Based on the job status, it then goes deeper into the log to search for potential errors or other relevant information to help assess the processes. The report tracking starts by obtaining table and job statuses and then color-codes them by severity levels. This paper introduces a report tracking framework that helps achieve this goal and improve customer satisfaction. By combining various SAS ® capabilities and tying them together with concise visualizations, it is possible to track jobs actively and alert customers of issues before they become a problem. Worse, maintaining your reputation as an information provider becomes a precarious prospect as the likelihood increases that your customers alert you of reporting issues before you are even aware yourself. With increasing data needs, it becomes more and more unwieldy to ensure that all scheduled jobs are running successfully and on time. It reduces up to 90% of the time spent on the manual review of the PROC COMPARE output files.

Number of variables in base and compared data sets are equal The SAS ® macro %threeN efficiently validates all the output files generated from PROC COMPARE in a short time and also expedites validation with accuracy. Number of observations in base and compared data sets are equal 3. Number of variables not in both compared and base data sets The status is flagged as Pass whenever the output file meets the following 3N criteria: 1. Number of observations not in both compared and base data sets 3. Data set name, label, and number of variables and observations 2. The proposed approach programmatically validates the output of PROC COMPARE in all the programs and generates an HTML output file with a Pass/Fail status flag for each output file with the following: 1. This is tedious, time-consuming, and error prone because there are more LST files to be reviewed and manual intervention is required.

PROC COMPARE is one of the SAS ® procedures used to compare two data sets and confirm the accuracy In the current practice, whenever a program rerun happens, the programmer must manually review the output file to ensure an exact match. Of these, double programming is the most highly recommended and commonly used method to demonstrate a perfect match between production and validation output. Various validation methods are available to confirm data accuracy. In the clinical research world, data accuracy plays a significant role in delivering quality results.

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