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This page was created on 09-May-2022 10:38 by Stefan

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Version Date Modified Size Author Changes ... Change note
20 08-Dec-2022 14:46 3 KB Stefan to previous
19 17-Oct-2022 11:28 3 KB Stefan to previous | to last
18 17-Oct-2022 11:21 3 KB Stefan to previous | to last
17 22-Aug-2022 16:35 3 KB Sophia Botsch to previous | to last
16 22-Aug-2022 16:34 3 KB Sophia Botsch to previous | to last
15 22-Aug-2022 12:11 3 KB Jonas Umgelter to previous | to last
14 22-Aug-2022 12:05 3 KB Jonas Umgelter to previous | to last
13 22-Aug-2022 11:49 3 KB Jonas Umgelter to previous | to last
12 22-Aug-2022 11:40 2 KB Jonas Umgelter to previous | to last
11 02-Aug-2022 14:52 2 KB Stefan to previous | to last
10 02-Aug-2022 14:50 2 KB Stefan to previous | to last
9 27-Jun-2022 11:55 2 KB Jonas Umgelter to previous | to last
8 27-Jun-2022 11:48 2 KB Jonas Umgelter to previous | to last
7 14-Jun-2022 11:02 1 KB Jonas Umgelter to previous | to last
6 14-Jun-2022 10:21 1 KB Stefan to previous | to last
5 13-Jun-2022 14:53 1 KB Jonas Umgelter to previous | to last
4 13-Jun-2022 14:46 1 KB Jonas Umgelter to previous | to last
3 12-May-2022 12:01 840 bytes Jurgen van den Hoogen to previous | to last
2 12-May-2022 12:00 1 KB Jurgen van den Hoogen to previous | to last
1 09-May-2022 10:38 108 bytes Stefan to last

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At line 3 changed one line
For data validation we are not completely sure what to add on the knowledge hub, since the tool itself already explains its usability. It is similar to the sensoring tool, which can be accessed through a link in the browser. So, in general, only the link to the data validation tool should be added there combined with a summarized description of the tool. 
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.
At line 5 changed 3 lines
- We propose the following description: “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.”
- The tool can be accessed throughout the following link: [https://share.streamlit.io/cslab-hub/data_validation_diplast/main/main.py]
The tool can be accessed throughout the following link: [https://share.streamlit.io/cslab-hub/data_validation_diplast/main/main.py]