Analyzing HR Data for Improved Workforce Management: A Case Study

INTRODUCTION

HR analytics, also known as people analytics, is a data-driven approach to managing human resources. It involves gathering and analyzing data related to employees, such as recruitment, performance, engagement, and retention, to derive insights and make informed decisions. This case study explores the application of HR analytics in a hypothetical organization and showcases its benefits in optimizing workforce management.

CASE STUDY OVERVIEW

Organization Description: Let's consider a medium-sized technology company called "TechSolutions Inc." The company specializes in software development and has a diverse workforce across different departments, including engineering, marketing, sales, and customer support.

Objectives: The main objectives of this case study are as follows: 1. Understand the factors influencing employee attrition and job satisfaction. 2. Identify key predictors of employee performance. 3. Develop strategies to improve employee engagement and retention.

DATA COLLECTION AND ANALYSIS

Data Sources: To conduct HR analytics, the following data sources can be utilized: 1. HRIS (Human Resource Information System): Employee demographic information, employment history, and compensation details. 2. Performance Management System: Employee performance ratings, goals, and achievements. 3. Employee Surveys: Feedback on job satisfaction, work-life balance, and engagement. 4. Exit Interviews: Reasons for employee departures and feedback on their experiences.

Data Analysis Steps: 1. Data Preprocessing: Clean and prepare the collected data, handle missing values, and ensure data quality. 2. Attrition Analysis: Analyze historical data to understand factors contributing to employee attrition, such as department, job level, salary, tenure, performance ratings, and employee demographics. 3. Job Satisfaction Analysis: Explore survey data to identify key drivers of job satisfaction, including work environment, career growth opportunities, compensation, and employee benefits. 4. Performance Prediction: Utilize machine learning techniques, such as regression or classification models, to identify predictors of employee performance based on historical performance data, employee characteristics, and other relevant variables. 5. Employee Engagement Analysis: Analyze survey data and feedback to assess employee engagement levels and identify areas of improvement, such as communication, recognition programs, or training opportunities. 6. Actionable Insights: Derive actionable insights from the analysis results to develop targeted strategies for improving employee retention, job satisfaction, and performance.

RESULTS AND RECOMMENDATIONS

Based on the analysis conducted in the previous steps, let's assume the following findings and corresponding recommendations:

1. Attrition Analysis:

   • Identification: High employee turnover observed in the sales department, particularly among junior-level employees.
   • Recommendations: Implement mentoring programs, career development initiatives, and regular performance evaluations to support junior sales employees and enhance their job satisfaction.

2. Job Satisfaction Analysis:

   • Key Drivers: Compensation, opportunities for growth and advancement, and work-life balance identified as key factors affecting job satisfaction.
   • Recommendations: Conduct a salary benchmarking analysis to ensure competitive compensation. Implement performance-based incentives, career development programs, and flexible work arrangements to improve job satisfaction.

3. Performance Prediction:

   • Predictive Factors: Employee tenure, previous performance ratings, and engagement survey scores identified as key predictors of future performance.
   • Recommendations: Implement targeted onboarding programs to improve employee retention. Provide regular feedback and coaching to enhance performance. Identify high-potential employees for career advancement opportunities.

4. Employee Engagement Analysis:

   • Engagement Levels: Low engagement levels observed in the engineering department, possibly due to limited career growth opportunities and communication gaps.
   • Recommendations: Establish clear career paths, offer training and development opportunities, and foster a culture of open communication and feedback within the engineering department.

By implementing these recommendations, TechSolutions Inc. can enhance employee satisfaction, engagement, and retention, leading to a more productive and motivated workforce.

Context

This table is a summary table of insights of my first data analyst project, a Google Data Analytics Professional Certificate Programme Case Study.

Content

It has nearly 5M rows and a 20 columns.

Case Study: How Does a Bike-Share Navigate Speedy Success?¶

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path. By the end of this lesson, you will have a portfolio-ready case study. Download the packet and reference the details of this case study anytime. Then, when you begin your job hunt, your case study will be a tangible way to demonstrate your knowledge and skills to potential employers.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Characters and teams ● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. ● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. ● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them. ● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

About the company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends

Three questions will guide the future marketing program:

How do annual members and casual riders use Cyclistic bikes differently? Why would casual riders buy Cyclistic annual memberships? How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual rid...

This dataset is a practical SQL case study designed for learners who are looking to enhance their SQL skills in analyzing sales, products, and marketing data. It contains several SQL queries related to a simulated business database for product sales, marketing expenses, and location data. The database consists of three main tables: Fact, Product, and Location.

Objective of the Case Study: The purpose of this case study is to provide learners with a variety of practical SQL exercises that involve real-world business problems. The queries explore topics such as:

• Aggregating data (e.g., sum, count, average)
• Filtering and sorting data
• Grouping and joining multiple tables
• Using SQL functions like AVG(), COUNT(), SUM(), and MIN/MAX()
• Handling advanced SQL features such as row numbering, transactions, and stored procedures

Dataset

This dataset was created by Danell Eduardo Rapozo Ramirez

Contents

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Characters and teams

● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. ● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. ● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them. ● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

Data Source

The data has been made available by Motivate International Inc. under this license. Dataset download link Click Here

Note: I am a junior data analyst looking forward to improve my abilities. I would love to receive any suggestions or recommendations to help sharpen my skills. Any help would be appreciated. Thanks!

