Missing Data Navigator for Spotfire® 1.0.0

Overview

The presence of missing values is typical across the industries. At the same time, missing data analysis is a wide topic that could have many steps. The Missing Data Navigator for Spotfire® is an interactive application that will help with this task. 

The actual release covers these features:

• It investigates where and how much data is missing.
• Generates bite-size reports.
• Suggests an optimal strategy to clean data by selected row/column deletion.
• Cleans the data by applying the selected strategy.
• Compares descriptive statistics of the original and cleaned dataset.

The application is in the form of a template, so you can replace the example dataset with your data and start investigations. 

Details

The application leverages visualization mods and a set of Python data functions delivering the analytical part. Some of the implemented algorithms and approaches were originally developed for this application by our data science team. 

If you are interested in more details about this application, visit this community article or look at the documentation packaged with the release.

Here is a quick demo of the application:

Data function library

If you want to create your application analogous to this one, you can reuse some of the content. Several features were extracted and included in the form of Python functions (in our spotfire-dsml library starting with version 1.1.3) or in the form of Spotfire data functions (these are part of the examples for spotfire-dsml library here). The missing data module of spotfire-dsml library also offers additional features not included in this release of the Missing Data Navigator (for example a wide range of missing data imputation features).