What is hr-workforce-planning-attrition-report?

This project is an interactive dashboard, built in Microsoft Power BI, that helps HR teams and business leaders understand how their workforce is changing over time and where employees are most likely to leave. Rather than digging through separate spreadsheets and HR systems, users can explore one connected report covering headcount trends, hiring and termination events, engagement scores, performance ratings, absence levels, and how actual staffing compares to planned targets. The report is meant to answer specific questions that HR leaders often struggle to answer quickly, such as how headcount has changed over time, which departments, locations, or roles have the highest turnover, what the main reasons for leaving are, how employee engagement and performance relate to who stays or goes, and where actual staffing levels fall short of or exceed the plan. A live, viewable version of the report and a companion article explaining how it was built are both linked from the README. The underlying dataset covers a multi-department organization across several locations from 2023 through 2025, including employee profiles, monthly headcount snapshots, hiring and termination records, salaries, performance reviews, engagement surveys, absence records, recruitment activity, and planned workforce targets. Behind the scenes, this data was cleaned and shaped using Power Query and DAX, Power BI's data preparation and calculation tools, including steps like standardizing ID formats so tables link together correctly, validating that hire dates come before termination dates, and building a proper date hierarchy so the report can be explored by year, quarter, or month. The data itself is organized in what is called a snowflake schema, a common way of structuring related tables in a data model, with an employee profile table acting as the central hub connecting out to termination events, compensation, performance, engagement, and absence records, plus shared reference tables for department, location, and date that tie everything together for cross-cutting analysis.