Human Resource Analytics Using KNIME – An Introduction

HR Analytics

What gets measured gets managed. What gets managed gets executed. -Peter Drucker

Global organizations with workforce analytics and planning outperform all other organizations by 30% more sales per employee. -CedarCrestone Research Survey

The corporate world is slowly evolving its practice of HR analytics toward a ‘model of predictive management’ for human resources because to be successful, HR analytics must extend beyond reporting what is (the present) or what was (the past) to predicting and analyzing what will be (the future). -Center for Advanced Human Resource Studies

The global HR analytics market will grow at a CAGR of around 12% during the 2019-2025 forecast period. -World HR Analytics Market

Companies using a portfolio of HR analytics solutions could realize an increase of 275 basis points in profit margins, on average, by 2025. -McKinsey Global Institute

HR Metrics and HR Analytics

The core responsibility of the HR department is to provide a high return on the business’s investment in its employees by effectively aligning with corporate culture, organizational values, vision, and mission. It provides organizations with insight and feedback on the contribution of HR initiatives to meeting operational goals and strategic business objectives.

HR Metrics are measurements used to determine the value and effectiveness of HR initiatives and help track key HR department activities by reporting numbers. These indicators allow businesses to measure performance, efficiency, and impact of business processes and change. While HR metrics act as a barometer for business performance, they can only track events but do not provide in-depth information about the event, which can be used for making strategic decisions and uncovering areas of improvement. On the other hand, HR analytics aims to provide insight into each process after gathering data and then use them to make relevant decisions about improving these processes. Thus, when used for HR analytics, HR metrics can pave the way toward effectively meeting organizational objectives.

What is HR Analytics?

HR analytics, also known as people analytics, talent analytics, or workforce analytics, can be defined as follows:

• The systematic identification and quantification of the people drivers of business outcomes.

• The process of collecting and analyzing human resources data to improve employee performance.

• Applying sophisticated data mining and business analytics techniques to human resources data.

The field of analytics refers to using analytics in an organization's HR department processes to improve workforce performance and result in a better return on investment.

HR analytics can be used to explore and examine the data and then transform their findings into insights that will help executives, managers, and operational employees of the HR department to make better and more informed decisions. These are three types of analytics:

• Descriptive analytics – What has happened?

• Predictive analytics – What could happen?

• Prescriptive analytics – What should happen?

The data-driven insights that HR analytics provides help identify existing and required outcomes. This will further improve productivity and efficiency, generating increased revenue for the organization.

Artificial Intelligence (AI) and machine learning help streamline the HR department's significant and essential functions. The latest developments in HR analytics use AI tools for text and image data analysis. AI helps measure employee behavior and hire the most suitable candidate for the job during recruitment by filtering resumes and recognizing and analyzing facial expressions and voice in the organization.

HR Analytics for Professional Challenges

To meet organizational objectives, HR professionals must recruit the right person, experience less attrition, utilize employees' best capabilities, improve the recruitment process, provide competitive salaries, launch successful employee training programs, etc. The organization's leaders can make consistently better decisions using facts supported by data. The key to finding the correct data for the organization is identifying the overall business needs, as every organization may differ in data collection, formulating strategies, and decision-making. According to the requirement, the data for doing HR analytics can be collected from the Human Resource Information System (HRIS) and through the customized survey portals.

Why KNIME for HR Analytics?

This series intends to encourage more ‘data-driven’ decisions in HR. HR Analytics is no longer a nice-to-have add-on; it is how HR practitioners should conduct HR decision-making in the future. Where applicable, human judgment is ‘added’ onto a rigorous data analysis done in the first place.

To achieve this ideal world, I need to equip you with some fundamental knowledge of KNIME, an open-source tool for data scientists. I am well aware that, on one side, you want to do something for your career in HR. However, you are most likely completely new to data analytics and coding.

KNIME is a data science platform for data analytics and machine learning. It is an open-source tool businesses can use to automate repetitive tasks, analyze data, and build AI models. The visual workflow representation of each node makes it easy to use and understand, even for people without a data analytics or programming background.

