I worked on PayPerform 360, an Excel-based HR analytics dashboard for analyzing employee performance, salary distribution, bonus allocation, and total payroll cost. The project simulates an HR and payroll workflow in which raw employee data is cleaned, transformed, analyzed, and presented in an interactive management dashboard. Its stated purpose is to help HR teams and business managers understand workforce cost, performance trends, department-wise salary distribution, and bonus impact.
My Employee-Performance-And-Payroll-Management-System repository is on @Mousam098's GitHub, with 1 star; started 2026-05; last updated 2026-07.
I worked on PayPerform 360, an Excel-based HR analytics dashboard designed to analyze employee performance, salary distribution, bonus allocation, and total payroll cost.
I describe the dataset as 100 employees, 5 Departments, and 3 Job Levels.
- The dataset fields are Employee_ID (unique employee identifier); Department (Engineering, Sales, Marketing, Finance, or HR); Job_Level (Junior, Mid, or Senior); YOE (Years); Performance_Rating (1-5); Annual_Salary_USD (base salary in USD); Bonus_Percentage (bonus as a percentage of base salary); Bonus_Salary (added, Salary × Bonus %); and Total_Annual_Salary_USD (added, Salary + Bonus).
My listed data-cleaning methods are IFERROR(), AVERAGEIF(), PROPER(), TRIM(), TEXT(), DATEVALUE(), Data → Remove Duplicates (VBA Macro), VALUE().
My dashboard KPI values are 100 employees; $9,940,030 annual payroll; $1,007,030 bonus paid; 3.63 / 5 average performance rating; 6.0 years average experience.
- I list six charts: Average Salary by Department (horizontal bar); Average Salary by Job Level (column); Employee Count by Department (pie); Total Payroll by Department (bar); Avg Performance Rating by Department (bar); Total Bonus Paid by Department (bar).
I report Engineering as the largest department, with 27% of employees; $106,667/year highest average salary; 33% of total payroll.
I report that Rating 5 employees earn $136,917 on average versus $72,803 for Rating 2 employees; my README claims performance rating directly correlates with pay.
I report that Senior employees can earn up to 25% bonus; low-performing Juniors receive 0%.
I report that HR has the lowest average performance rating (3.35), which the README identifies as a potential area for organizational improvement.
I report a $1.01M total bonus payout; 10.2% of the total payroll budget.
- I describe Microsoft Excel as the primary tool; cleaning with PROPER, TRIM, VALUE, IFERROR, AVERAGEIF, DATEVALUE, and TEXT; Pivot Tables for analysis by Department, Job Level, and Performance Rating; native Excel bar, column, and pie charts; a VBA Macro to make duplicate removal reusable; conditional formatting to highlight nulls and outliers; and dashboard KPI cards, chart layout, and visual formatting.
I describe the workbook flow as Instructions → Raw Data / Cleaning Data → Pivot Table → Dashboard. The README recommends downloading the .xlsx file, opening it in Microsoft Excel (2016 or later recommended), starting with Instructions, comparing Raw Data and Cleaning Data, reviewing Pivot Table, and opening Dashboard.
I list the project authors as MOUSOM KOLEY AND ARNIK JANA.
I list the project tags as Excel, Data Cleaning, HR Analytics, Pivot Tables, Dashboard, Payroll Analysis, Data Analytics, Microsoft Excel, KPI, and Data Visualization.
The README includes a Preview heading but no preview content. It does not provide supporting calculations, underlying employee records, or chart data for the reported insights and KPIs.
I demonstrate practical skills in data cleaning, Excel formulas, Pivot Tables, dashboard design, KPI reporting, and business insight generation.
One goal of my project is to identify and fix data quality issues in a raw employee dataset.
One goal of my project is to build calculated fields for bonus and total compensation.
One goal of my project is to use Pivot Tables to extract department and performance insights.
One goal of my project is to design a professional dashboard for management decision-making.