In comparison to ad hoc reports, analysis tools provide a more flexible and direct interaction with data and access to the data model. The goal is to generate insights by detecting patterns, deviations or clusters.
In that sense, analysis here describes the flexible navigation within data to answer urgent business questions. Popular types of analysis fall into three categories: dimensional (OLAP), visual and set-oriented analysis. The most popular ones among them are dimensional (OLAP) and visual analysis.
Dimensional analysis allows business users to quickly retrieve results from highly structured sources (cubes) without requiring deep technical expertise. Stable data models, which are common in finance or planning, are an ideal fit for that technology.
Visual analysis leverages the capacity of the human brain to spot patterns and trends quickly. Typically, visual analysis is a good choice for analyzing data with changing structure and multiple attributes such as customer data, not least because the data preparation features are often bundled in the relevant tools.
The increasing ease of use driven by augmented analytics used to guide analyses makes analyzing data simpler and yet more powerful (e.g., by making more complicated statistical methods available to business users or pointing them to abnormalities they were not even looking for). Therefore, more and more users rely on leading-edge tools to support management decisions with new insights.