Improving Employee Productivity and Performance with Digital HR

A few years ago, I undertook a pivotal project for the Hilton Hotel in North England. The primary objective was to delve deep into the aspirations and motivations driving the employees. What fuels their commitment to their roles? How can we extract their utmost potential without overstepping their limits? And most importantly, how can we ensure they remain loyal and satisfied with the organization? To unravel these complexities, the approach was to dissect the larger workforce into more digestible subcategories, scrutinizing each with a distinct set of criteria. The insights derived from that endeavor significantly influenced strategic HR decisions, benefitting both the employees and the organization.

In this presentation, I aim to revisit that analytical journey, albeit with a twist. For confidentiality reasons, I'll be leveraging a random dataset – one that focuses on IBM employees. While the data might not be from Hilton, the underlying themes, challenges, and objectives resonate closely with my previous study. The dataset, in its essence, mirrors many facets of the original one, making it a fitting substitute for this exposition. Through this analysis, I will illustrate the strategies employed and insights gleaned during my work with Hilton, using the IBM dataset as a representative canvas.

In the subsequent sections, you'll witness a comprehensive analysis of this dataset, echoing the methodologies and thought processes applied in the Hilton project. From understanding attrition patterns to dissecting income distributions and performance metrics, we will embark on a data-driven exploration, aiming to recreate the insights from the original case study.

GOAL

Enhance the productivity and retention of employees by understanding the intrinsic and extrinsic factors influencing their performance, satisfaction, and overall work experience.

PROJECT OBJECTIVES

Data Integrity: Ensure a clean and reliable dataset by identifying and rectifying inconsistencies and outliers.
Holistic Understanding: Utilize statistical techniques and visualization tools to derive relationships, patterns, and trends within the dataset.
Segmented Analysis: Break down the data by relevant attributes like department, role, and experience to gain nuanced insights into the unique challenges and opportunities within each subgroup.
Strategic Recommendations: Propose actionable strategies based on the insights to foster a more productive and harmonious work environment.

RESULTS & INSIGHTS

Attrition Patterns: Approximately 16.12% of employees have left IBM. Certain roles, such as "Laboratory Technicians" and "Sales Executives", displayed a higher propensity to leave, suggesting specific challenges in these departments.
Compensation Discrepancies: The average monthly compensation across the organization is $6,502.93. However, roles in the "Sales" department generally receive a higher median salary than those in "Human Resources".
Employee Satisfaction: The average job satisfaction rating stands at 2.73 out of 4. While generally positive, roles like "Sales Representatives" and "Research Scientists" showed significant variance in satisfaction levels.
Performance Evaluation: A majority (84.63%) of employees have received a performance rating of 3, with 15.37% achieving a rating of 4. This skewed distribution warrants a closer look at the performance evaluation process to ensure it effectively discriminates varying performance levels.
Employee Tenure: The average tenure at IBM is 7 years. Senior roles, like "Managers" and "Research Directors", have longer tenures, suggesting potential career growth and satisfaction in these positions. On the contrary, "Sales Representatives" showed shorter durations, pointing towards higher attrition or the role being a stepping stone to other positions.
Overtime Trends: A significant portion, 28.30%, of the workforce engages in overtime, indicating potential workload challenges or imbalances.

To further enhance the accessibility and interpretability of these insights, all the above data points and visualizations have been aggregated into a comprehensive Power BI dashboard. This interactive tool aims to facilitate a more intuitive understanding for stakeholders and executives, thereby empowering them with data-driven insights for more informed decision-making. The dashboard's interactive nature allows for dynamic data exploration, enabling stakeholders to delve deeper into specific areas of interest and derive custom insights tailored to their unique queries.

Data Cleaning

Before delving into the analysis, we ensured the dataset was pristine by:

Dropping irrelevant columns that didn't contribute significant insights.
Identifying and handling outliers in the 'MonthlyIncome' and 'TotalWorkingYears' columns using the IQR method.
The cleaned dataset now contains 1326 entries (down from 1470) and 31 columns.

Key Findings & Insights

1. Attrition Insights:

Approximately 16.12% of employees have left the company.
Roles like "Laboratory Technicians" and "Sales Executives" exhibited higher attrition rates, suggesting potential challenges in these departments.

2. Compensation Analysis:

The average monthly income stands at $6,502.93.
The "Sales" department boasts a higher median salary relative to "Human Resources", indicating potential discrepancies in compensation strategies.

3. Employee Satisfaction:

The average job satisfaction score is 2.73 (on a scale of 1 to 4).
"Sales Representatives" and "Research Scientists" displayed variance in satisfaction levels, hinting at role-specific challenges.

4. Performance Metrics:

A whopping 84.63% of employees have secured a performance rating of 3, with 15.37% achieving a rating of 4. This distribution warrants a review of the performance evaluation criteria to ensure it effectively discriminates between varying levels of employee performance.

5. Employee Tenure:

The average tenure at IBM is approximately 7 years.
Senior roles like "Managers" and "Research Directors" tend to have longer tenures, while "Sales Representatives" show shorter durations, indicating either a stepping stone role or a higher attrition rate.

6. Overtime Insights:

About 28.30% of employees are working overtime, a critical metric that could be indicative of workload imbalances and potential burnout.

Monthly Income Distribution: A graphical overview showing the spread of IBM employees' earnings, highlighting the average monthly income.

Attrition Analysis: A comparative view of IBM employees who stayed versus those who left, spotlighting workforce retention trends.

Recommendations

Tailored Retention Strategies: Given the pronounced attrition in roles like "Laboratory Technicians" and "Sales Executives", targeted initiatives can be pivotal.
Compensation Review: A comprehensive analysis of the "Human Resources" department's compensation can ensure competitive salaries and enhance talent attraction.
Enhanced Training Programs: Initiatives targeting roles with varied job satisfaction, especially "Sales Representatives" and "Research Scientists", can be beneficial.
Clear Career Pathways: For roles like "Sales Representatives" with shorter tenures, well-defined career progression can aid in retention.
Performance Evaluation Review: A revamp of performance metrics can ensure they are more discriminative and reflective of true employee performance.

Conclusion

In today's business milieu, harnessing insights from data is not just a luxury but a necessity. Through this comprehensive analysis, we have unearthed pivotal trends and patterns that, if acted upon, can significantly boost IBM's employee productivity and satisfaction. Implementing the recommendations can pave the way for a more harmonious and efficient workplace, driving both individual and organizational success.

Future Work

A deeper dive into the reasons for attrition using exit interview data can provide more targeted solutions.

Employee engagement surveys can further elucidate the nuances of job satisfaction across roles.

Analyzing the impact of training programs on performance and satisfaction can guide L&D investments.