Today’s business and data analysts are often working blind. They lack visibility into the datasets that exist, the contents of those datasets, and the quality and usefulness of each. As a result, they spend too much time finding data, understanding data, and recreating datasets that already exist. In the worst-case scenario, they work with inadequate and poorly understood datasets and produce inadequate and incorrect analysis. 

The value and benefits of data cataloging are often described as the ability for analysts to find the data that they need quickly and efficiently. Without cataloging, it is said, analysts spend 80% of their time finding and preparing data and only 20% obtaining insights. The data catalog reverses this to 20%preparation and 80% analysis.

This paper illustrates the impact of data catalogs for self-service data analysis. It contrasts analysis processes with and without a data catalog, using a scenario-based framework to illustrate the processes, activities, and timelines of analysis with each approach.