Self-service business intelligence (BI) and analytics typically involves allowing nontechnical users throughout an organization to directly access data for self-directed discovery, analysis, visualization, and testing of hypotheses. This access is usually provided through a set of tools that are intended to be easy enough to use that users will not require the services of a data scientist or member of the IT department.
Self-service analytics tools typically automate some amount of data integration and preparation and allow users to analyze and visualize data without requiring deep domain expertise. Administrators should think through their existing data architecture and governance policies before implementing self-service tools to ensure end users can find the data they want but cannot access sensitive data that would present a security or compliance risk. It should also be expected that an initial period of training and support will be needed before users are truly serving themselves.
Self-service is often implemented in such analytics-dependent industries as marketing, customer relations management, finance, real estate, and human resources to increase BI use throughout their organizations while relieving bottlenecks due to lack of IT or data science resources.