Statistics Data Preparation

The Statistics Data Preparation Module improves data preparation for more accurate results.

 

Statistics Data Preparation offers advanced techniques to simplify the data preparation phase of the analytical process to provide faster and more accurate data analysis results. The features in this module allow you to choose a fully automated data preparation procedure for faster results or select several other customised methods for more complex datasets.

It is possible to identify both suspicious or invalid cases and entire variables that may affect a correct analysis. In addition, it is possible to visualise missing data patterns, summarise variable distributions and use algorithms designed for nominal attributes.

Statistics Data Preparation allows you to:

  • Automate the data preparation process to eliminate the time-consuming and complex manual data preparation process.

  • Validate data without manual checks to perform faster and more accurate data validation processes.

  • Prevent complex values of incorrect analyses to automatically identify anomalies that can damage results.

 

Automating the data preparation process

 

  • Preparing data in one step.

  • Identifying and correct quality errors and assign missing values.

  • Quickly determining which data to use in analyses.

  • Easy-to-understand reports with tips and visualisations

 

Validating data without manual checks

 

  • Ensuring consistency of data validation on the basis of the project.

  • Applying validation rules according to the individual measurement level of the variables (categorical or continuous).

  • Receiving reports of invalid cases, summaries of rule violations and the number of cases involved.

  • Eliminating or correcting suspicious cases before analysis.

 

Preventing the complex values of incorrect analyses

 

  • Searching for unusual cases based on deviations from similar cases.

  • Marking complex values, creating a new variable.

  • Examining unusual cases to determine whether they should be included in the analyses.

 

Tecnical sheet

Statistics Data Preparation