How to Succeed with Self-Service Analytics, Know Thy Customer by Wayne Eckerson - Audio Blog
Dec 16, 2019
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Learn how to succeed with self-service analytics by knowing your customers. Explore user categories, challenges and solutions for data explorers, and the importance of understanding data architecture and user classification for effective analytics programs.
Launching self-service analytics without understanding users leads to chaos.
Classify users into groups for tailored content and tool permissions.
Deep dives
Classifying Users for Self-Service Analytics Success
Self-service analytics requires a nuanced approach to cater to individual roles and preferences. A key insight is the necessity to classify users into groups based on their data consumption and production behaviors. Casual users, constituting about 60% of data users, prefer tailored content for decision-making. In contrast, power users, a smaller group but influential in data strategies, heavily rely on self-service tools for in-depth analysis and modeling.
Tailoring Tools and Permissions for Different User Groups
To support varied user needs effectively, tools and permissions play a crucial role. Data architecture, permissions, and tool functionalities need to align with user roles to enhance adoption and avoid information overload. Granular permissions ensure access to relevant data layers based on user classifications. The role of a data classification scheme extends to training programs, organizational decisions, and the establishment of a data analytics center of excellence.
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.
Originally published at https://www.eckerson.com/articles/succeeding-with-self-service-analytics-know-thy-customer
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