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.

Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task. In this work we present a well fitted set of tools for a complete exploratory analysis of a clinical dataset and perform a case study analysis on a set of 515 patients. The proposed procedure comprises several steps: 1) robust data normalization, 2) outlier detection with Mahalanobis (MD) and robust Mahalanobis distances (rMD), 3) hierarchical clustering with Ward’s algorithm, 4) Principal Component Analysis with biplot vectors. The analyzed set comprised elderly patients that participated in the PolSenior project. Each patient was characterized by over 40 biochemical and socio-geographical attributes. Introductory analysis showed that the case-study dataset comprises two clusters separated along the axis of sex hormone attributes. Further analysis was carried out separately for male and female patients. The most optimal partitioning in the male set resulted in five subgroups. Two of them were related to diseased patients: 1) diabetes and 2) hypogonadism patients. Analysis of the female set suggested that it was more homogeneous than the male dataset. No evidence of pathological patient subgroups was found. In the study we showed that outlier detection with MD and rMD allows not only to identify outliers, but can also assess the heterogeneity of a dataset. The case study proved that our procedure is well suited for identification and visualization of biologically meaningful patient subgroups.

Dataset

This dataset was created by Joel Luma

Contents

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.

The Poverty Mapping Project: Poverty and Food Security Case Studies data set consists of small area estimates of poverty, inequality, food security and related measures for subnational administrative Units in Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria and Vietnam. These data come from country level cases studies that examine poverty and food security from a spatial analysis perspective. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and Centro Internacional de Agricultura Tropical (CIAT). The data set was originally produced by CIAT, International Maize and Wheat Improvement Center (CIMMYT), International Livestock Research Institute (ILRI), International Food Policy Research Institute (IFPRI), International Rice Research Institute (IRRI), International Water Management Institute (IWMI), and International Institute for Tropical Agriculture (IITA).

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

Database for the article: Data analytics and Artificial Neural Network framework to profile academic success: Case Study of Leaders of Tomorrow Program

List of Top Schools of Communications in Statistics Case Studies Data Analysis and Applications sorted by citations.

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

This study examines how two nonprofit organizations conducting environmental and social justice work in Finland and Slovenia implement ecosocial principles. The research project consists of two case studies to demonstrate how principles from the ecosocial work literature are implemented at the organization. The data for this project includes eight semi-structured interviews and twelve documents. This dataset consists of 12 documents (annual reports, staff charts, webpages, presentations, and reports). These documents were published between 2021 and 2023. The purpose of the documents is to describe the structure, aims, and programs at each organization for better analysis and triangulation with the interview data.

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...

The summary from the detailed analysis of the case study in EPA (1988b) is provided in Table 3 of the manuscript, and was used as the data source for the two datasets used in this study. These include a flat and hierarchical structure of the five balancing criteria, shown in Table 4 and Table 5, respectively. Table 4 provides a comprehensive score for each balancing criterion, similar to the summary tables presented in the FS of Superfund sites (e.g., (EPA 2016b, AECOM 2019)). Table 5 uses the same information in Table 3, but in this case, each piece of information is used to define multiple sub-criteria for each balancing criterion, except the cost one. This leads to a much more elaborate information table with the four remaining balancing criteria, now characterized by 13 sub-criteria. It is important to note that the scoring provided in Table 4 and Table 5, with the exception of the cost (c_5), were derived from the author’s interpretation of the descriptive language of the detailed analysis in for the hypothetical case study in presented in Table A-7 in Appendix A of the guidance document of EPA (1988b). It should be noted that the analysis of the three remedy alternatives presented in this hypothetical case study is governed by site-specific characteristics and may not represent potential performance of these remediation alternatives for other sites . The intent of this exercise is to illustrate the flexibility and adaptability of the MCDA process to address both the main, overarching criteria, as well as sub-criteria that may have specific importance in the decision process for a particular site. Ultimately, the sub-criteria can be adapted to address specific stakeholder perspectives or technical factors that may be linked to properties unique to the contaminant or physical characteristics of the site. This dataset is associated with the following publication: Cinelli, M., M.A. Gonzalez, R. Ford, J. McKernan, S. Corrente, M. Kadziński, and R. Słowiński. Supporting contaminated sites management with Multiple Criteria Decision Analysis: Demonstration of a regulation-consistent approach. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 316: 128347, (2021).

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

This dataset is issued from the public repository TCGA (https://portal.gdc.cancer.gov/) and contain several files, each corresponding to a given omic on the same individuals with breast cancer. Raw data have been obtained from the mixOmics case study described in http://mixomics.org/mixdiablo/case-study-tcga/ [link accessed on August 18, 2021] and were made available by the package authors at http://mixomics.org/wp-content/uploads/2016/08/TCGA.normalised.mixDIABLO.RData_.zip (R data format). Data in the zip file had been normalised for technical biases by the package authors. Data from the train and test sets were exported as TXT/CSV files and completed with miRNA expression on the smae individuals and toy datasets to handle missing value cases and alike. They serve as a basis for the illustration of the web data analysis tool ASTERICS (Project 20008788 funded by Région Occitanie).

How CMD+RVL's Knowledge Graph powers discovery, transparency, and analysis in capital markets.

The Insurance Dataset project is an extensive initiative focused on collecting and analyzing insurance-related data from various sources.

