Is self-service BI really self-service?

July 14, 2016

The BI market loves self-service tools these days. But 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. You can read more about the BI lifecycle in this blog post: BI Governance Best Practices.

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. You can read about the User Personas and how to design a BI strategy and reports, dashboards, and analyses that help them best utilize the power of analytics: Increase BI User Adoption.

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.


1. Guided Analytics

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. These dashboards and analyses work best when designed with Action Loops in mind. What’s an Action Loop? It’s a strategic approach to problem solving that results in faster, better decision-making. You can read about it here: The Better BI Strategy.

Users of guided analytics get answers they need to support daily business and feel empowered with the insight most relevant to them. When categorized with the BI User Persona nomenclature, these users are ordinarily Information Consumers. These employees consume BI in manageable dashboards, reports, and analyses without getting their hands dirty with BI. The technology itself is secondary to the data it delivers.

2. Existing Data Design

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.

This type of content creation can be done in various ways, depending on user preference. In TARGIT Decision Suite, for example, existing data design is done with the Intelligent Wizard, which lets users type in natural language in the command box to produce an analyses. Or by dragging and dropping dimensions and measures into a new analyses view. 

This level of self-service analytics is relevant for a company’s Business Analysts, Information Designers, and some more advanced Business Users. These users are comfortable in the BI environment and often create reports and analyses not only for their own consumption, but for the use of other employees on their team.

3. Data Discovery

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.

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.

self-service BI

Self-service that works for your company

My advice to BI managers then is to find a business intelligence platform that meets your specific needs. Be sure to enter the conversations with a clear idea of who will be using BI, their technical competencies, and their goals with the software and data-driven environment the company strives to cultivate. Dig deeper anytime a tool is described as self-service.

When rolling BI out in your company, be sure to provide the proper training for everyone, not just the technical wizzes who will be getting their hands dirty with BI every day. And when presenting employees with a self-service BI tool, be sure to walk them through the actual clients, features, and functions that will most appeal to their informational needs and skill sets. This is the key to happy BI users and wide BI adoption.