Table Of Contents

Harmonizing and Enriching Data

Last Modified: April 8, 2021

Data Preparation harmonizes disparate raw data from various sources, file formats, units, and naming conventions to provide a consistent and comparable view of your test results in one source. Harmonized data is the basis for reliable, actionable insights into your testing activities.

The following table highlights tasks in Data Preparation to meet common data preparation goals.

Goal Data Preparation Task
Set up a Data Preprocessor to harmonize your data and prepare it for analysis. Create and configure a Data Preprocessor instance
Define rules to harmonize engineering units, file property names like test_engineer and values like <name>, and calculate statistical values. Create a data preparation procedure in DIAdem or Python.
Apply the data harmonization rules to your data. Upload the data preparation procedure to a Data Preprocessor instance and run Data Preparation manually or using the scheduler.
Read and convert raw data to TDM/TDMS. Enable DataPlugins to read the file format of your raw data. Install new DataPlugins with the DataPlugins application.

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