Each level, while self-service by definition, is only that for a particular type of BI user with a particular set of BI skills. These levels begin with the broadest amount of users and end in a realm that’s exclusive to highly technical employees within the company.
Guided analytics include pre-built dashboards and analyses that clearly guide users—regardless of technical expertise—and make it possible to drill down into the numbers for more details. Users of guided analytics get answers they need to support daily business and feel empowered with the insight most relevant to them.
This level of self-service BI provides users with the ability to create new content for their own consumption and to share with others. These abilities include designing new reports and analyses, setting notifications, scheduling reports, and more. It is important to note that all new content created is based on data models already available within the BI environment.
Self-service data discovery allows users to integrate new data sources and mash up those data sources when data within the existing BI environment. The process of data discovery fully embraces experimentation with new data and hypotheses.
Proponents of self-service data discovery are skilled at thinking outside the BI box. They use data discovery tools to test their own hypothesis on the reasons behind company results, such as combining internal revenue data with industry revenue data to see if they’re gaining market. Or using weather and traffic data to see potential correlations with business performance. Or even examining population density in certain areas to select locations of new retail outlets.
The beauty of external data discovery is that it’s just about as endless as the user’s imagination. The beauty of external data discovery is that it's just about as endless as the user's imagination.
If and when the data is determined a valuable performance indicator, it can then be published for use across the entire department, allowing everyone to gain the advantage of insight. This execution of the tested data keeps the BI lifecycle spinning.
Suffice it to say, this level of self-service BI is not one that even non-technical users can easily wrap their arms around. This is an environment for the Business Analysts, Data Scientists, and Citizen Data Scientists.