Penelitian ini memakai data berupa user-generated-content dari Twitter. Pengambilan data diambil dengan teknik crawling menggunakan tools Python. Proses analisis akan terdiri dari beberapa tahap, yaitu perception analysis, dan sentiment analysis. Analisis data ini akan menggunakan tools Google Collab dengan bahasa pemrograman Python dan Orange.

In November 2005, participants at the Workshop on Geological Storage of CO2 at Princeton University agreed on the need for a common test problem to assess various models to simulate the fate of CO2 injected into the subsurface. Alberta Geological Survey offered to make available the data for the Wabamun Lake area in Alberta, Canada, which were assembled to develop a comprehensive model for studying CO2 geological storage. The Wabamun Lake area, southwest of Edmonton in central Alberta, was selected as the test area because a variety of favourable conditions identified it as a potential site for future, large-scale CO2 injection. Several large, industrial CO2 point sources are in the area, resulting in short transportation distances of the captured gas. Various deep saline formations with sufficient capacity to accept and store large volumes of CO2 in supercritical phase exist at the appropriate depth and are overlain by thick confining shale units. Most importantly, a wealth of data exist (i.e., stratigraphy, rock properties, mineralogy, fluid composition, formation pressure, information about well completions, etc.), collected by the petroleum industry and submitted to the Alberta Energy and Utilities Board. For these reasons, the Wabamun Lake area is an ideal location to characterize a CO2 storage site and analyze the potential risks.

Case study: How does a bike-share navigate speedy success?

Scenario:

As a data analyst on Cyclistic's marketing team, our focus is on enhancing annual memberships to drive the company's success. We aim to analyze the differing usage patterns between casual riders and annual members to craft a marketing strategy aimed at converting casual riders. Our recommendations, supported by data insights and professional visualizations, await Cyclistic executives' approval to proceed.

About the company

In 2016, Cyclistic launched a bike-share program in Chicago, growing to 5,824 bikes and 692 stations. Initially, their marketing aimed at broad segments with flexible pricing plans attracting both casual riders (single-ride or full-day passes) and annual members. However, recognizing that annual members are more profitable, Cyclistic is shifting focus to convert casual riders into annual members. To achieve this, they plan to analyze historical bike trip data to understand the differences and preferences between the two user groups, aiming to tailor marketing strategies that encourage casual riders to purchase annual memberships.

Project Overview:

This capstone project is a culmination of the skills and knowledge acquired through the Google Professional Data Analytics Certification. It focuses on Track 1, which is centered around Cyclistic, a fictional bike-share company modeled to reflect real-world data analytics scenarios in the transportation and service industry.

Dataset Acknowledgment:

We are grateful to Motivate Inc. for providing the dataset that serves as the foundation of this capstone project. Their contribution has enabled us to apply practical data analytics techniques to a real-world dataset, mirroring the challenges and opportunities present in the bike-sharing sector.

Objective:

The primary goal of this project is to analyze the Cyclistic dataset to uncover actionable insights that could help the company optimize its operations, improve customer satisfaction, and increase its market share. Through comprehensive data exploration, cleaning, analysis, and visualization, we aim to identify patterns and trends that inform strategic business decisions.

Methodology:

Data Collection: Utilizing the dataset provided by Motivate Inc., which includes detailed information on bike usage, customer behavior, and operational metrics. Data Cleaning and Preparation: Ensuring the dataset is accurate, complete, and ready for analysis by addressing any inconsistencies, missing values, or anomalies. Data Analysis: Applying statistical methods and data analytics techniques to extract meaningful insights from the dataset.

Visualization and Reporting:

Creating intuitive and compelling visualizations to present the findings clearly and effectively, facilitating data-driven decision-making. Findings and Recommendations:

Conclusion:

The Cyclistic Capstone Project not only demonstrates the practical application of data analytics skills in a real-world scenario but also provides valuable insights that can drive strategic improvements for Cyclistic. Through this project, showcasing the power of data analytics in transforming data into actionable knowledge, underscoring the importance of data-driven decision-making in today's competitive business landscape.

Acknowledgments:

Special thanks to Motivate Inc. for their support and for providing the dataset that made this project possible. Their contribution is immensely appreciated and has significantly enhanced the learning experience.

STRATEGIES USED

Case Study Roadmap - ASK

●What is the problem you are trying to solve? ●How can your insights drive business decisions?

Key Tasks ● Identify the business task ● Consider key stakeholders

Deliverable ● A clear statement of the business task

Case Study Roadmap - PREPARE

● Where is your data located? ● Are there any problems with the data?

Key tasks ● Download data and store it appropriately. ● Identify how it’s organized.

Deliverable ● A description of all data sources used

Case Study Roadmap - PROCESS

● What tools are you choosing and why? ● What steps have you taken to ensure that your data is clean?

Key tasks ● Choose your tools. ● Document the cleaning process.

Deliverable ● Documentation of any cleaning or manipulation of data

Case Study Roadmap - ANALYZE

● Has your data been properly formaed? ● How will these insights help answer your business questions?

Key tasks ● Perform calculations ● Formatting

Deliverable ● A summary of analysis

Case Study Roadmap - SHARE

● Were you able to answer all questions of stakeholders? ● Can Data visualization help you share findings?

Key tasks ● Present your findings ● Create effective data viz.

Deliverable ● Supporting viz and key findings

**Case Study Roadmap - A...