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Final Project: Data Compilation and Analysis of Cedar County Liquor Sales for December
2015
Students Name
Introduction
The primary business question addressed in this project was: "What were the total sales
for December 2015?" Understanding the total sales of December 2015 is core to senior
management at Cedar County. During this period, liquor sale peaks due to holidays and other
seasonal promotions, thereby allowing insight into such sales for that period, hence facilitating
an understanding of the performance in terms of revenues, seasonality, and further opportunity
for improvement.
The following project aimed at combining and analyzing the sales from various Cedar
County outlets centralize the information and analyze the sales recordings for practical insight
into December's 2015 sales to develop an elaborative enterprise-level management report
providing insight into understandable, concise, and actionable information.
Project Objectives:
Consolidate Data: To combine the sales data located in six Excel spreadsheets to
represent different zip codes in the County of Cedar.
Clean the Data: Ensure data integrity through cleaning, standardization, and validation.
Analyze Sales: Focus on sales revenue for December 2015 to determine total monthly
sales.
Report Generation: A professional and visually attractive report is to be generated,
which will present findings to management.
Actions Taken:
, 2
The project involved extensive data compilation, cleaning, and analysis, ensuring the
accuracy and reliability of results. The data is interpreted into graphs and charts to make insight
from the analysis quite comprehensible to senior management.
Key Findings and Methodology
Data Compilation:
Sales data was extracted from the following six Excel spreadsheets, representing different
Cedar County zip codes:
Iowa_Liquor_Sales_52772
Iowa_Liquor_Sales_52747
Iowa_Liquor_Sales_52358
Iowa_Liquor_Sales_52337
Iowa_Liquor_Sales_52306
Iowa_Liquor_Sales_52216
Each spreadsheet represented the transaction information, including date of sale, store
number, product information, sales revenue, and zip code. All these datasets were imported in
Microsoft Access and stored each of them separately in respective tables. Then, Store Number
and Item Number are some of the unique identifiers that will help us link different tables to have
data completely integrated.
Data Cleaning Process
The following cleaning steps were taken to ensure the accuracy and reliability of the data:
1. Standardization:
Final Project: Data Compilation and Analysis of Cedar County Liquor Sales for December
2015
Students Name
Introduction
The primary business question addressed in this project was: "What were the total sales
for December 2015?" Understanding the total sales of December 2015 is core to senior
management at Cedar County. During this period, liquor sale peaks due to holidays and other
seasonal promotions, thereby allowing insight into such sales for that period, hence facilitating
an understanding of the performance in terms of revenues, seasonality, and further opportunity
for improvement.
The following project aimed at combining and analyzing the sales from various Cedar
County outlets centralize the information and analyze the sales recordings for practical insight
into December's 2015 sales to develop an elaborative enterprise-level management report
providing insight into understandable, concise, and actionable information.
Project Objectives:
Consolidate Data: To combine the sales data located in six Excel spreadsheets to
represent different zip codes in the County of Cedar.
Clean the Data: Ensure data integrity through cleaning, standardization, and validation.
Analyze Sales: Focus on sales revenue for December 2015 to determine total monthly
sales.
Report Generation: A professional and visually attractive report is to be generated,
which will present findings to management.
Actions Taken:
, 2
The project involved extensive data compilation, cleaning, and analysis, ensuring the
accuracy and reliability of results. The data is interpreted into graphs and charts to make insight
from the analysis quite comprehensible to senior management.
Key Findings and Methodology
Data Compilation:
Sales data was extracted from the following six Excel spreadsheets, representing different
Cedar County zip codes:
Iowa_Liquor_Sales_52772
Iowa_Liquor_Sales_52747
Iowa_Liquor_Sales_52358
Iowa_Liquor_Sales_52337
Iowa_Liquor_Sales_52306
Iowa_Liquor_Sales_52216
Each spreadsheet represented the transaction information, including date of sale, store
number, product information, sales revenue, and zip code. All these datasets were imported in
Microsoft Access and stored each of them separately in respective tables. Then, Store Number
and Item Number are some of the unique identifiers that will help us link different tables to have
data completely integrated.
Data Cleaning Process
The following cleaning steps were taken to ensure the accuracy and reliability of the data:
1. Standardization:
