Data Explorers represent about 30% of all data users.Ĭasual users don’t want self-service in the true sense of the phrase: the ability to create data sets and reports from scratch without IT assistance. 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. They generally represent about 60% of all data users. Data Consumers are casual users who consume reports and dashboards as is without modification. Eckerson Group’s User Classification SchemeĬasual Users. It divides each camp into two categories: casual users consist of “data consumers” and “data explorers” while power users consist of “data analysts” and “data scientists”. It divides users into two categories based on whether they consume or produce information: casual users and power users. As a shortcut, Eckerson Group offers a classification scheme that has proven to reflect user demographics at most organizations. My underlying message is: “Get out there and meet these people-they are your customers!” Moreover, these data producers also happen to your most endearing advocates or zealous critics-they can either make or break your data analytics program.Ĭlassifying Users. How can you serve people if you don’t know who they are or don’t even know if they exist? With consulting clients, I’ve spent an entire day at a whiteboard mapping out who produces data artifacts in every department of an organization. How can you serve people if you don’t know who they are or don’t even know if they exist? If lucky, we might get a printout of current data architecture, and sometimes we get a data analytics org chart, but we never get a user classification scheme. When we work with consulting clients, we request 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.
Knowing and classifying your users is a critical first step toward self-service success. It also informs how you organize your data analytics team and design your data architecture. This classification scheme becomes the basis for how you configure data sets, analytical tools, and data access permissions. To succeed with self-service analytics, it’s imperative to create an inventory of business users and classify them into groups. 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 thinks it’s the ability to create data sets and dashboards without IT assistance.Ĭlassification. There are as many types of self-service as there are individuals in an organization. The dirty little secret of self-service analytics is that one size doesn’t fit all. Although tools can empower business users, they don’t produce positive results if they aren’t tailored to each individual, their role, behavior, and preferences.
That’s because software vendors do a great job of equating self-service with their products. Most companies think that the essence of self-service analytics is a tool. This article explores a fundamental tenet of self-service analytics: know thy customer.
That is what we learned in part one of this series.
Read - How to Succeed with Self-Service Analytics, Part I: Pitfalls and ParadoxesĪlthough self-service analytics sounds like a win-win proposition, most projects go awry due to unexpected complexities, paradoxes, and misconceptions.