Data Validation:

Having high quality data has an enormous potential in improving the data flows within the processes of your company, and is vital in analyzing your data and interpret the results. Therefore, the data quality needs to be checked. For instance, the data may contain errors, missing values, wrongfully labeled data or low sampling rates from sensors. The data validation tool guides you through the important steps when assessing the quality of the data, and gives you tips and tricks how and what to adjust for improving the overall quality of your data. In addition, the Data Validation tool functions as a stepping stone towards analyzing your data using the Data Analytics tool.

Type of tool: desktop application

Short description of the tool: The Data Validation tool helps with general knowledge on validating the quality of your data. Each chapter gives tips and tricks for data quality.

Required skills:

- Process/material knowledge: e.g. knowledge of material properties

- Digitalization knowledge: e.g. no programming

Required programs (step-by-step guide and links provided in user guideline blow):
- Python
- Java
- Anaconda
- Tool files from the GitHub (link below)

Disclaimer: (Disclaimer Text)


This tool supports you to:

Example use case:

Tool guideline and access:
- ⚠️ We recommend to open and save the user guideline before proceeding:
- The tool can be accessed throughout the following link:

Contact person of the tool: Jurgen van den Hoogen & Stefan Bloemheuvel form the Jheronimus Academy of Data Science (JADS).

Before applying this tool:
We recommend also taking a look at the following Di-Plast tools below. They can help you to gather necessary information and data, help to better prepare your data and continue working with it afterwards:
--> Improve internal information and material flow -> VSM
--> Get guidance to set up a working data infrastructure -> Data Infrastructure Wiki
--> Find the right sensor to survey your process -> Sensor Tool

After applying this tool:
-->Analyse and Visualize your process data with data analytics -> Data Analytics
-->Get important insights in enhancing your data understanding -> Exploratory Pattern Analytics
-->Match material requirements with material properties -> Matrix