Assessment 1 Group 20

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Business

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Apr 3, 2024

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Assessment 1 Group 20 Business Intelligence Solution Development and Report Due Date and Time: 7 th of May 2021, 20:00 PM Group Members: Dulnath Hendawitharana Abdulkhamid Abdullaev Idiris Mohamed Hatim Abbas Words: 4,000

Executive Summary This report analyses the dataset of a Healthcare infrastructure Development Company using Microsoft Excel and Power BI and the steps taken to clean and standardise the data. This report provides BI solutions for managerial use to help improve the company’s current position. This report also provides two dashboards to be used by the business managers. These reports and dashboards provide insights and recommendations for the business manager in order to help solve any business problems and/or goals that the business manager has. Table of Contents 1. Introduction 1 2. Cleaning and Standardising the Dataset 4 2.1 Steps Taken to Clean and Standardise Data 5 2.2 Justification Why These Steps Were Taken5 3. Analysis and Reporting 4 3.1 Analyse Dataset Using Excel 5 3.1.1 Gender & Income 5 3.1.2 Age & Income 5 3.1.3 Education & Income 5 3.1.4 Department & Income 5 3.1.5 5 3.2 Analyse Dataset Using Power BI 5 4. Dashboard Development 4 4.1 Analyse Dataset Using Excel 5 4.2 Analyse Dataset Using Excel 5 1. Introduction

2. Cleaning and Standardising the Dataset 2.1 Steps Taken to Clean and Standardise Data (Appendix 1) Cleaning a dataset is the process of identifying unfinished, unreliable, and incorrect data and removing incorrect values from a dataset and then restore, remodel, or remove any imprecise data. The first step we took when cleaning and standardising the dataset was to convert the dataset into a table so we could easily identify all the missing and incorrect data. We did this by selecting all the data values from A1 to AQ23533. Then selected “Insert” and “Tables”. (See Appendix 1) The next step we took was to identify all categories that are not relevant in analysing data. For example, the categories “Over 18 Years Old” and “Employee Count” was not relevant in this dataset. The next step we took was to identify all the missing and incorrect data and remove it from the dataset. We did this by selecting the arrow under each category (for example: “Age”, “Attrition”, “Business Travel”, “Daily Rate” etc.) and removing those employees from the dataset. (See Appendix 1) 2.2 Justification Why These Steps Were Taken When cleaning and standardising the dataset, we used the listwise deletion method (or complete case analysis). For example, if a case had any missing data for any of the variables, we simply excluded it from the case analysis. (Briggs et al.,2003). The advantages of this method are that it can be used with any kind of statistical analysis. The limitations of this method are that it can exclude a large fraction of the original sample size. For this dataset we did not have this problem, as the original sample size was 23,533 and we reduced the sample size to 23,266. We took these steps, as shown above, because it allows as to organise and analyse the dataset effectively without any inconsistencies. If we didn’t take these steps, the results from our analysis would be flawed.

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3. Analysis and Reporting 3.1 Analyse Dataset Using Excel 3.1.1 Gender & Income 3.1.2 Age & Income 3.1.3 Education & Income

3.1.4 Department and Income 3.1.5 4.34% 65.27% 30.38% Total Human Resources Research & Development Sales

3.2 Analyse Dataset Using Power BI 4. Dashboard Development 4.1 Analyse Dataset Using Excel 4.2 Analyse Dataset Using Excel

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Employees with a lower rate, are generally more satisfied, Department wise satisfaction and hourly rate division based on job role.

