To ensure reliable results, you need to first ensure that your data source is reliable and credible. This is to increase the chances of accuracy of the analysis. You need to also make sure that you enter the data without any mistakes. This is to avoid any discrepancies in your data. You should also make sure that you regularly check the data. This is to ensure that they are acurate before using it for the analysis.

Running into data issues in projects can be tight, but completing a system is essential to ensure reliable operation. My suggestion is to take these steps to make the process more robust: Use data: Check data regularly, both with manual control and automation tools. This enables early detection of errors. A clear system composition: Set clear instructions in the data for everyone to follow in the same way and train your team on them. Periodic review: By auditing the data regularly, you can quickly resolve potential problems. I am also thinking about implementing these methods in treatment modalities and moving forward quite effectively. How are you doing with this? I would love to hear about your experiences!

Look at the data collection process, standardize as many steps that you can. Where the steps are automated check the integration pipelines to look for any discrepancies and fix them.