Bellabeat is a health and wellness technology company that manufactures health-focused smart products. The management of the company has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information, the management of the company would like high-level recommendations for how these trends can inform Bellabeat marketing strategy.

Based on the Fitbit data obtained from the survey of 33 unique users, the data was cleaned, aggregated and analyzed to understand the user trends. A dashboard and presentation was compiled to tell the story of data.

This is the final presentation for the Google Data Analytics Certification (Case Study 1).

This dataset is collected by Lyft Bikes and Scooters, LLC (“Bikeshare”) operates the City of Chicago’s (“City”) Divvy bicycle sharing service and this dataset is a part of the Google Data Analytics Professional Certificate capstone project on Coursera. Project Name: Case Study 1 Case Study: How Does a Bike-Share Navigate Speedy Success? This is the Data License Agreement of dataset.

Characters and teams ● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.

● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them.

● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

This dataset cover from January 2022 to December 2022.

Collected Data from Coursera data case study, topic 8 steps followed were both from the curse and YouTube video tutors from tutors like. google data analytics professional certificate capstone Case Study in Excel by matt bratting

This dataset was inspired by the Google Data Analytics course. It is for the Case Study of a Cyclistic Bike share company based on Divvy bikes data sources.

Dataset

This dataset was created by MarcTan9

Contents

This is my first case study from the Google Data Analytics Capstone Project.

This dataset contains financial transaction records, including revenue and expenses, over a specified period. It is designed for data analysis and visualization tasks, providing insights into financial performance and trends.

Key features include:

*Transaction Details: Includes transaction ID, date, category (revenue or expense), and amount in USD. *Payment Methods: Tracks different payment channels like credit cards and bank transfers. *Remarks: Additional context for each transaction, such as "Office Supplies" or "Quarterly Sales."

This dataset is ideal for practicing data cleaning, exploratory data analysis, and visualization. It supports applications like trend analysis, category comparison, and payment method distributions, making it a great resource for aspiring data analysts.

This was a exiting case study for the Google Data Analytics Certification 2023. I choose to do the Case Study 2, the goal was as a business analyst for a small health tracker company how can we use the data from Fitbit users to inform a decision for growth when comparing it to one of Bellabeat's products. I included apple watch users since the data did appear limited in the sample size being 33 participants and with the apple watch users the sample size went up to 59 participants.

I have included my notes from data cleaning process and a power point on my findings and recommendation.

Datasets were not my own and belong to Datasets - ‘FitBit Fitness Tracker Data’ by Mobius, 2022, https://www.kaggle.com/datasets/arashnic/fitbit License: CC0: Public Domain, sources: https://zenodo.org/record/53894#.X9oeh3Uzaao - ‘Apple Watch and Fitbit data’ by Alejandro Espinosa, 2022, https://www.kaggle.com/datasets/aleespinosa/apple-watch-and-fitbit-data, License: CC0: Public Domain, sources: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZS2Z2J

Hi there! My name is Sam, and I am new to Data Analytics. I took some self directed online courses which were super helpful and very hands on. I know there is a lot for me to learn and improve upon; but that's why I'm here! I love learning, and I love learning from others and their experienCE.

Click the first link under "About this File" to view.

Dataset

This dataset was created by Johnnie R.

Contents

It contains the following files:

This case study was completed as part of the Google Data Analytics Professional Certificate on the fictional bike sharing company Cyclistic. The aim of the analysis was to look at the differences in use of the service between casual and annual member riders. This would be used to inform future marketing strategies to convert casual riders to annual members.

Context

The link to the data was provided in the Google Data Analytics Certification course on Coursera, in order for students to complete the capstone assignment.

It was originally made available at the following link: https://divvy-tripdata.s3.amazonaws.com/index.html.

Content

The 12 uploaded files contain 12 month bike sharing usage data for the city of Chicago (one month per file). The data refers to a fictional bike sharing company called Cyclistic.

Introduction After completing my Google Data Analytics Professional Certificate on Coursera, I accomplished a Capstone Project, recommended by Google, to improve and highlight the technical skills of data analysis knowledge, such as R programming, SQL, and Tableau. In the Cyclistic Case Study, I performed many real-world tasks of a junior data analyst. To answer the critical business questions, I followed the steps of the data analysis process: ask, prepare, process, analyze, share, and act. **Scenario ** You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Characters and teams Cyclistic: A bike-share program that has grown to a fleet of 5,824 bicycles that are tracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system at any time. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. Stakeholders Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. Cyclistic marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals and how you, as a junior data analyst, can help Cyclistic achieve them. *Cyclistic executive team: *The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.