Topics Covered in HR Analytics Series

This series of articles on “HR Analytics with KNIME“ covers the role of HR analytics in the following functional areas.

Analyzing Employee Details – Data Exploration & Manipulation

It illuminates the available data exploration and manipulation nodes in KNIME, familiarizes them with their functions, and educates them on using them in different HR data analysis scenarios.

Reviewing Employee Details – Data Visualization

Educating on the various visualization techniques and their relevant nodes in the KNIME provides knowledge of charts for categorical and continuous variables, demonstrates the use of charts to solve real-world problems, and encourages effective decision-making regarding selecting a chart related to the data type.

HR Analytics Using Statistical Techniques

Structuring Employee Rewards Package – Conjoint Analysis

The article mentions the organization's flexible compensation and benefits plan and discusses how to perform conjoint analysis to determine the preference of different attributes and levels. It helps evaluate conjoint analysis results and determine the utility of varying compensation and benefits plans.

Evaluate Training Effectiveness – Comparative Statistics

It helps you understand the importance of employee training and development programs, provides knowledge of different comparison techniques, assesses the results of comparison techniques used for evaluating training programs, and elaborates on familiarity with applications of comparison techniques in other areas of HR analytics.

Forecasting HR Cost – Time Series Modeling

Educated on the importance of time series analysis, it helps determine the series’ stationarity and assists in constructing different models using ARIMA modeling techniques. It also mentions how to forecast using ARIMA modeling techniques.

HR Analytics Using Unsupervised Machine Learning

Unsupervised machine learning algorithms are used when the output is unknown, and no predefined instructions are available to the learning algorithms. In unsupervised learning, the learning algorithm only has input data, and knowledge is extracted from these data. These algorithms create a new representation of the data that is easier to comprehend than the original data and help improve the accuracy of advanced algorithms by consuming less time and reducing memory. Standard unsupervised machine learning algorithms include association rule mining, dimensionality reduction algorithms, and clustering.

Evaluating Job Satisfaction – Association Rule Mining

It helps determine the factors associated with job satisfaction, educates people to understand the methodology of association rule mining, and shares knowledge on implementing the apriori algorithm for determining factors impacting job satisfaction. It also facilitates the evaluation of the results of association rule mining for job satisfaction.

Identify Absenteeism Patterns – Cluster Analysis

It helps to understand the importance of clustering and mentions different clustering techniques. You will also be able to analyze the results of various clustering techniques and implement them in a real-world situation.

Identify Core Factors in Performance Appraisal – Principal Component Analysis

It helps to understand the factors of the performance appraisal system and helps in applying dimension reduction algorithms. It also educates on implementing algorithms to determine performance appraisal factors and analyze the results of these algorithms.

HR Analytics Using Supervised Classification Machine Learning

Classification is a machine learning technique for solving problems predicting a dependent categorical variable. The classification problem occurs when the dependent variable has two or multiple categories and is predicted by a set of independent variables. Most of the data in real-world problems require the concept of classification. It should be specified that any number of dependent variable categories can exist in classification. However, logistic regression applies only to two categories (binary classification). Logistic regression predicts binary values, such as whether the customer will buy or not, Pass/Fail, Yes/No, 0/1. It measures the probability of event=success and event=failure.

Predicting Employee Attrition - Supervised Classification Machine Learning

It helps to understand the importance of attrition for employees and gain knowledge of different classification techniques. You can use classification techniques to predict employee attrition and familiarize yourself with classification techniques applied in other areas of HR analytics.

Summary

In conclusion, HR analytics is a transformative approach that empowers organizations to make data-driven decisions, enhancing workforce performance and aligning HR initiatives with strategic business objectives. By leveraging tools like KNIME, HR professionals can efficiently analyze and visualize data, leading to improved recruitment processes, optimized employee training programs, and better overall organizational outcomes. Integrating advanced technologies such as AI and machine learning will further streamline HR functions as the field evolves, ensuring that companies remain competitive in a rapidly changing global market. Embracing HR analytics is a trend and a necessity for future-ready organizations aiming to maximize their human capital investment.