Data Quality and Dataset Warehouse Assignment

Assignment Task

Justification

The given Warehouse codes were an inconsistency in naming. The warehouse values were compared to the values in the “Warehouse” table in the dataset and replaced as appropriate.

The given Item codes were an inconsistency in naming. The Item Code values were compared to the values in the “Item” table in the dataset and replaced as appropriate.

The ”Onhand Qty” had several negative values. As the definition of the field was set to be “The total number of bicycles physically available in the warehouse on the inventory date.”, a negative value would be impossible. However, the negative values were analyzed and it was concluded that this indicated the quantity required for outflow, but as there were no physical stock available, the value was set to negative. In order to ensure correct analysis in different situations, a new field “OnhandQty_V” has been created.

The Null Values for “Outflow Qty” were replaced with 0, after calculation how the values were derived. The given Formula was used to ensure that the NULL value indicated “0” and not any other missing value.

The missing values in the “Unit Value” column were replaced by the Median of the unit values as per the Item code.

To ensure that no duplicate entries were present, a new field that concatenated inventory Date, Item Code, and Warehouse was created. There was no repetition of the values in the fields, indicating that there are no duplicates in the dataset.

The given Country were an inconsistency in naming. The USA and United States are the USA replaced with the United States same country.

In conclusion, the data quality assessment revealed that the dataset is of high quality, with only minor inconsistencies. The key elements of completeness, accuracy, consistency, and timeliness of the data are satisfactory for analysis. By conducting a data cleaning process, inconsistencies and anomalies were adjusted using appropriate methods such as imputation and grouping. Overall, the process of understanding, assessing, and cleaning the data has set a strong groundwork for meaningful analysis and insights from the dataset.

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