One reason self-service business intelligence (BI) has been so elusive is that most vendors and BI professionals don’t realize that there are two types of self-service BI they must support: one for information consumers and another for information producers.
An information consumer consumes information created by an information producer. Simple enough. But an information consumer isn’t equivalent to a casual user and an information producer doesn’t equate to a power user. Let me explain.
An information consumer is anyone who consumes information. This can be a casual user or a power user. An information producer is anyone who produces information. This is typically a power user or IT professional, but can occasionally be a casual user.
For example, casual users consume information 80% of the time and produce it 20% of the time (or at least try to produce it until they give up and ask for help from a super user or IT professional.) The reverse is true for power users. They produce information 80% of the time and consume it 20% of the time.(Ideally, power users should do less producing and more consuming or analyzing.) IT professionals are almost entirely information producers.
Figure 1: Mapping Types of Users to Self-Service Hierarchies
Next, it’s important to understand that information consumers and producers have separate sets of functional requirements. These requirements generally flow in a functional hierarchy as depicted in Figure 1. In other words, information consumers and producers want additional functionality over time as they become more experienced with the BI tool and more knowledgeable about their data and business processes. They may also need different functionality if their role and tasks change frequently as part of their jobs.
Consumer Hierarchy. The functional hierarchy for information consumers is:
- View. Users simply view information online or on paper. Users study the data but do not interact with it in anyway, although some may copy and paste numbers into a spreadsheet.
- Navigate. Users navigate an existing data set by drilling down along predefined paths to view detailed data or by selecting predefined filters to narrow or expand views.
- Modify. Users change an existing data set by sorting or ranking data, toggling among charts and tables, adding columns using calculations, and pivoting axes to change data views.
- Explore. Users add new data to an existing data set by accessing additional predefined data sources and manipulating metadata.
- Model. Users create “what-if” scenarios and predictive models by manipulating independent and dependent variables.
Producer Hierarchy. The functional hierarchy for information producers is:
- Personalize. Users select which information objects to display on their screen and customize the look and feel to suit their tastes.
- Assemble. Users create new reports and dashboards from widgets created from existing report parts, such as charts and tables.
- Craft. Users create new reports or dashboards from scratch using a semantic layer of predefined information objects.
- Source. Users query new data sources, including local files and external data sets, using SQL or built-in query functions and integrate the result sets using point and click logic or Excel.
- Develop. Users write scripts or programming code to query and/or manipulate data to support consumer requirements.
Mapping Users to Functional Hierarchies
Casual Users. As you can see in Figure 1, casual users generally consume information by viewing and navigating data – the first two levels in the consumer functional hierarchy. On the producer side, casual users might only personalize their dashboard or portal, if that functionality is available to them. This bounded set of functionality aligns with my dashboard framework, called MAD, which stands for monitor, analyze and drill to detail.1 In a MAD dashboard, casual users devote the majority of their dashboard time to monitoring predefined metrics and navigating to details to understand root causes and impacts.
Power Users. Conversely, power users consume information in more sophisticated ways than casual users. They spend most of their time modifying, exploring, and modeling data. On the producer side, power users typically assemble, craft, and source information and may occasionally write code or scripts. (Actually, super users primarily assemble and craft reports and dashboards, and business analysts, modelers and data scientists source data and write scripts and code to manipulate data.) IT professionals source and develop data-centric applications.
When casual users first use a BI tool to consume information, they may only view data. After a while, as they become more familiar with the tool, they may want to navigate to the details. Later, they may want to modify data in the report or dashboard by adding a calculated column. Conversely, an information producer may first want to assemble data from pre-existing report parts or craft them using a semantic layer. But after awhile they may want to source data independently and mix it with other data.
Next generation BI tools need to offer the full spectrum of functionality for both information consumers and producers. But, more importantly, they need to expose this functionality on demand, as users need them and are capable of using them. Most tools enable administrators to control what functionality users can access using fine-grained access control lists. But this approach alone is cumbersome since it’s hard to know exactly when users are ready for more. But exposing all functionality at once can overwhelm even the most experienced user, undermining their productivity. (Think of Microsoft Office 2007.) So the best BI tools expose functionality discreetly via icons displayed in the menu bar, ribbon, or content frame but only to users who are most likely to want to use those functions at some point.
Summary. Self-service BI is a key feature of next-generation BI tools. Top-down and bottom-up BI tools both offer self-service capabilities. Top-down tools are designed to help casual users perform ad hoc tasks, while bottom up tools help power users publish interactive dashboards. Today, mashboards and visual analysis tools hold the most promise for delivering on the promise of self-service BI.
Besides tools that bridge the gulf between metrics-driven reporting and ad hoc analysis, we also need BI tools that expose functionality on demand to information consumers and producers. Each type of user traverses a different functional hierarchy as they gain more experience with a tool or change roles or tasks. Thus, it’s imperative that tools support the full range of functionality for each type of user but only expose functionality as users need it to maximize adoption and usage. This is another critical, but often overlooked, dimension of self-service BI.
1 See the second edition of my book, “Performance Dashboards: Measuring, Monitoring, and Managing Your Business,” Wiley, 2010. Available at most online bookstores.