How to Succeed with Self-Service Analytics Part 2: The Key to Self-Service Analytics: Know Thy Customer

Data leaders who launch self-service analytics programs without knowing their business users risk unleashing chaos. This chapter provides a simple framework for classifying business users, which can be used to guide self-service strategies.

Executives might think the primary way to implement self-service analytics is to purchase a visualization tool for every knowledge worker in the organization. After all, some software vendors have equated self-service analytics with their products. Granted, software tools can empower business users, but they only produce positive results if they are tailored to each individual’s role and preferences.

The dirty little secret of self-service analytics is that one size doesn’t fit all.

The dirty little secret of self-service analytics is that one size doesn’t fit all. There are as many types of self-service as there are individuals in an organization. An old-school executive who reads printouts of Excel reports might think self-service is viewing an online dashboard; a business manager might think it’s the ability to modify a report or dashboard with a point-and click interface; a data analyst might think it’s the ability to create data sets and dashboards without IT assistance.

Classification. To succeed with self-service analytics, it’s imperative to create an inventory of business users and classify them. This classification scheme becomes the basis for how you configure data sets, analytical tools, and data access permissions. It also informs how you organize your data analytics team and design your data architecture. Knowing and classifying your users is a critical first step toward self-service success.

Where to Start

When we begin a consulting engagement, we ask clients for three things: a current schematic of the data architecture; an organization chart that identifies data analysts and their managers in each division and department; and a user classification scheme. If we’re lucky, we might get a printout of current data architecture and perhaps a corporate organization chart, but we rarely get a user classification scheme.

How can you serve people if you don’t know who they are or even that they exist?

How can you serve people if you don’t know who they are or even that they exist? We often spend an entire day with clients mapping out who produces insights and reports in every department, and we rarely finish. We urge clients to “get out there and meet these people!” The data analysts and their managers are your most loyal advocates or zealous critics—they can make or break your data analytics program.

Classifying Users. As a shortcut, Eckerson Group offers a classification scheme that has proven to reflect user demographics at most organizations. It classifies business users as either casual or power users, based on whether they consume or produce information. It then divides each of those two groups into two sub-categories. Casual users consist of data consumers and data explorers, while power users consist of data analysts and data scientists.

Casual Users. Data consumers are casual users who consume reports and dashboards without modification. They generally represent about 60% of all data users. Data explorers, on the other hand, are casual users who occasionally want to modify reports or dashboards to create a new view of existing data. Data explorers represent about 30% of all data users.

Casual users don’t want self-service in the true sense—the ability to create data sets and reports from scratch without IT assistance.

They do not perform lots of analysis or create reports and dashboards; their job is to make decisions. What casual users want is “silver service”—the ability to consume content that is highly tailored and pre-digested to meet their decision-making needs. That means either the corporate data analyst team or a local data analyst needs to create a custom data set (i.e., data mart), a business model, and a report geared to the casual users in the department.

What casual users want is “silver service”—the ability to consume content that is highly tailored and pre-digested to meet their decision-making needs.

Power Users. Unlike casual users, power users desperately desire true self-service. For decades, they have had to beg, borrow, and steal data from the IT department and manipulate the assembled data crumbs using Excel or Access. Lacking true self-service, they can spend upwards of 80 percent of their time finding, cleaning, and integrating data rather than analyzing it. Self-service reporting, analysis, data integration, and analytics tools are a huge boon to the power user community.

Because of their voracious appetite for data, power users exert an outsized influence on corporate data strategies even though they only represent 10% of data users in an organization. Most are data analysts who create budgets, analyze pricing, design incentive metrics, evaluate campaigns, and generally exist to answer ad hoc questions from business leaders. Their counterparts are data scientists who, combining strong data skills with statistics and computer programming, create powerful descriptive and predictive models from large volumes of historical data. As experts in artificial intelligence, they are currently in high demand even though they only make up about 2 percent of data users today.

