Self-service analytics is an approach to advanced analytics that allows business users to manipulate data to spot business opportunities, without requiring them to have a background in statistics or technology.
Increasingly, vendors are introducing products that allow business users to work with data that's aggregated from a range of sources. The software provide users with a dashboard that allows the users to query and manipulate large amounts of data. In the past, such data analysis was solely the domain of trained data analysts, who are now often referred to as data scientists.
Proponents of self-service analytics software maintain that a self-service approach fills the gap caused by a shortage of trained analysts and gets data into the hands of the people who need it most -- business users. A self-service approach allows business users to make data-driven decisions in real time without having to rely on information technology (IT) staff or data scientists to create reports. Critics of self-service analytics maintain that only a trained data scientist can reliably understand the meaning of certain data correlations and if the analysis process is mismanaged, it can lead to potentially damaging decisions. Everyone agrees that when an organization is thinking of implemeting self-service analytics, the organization should also have a data governance policy in place.