Discovering Insights in Multidimensional Data: A Guide to Rudimentary and Advanced Analytics

In this article, the authors propose a benchmark framework called Text2Analysis to evaluate advanced analytics in multi-dimensional data. The framework includes seven basic insights, which are rank, rank last, attribution, trend, monotonicity, and outlier. These insights can be combined to form complex analyses using rudimentary operations and advanced operations.
Rudimentary operations include data management, manipulation, and transformation using Structured Query Language (SQL). Advanced analysis focuses on representative tasks from each type of analytics, including descriptive and diagnostic analytics, predictive analytics, and reporting and visualization. The authors highlight that many user queries in the real world are often unclear and missing parameters for advanced data analysis tasks.
The authors propose using Text2Analysis to demystify complex concepts by using everyday language and engaging metaphors or analogies to capture the essence of the article without oversimplifying. The summary should be concise, targeting an average adult’s comprehension level, and formatted in markdown syntax with section titles as headers.