Appendix 1 – Steps Taken to Clean and Standardise Dataset Dataset Before Steps Were Taken to Clean and Standardise Data Step 1: Convert Dataset into Table Step 2: Identify All Categories That are Not Relevant in Analysing Dataset Age Attrition BusinessTr DailyRate Departme DistanceFr Education Education Employee Employee Applicatio County 33 Current em Travel_Frequently 1296 Research & 6 3 Marketing 1 3794 Mobile Co 37 Voluntary Travel_Rar 807 Human Re 6 4 Human Re 1 1 123457 Barbour Co 37 Voluntary Travel_Rar 807 Human Re 6 4 Marketing 1 4 123459 Shelby Cou 37 Voluntary Travel_Rar 807 Human Re 6 4 Human Re 1 5 123460 Mobile Co 37 Voluntary Travel_Rar 807 Human Re 6 4 Marketing 1 6 123461 Mobile Co 37 Voluntary Travel_Rar 807 Human Re 6 4 Human Re 1 12 123467 Cullman C 37 Voluntary Travel_Rar 807 Human Re 6 4 Marketing 1 14 123469 Escambia C 37 Voluntary Travel_Rar 807 Human Re 6 4 Human Re 1 15 123470 Elmore Co 37 Voluntary Travel_Rar 807 Human Re 6 4 Marketing 1 16 123471 Morgan Co 59 Current em Non-Trave 1420 Human Re 2 4 Human Re 1 20 123475 Talladega 59 Current em Non-Trave 1420 Human Re 2 4 Life Scienc 1 21 123476 DeKalb Co 59 Current em Non-Trave 1420 Human Re 2 4 Human Re 1 25 123480 Etowah Co 59 Current em Non-Trave 1420 Human Re 2 4 Life Scienc 1 26 123481 Butler Cou 59 Current em Non-Trave 1420 Human Re 2 4 Human Re 1 30 123485 Coosa Cou Age Attrition BusinessTravel DailyRate Department DistanceFromHome Education EducationField EmployeeCount EmployeeNumber Application ID County 33 Current emplo Travel_Frequently 1296 Research & Development 6 3 Marketing 1 3794 Mobile Coun 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Human Resources 1 1 123457 Barbour Coun 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Marketing 1 4 123459 Shelby Count 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Human Resources 1 5 123460 Mobile Coun 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Marketing 1 6 123461 Mobile Coun 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Human Resources 1 12 123467 Cullman Cou 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Marketing 1 14 123469 Escambia Cou 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Human Resources 1 15 123470 Elmore Coun 37 Voluntary Resi Travel_Rarely 807 Human Resources 6 4 Marketing 1 16 123471 Morgan Coun 59 Current emplo Non-Travel 1420 Human Resources 2 4 Human Resources 1 20 123475 Talladega Co 59 Current emplo Non-Travel 1420 Human Resources 2 4 Life Sciences 1 21 123476 DeKalb Coun 1420 Human Resources 2 4 Human Resources 1 25 123480 Etowah Coun 1420 Human Resources 2 4 Life Sciences 1 26 123481 Butler Count 1420 Human Resources 2 4 Human Resources 1 30 123485 Coosa County Over18YearOld EmployeeCount Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1 Y 1

Step 3: Identify All Incorrect and Missing Data and Delete from Dataset Appendix 2 – Group Member Contribution Form MIS271 – Business Intelligence & Data Warehousing Trimester 1, 2021 Assignment 1 GROUP MEMBER CONTRIBUTION FORM Group Number: 20 Name (Print) Student ID %Contribution Signature 1. Dulnath Hendawitharana 25% Dulnath Hendawitharana 2. Abdulkhamid Abdullaev 25% Abdulkhamid Abdullaev 3. Idiris Mohamed 25% Idiris Mohamed 4. Hatim Abbas 25% Hatim Abbas Age Attrition BusinessTravel DailyRate Department DistanceFromHome Education EducationField EmployeeCount EmployeeNumber Application ID County 33 Current emplo Travel_Frequently 1296 Research & Development 6 3 Marketing 1 3794 Mobile Coun

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If every member of the group contributes equally, the figure entered in the ‘%Contribution’ column should be 25% for a 4-member group. This page should be signed by each member of the group and attached to the end of the report. Note: Where the group contribution significantly vary, marks may be individually adjusted between group members. In this case, the Unit Chair may seek additional information from the group.

Assessment 1 Group 20

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