The traditional approach to the transformation process (Extract, Load, Transform) requires that technical users get involved directly. They execute connection to data sources, the transformation of data, and provide final datasets to business users. The process often requires much time whenever business users ask for more information or due to changes/updates in business logic. Then, technical users have to revert datasets and transformations.
This article completes the series of posts dedicated to Advanced Analytics with Oracle Data Analytics. Previously, we explored such metrics as Reference Line and Forecast Line in Part 1, and Outliers, Clusters, and Explain features in Part 2. The drag-and-drop methodology helps to build your informative and interactive visuals to view the data.
This part of the material will focus on the Express Builder functionality. This feature is especially helpful for advanced users who possess a deeper understanding of data science and statistics. It is necessary because users need more control over advanced functions and parameters. It also offers running Python scripts for data analysis.
This is the second part of the material dedicated to Oracle Analytics. In Part 1, we started our journey and learned to create Reference Line, Forecast Line with Sales data. It let us get some essential remarks about the products with outstanding performance or relations between Sales and Profit.
In this part, we are going to continue using Advanced features. Here, our goal is to create Outliers and Clusters to identify abnormal data points in datasets and identify the grouping of customers.
Advanced Analytics is a mandatory trend for Business Intelligence platforms and Self-service Analytics tools. It offers a set of statistic functions and algorithms, such as trending, forecast, etc. They help business users, analysts, and others specialists to deep dive into data insights for making better business decisions.
The current article is the first part of the series dedicated to the usage of Advanced Analytics with Oracle Analytics and data hosted in Oracle Autonomous Data Warehouse (ADW). It is not going to cover all the details about statistic functions, algorithms, or probability functions. We are going to discover prebuilt Advanced Analytics features developed in Oracle Analytics.
