DAT-325 Module 7 Project Three

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Southern New Hampshire University *

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325

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Industrial Engineering

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Oct 30, 2023

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docx

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DAT 325 Project Three Data Validation File Names Table Perform the following data validation on each of the import, existing, and merge data tables. ● The COUNT of rows in each table. You should reconcile these counts to make sure the number of rows you inserted made it into the merged table. Excel Files Source File Anomalies Import Data Existing Data Merge Data Count 205 11 105 310 310 ● Use the identified variable for your chosen data set as listed below to determine MIN, MAX and AVERAGE for each table: Data Set: Human Resources Variable: Pay Rate by Department Determine the MIN, MAX, and AVERAGE in your uploaded table, the MIN, MAX, and AVERAGE in the existing table before you inserted the new rows, and the MIN, MAX, and AVERAGE in the existing table after you insert new rows. Excel Import Data Existing Data Merge Data MIN 15 14 14 MAX 80 64 80 AVERAGE 35.33 29.20 31.34 Summary The distribution data allows us to analyze the impact of the import on the existing distribution. By comparing the minimum, maximum, and average values for the "Pay Rate by Department" variable in different tables (Import Data, Existing Data, and Merge Data), we can assess the changes in the data distribution. By comparing the MIN and MAX values, we can observe that the minimum value remains the same throughout the process, indicating that no new records with a lower pay rate were introduced with the import data. The maximum value increases from 64 (Existing Data) to 80 (Merge Data), meaning that new records with higher pay rates were imported. From this analysis, it appears that the import has introduced higher pay rates into the existing data. The average pay rate has increased compared to the existing data before insertion, but not as much as in the Import Data alone. It suggests that the import has influenced the distribution but to a lesser extent.

Validation steps are crucial for ensuring clean and high-quality import data for several reasons. One of them is that the validation process helps identify and correct anomalies (errors, inconsistencies, missing values, or outliers) within the dataset. By addressing these issues, the data quality improves, reducing the likelihood of misleading or incorrect analysis results. Validating the data import also helps ensure that it matches the required data types, ranges, and constraints, maintaining data integrity throughout the process. In summary, the distribution data provides insights into how the import affects the existing data. Validation steps are crucial to identify anomalies, maintain data integrity, ensure consistency, and enhance the overall quality of the import data, leading to reliable and accurate analysis results.

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