The Dilemma of Data Explorers

Of the four personas, data explorers are the hardest to support. Most of the time (80 percent), data explorers consume reports and dashboards just like data consumers, but occasionally (20 percent of the time) they want to act like power users and generate ad hoc queries and create reports from scratch. Unfortunately, most data explorers don’t have power user skills and can’t remember how to use the self-service features of a BI tool.

Fortunately, new AI-infused BI tools are tailor-made for data explorers: the tools let them submit queries using keywords and natural language instead of SQL or more complex query generation tools. They also automatically surface insights from algorithms that run against queried data in the background.

Over time, we expect the percentage of data explorers to grow significantly as more computer- and data-literate individuals enter the workforce and companies become more data-driven. We expect more casual users will dive into data to explore root causes and remediation strategies. As AI becomes the new BI (see our report by this name), data explorers won’t have to work as hard to create ad hoc views of data.

Applying User Classifications

With a user classification scheme in hand, data analytics leaders can more easily select analytical tools, define permissions, create data architecture, establish training and support services, and establish a business engagement strategy.

Tools. Eckerson Group often uses the matrix in figure 2-2 to help data leaders evaluate their data and analytics tools portfolio. A decade ago or more, a company might need to purchase a different product from a different vendor for each type of user. Today, it’s possible (but not necessarily prudent) to purchase a data analytics platform from a single vendor to support most, if not all, user requirements.

Permissions. It’s critical to purchase tools with granular permissions. Because tools today supply a broad range of functionality and provide access to large and diverse sets of data, it’s important to configure a tool environment so it’s tailored to user needs by role and sometimes by an individual. It’s important to “dumb down” the tool for casual users to keep from overwhelming them with features and functions they don’t need, which is a surefire way to undermine adoption.

Permissions help avoid the proliferation of conflicting reports and dashboards. By default, permissions should prevent business users from publishing reports and dashboards until they gain proper authorization. Usually, this happens when they achieve a certification that demonstrates they understand the company’s data, its standards, and self-service policies.

Data Architecture. Permissions also extend to the data architecture, where each class of business user gains access to a different layer. The conceptual data architecture in figure 2-3 shows that data consumers access applications (i.e., reports, dashboards, and custom applications), while data explorers access a domain-specific business model of data (i.e., semantic layer). They use the semantic layer to modify or extend existing applications. Data consumers and explorers use BI tools, plus a reporting portal to find relevant reports and mobile applications that display analytics.

Similarly, data analysts query data from subject-oriented tables in a data hub (i.e., data lake, data warehouse, data fabric). Data analysts use the flattened subject-oriented data to craft custom data sets for analysis. To create data pipelines and analytic models, data scientists extract raw data from a landing area. Both data analysts and data scientists use a triumvirate of self-service analytic tools: a data catalog to find data, a data prep tool to combine data, and a visualization tool to analyze and share data. In reality, all four types of users access more than just one component in the architecture. The 80/20 rule applies if the data is well organized. Data explorers will access analytic applications more than business views; data analysts will access business views more than data hubs, and data scientists will query data hubs more than landing areas. Business users follow the path of least resistance: They query data at the highest level of abstraction and cleanliness possible that contains the data they need.

Other Considerations. A user classification scheme is also critical for creating suitable training and support programs that foster adoption. And for creating a data analytics center of excellence in which power users are the eyes and ears of a corporate data analytics team in the business units. Likewise, their managers generally form the working committee of a data analytics council and run the community of practice that gathers power users from across the organization for regular meetups and other activities.

Conclusion

Data leaders who launch self-service analytics programs without knowing their business users risk unleashing chaos. Data leaders need to canvas the organization and understand who produces what information for whom and where. They then need to classify business users based on their information needs. Finally, they need to use that scheme to drive tooling, architecture, governance, training, support, and organizational decisions. The next chapter explains how to regulate the creation of reports by data analysts empowered with self-service tools.