by HR Analytics, also known as people analytics, is a data-driven approach to managing human resources processes and improving employee performance and retention. It involves collecting, analyzing, and interpreting various types of HR data such as recruitment, onboarding, training, performance metrics, employee engagement, and turnover rates. By leveraging statistical analyses and predictive modeling, HR analytics aims to uncover insights and trends that inform strategic decision-making, optimize HR policies and practices, and enhance overall organizational effectiveness. This approach enables businesses to make evidence-based decisions that can lead to improved productivity, employee satisfaction, and organizational growth.
Different phases of HR Analytics:
HR analytics can be broadly divided into several phases, each representing a step towards more sophisticated analysis and deeper insights into HR data. These phases are often conceptualized as a maturity model, ranging from basic descriptive analytics to advanced predictive and prescriptive analytics. Here’s an overview of the different phases:
-
Operational Reporting (Descriptive Analytics):
The first phase focuses on basic data collection and reporting. It involves gathering HR data and summarizing it into reports that describe what has happened in the past, such as headcount, turnover rates, and absence rates. The aim is to provide a snapshot of current or historical HR performance.
-
Advanced Reporting (Diagnostic Analytics):
This phase goes a step further by not just describing what has happened but also diagnosing reasons behind those outcomes. It involves more detailed analysis, such as identifying patterns, trends, and correlations within the HR data. For example, it might analyze the impact of employee engagement on productivity or explore the reasons behind high turnover rates in specific departments.
-
Strategic Analytics (Predictive Analytics):
At this stage, HR analytics begins to forecast future trends based on historical data. Using statistical models and machine learning algorithms, it predicts outcomes such as which employees are at risk of leaving the company or the future impact of training programs on employee performance. The focus shifts from understanding the past and present to predicting the future.
-
Prescriptive Analytics:
The most advanced phase of HR analytics, prescriptive analytics not only predicts what will happen but also suggests actions to achieve desired outcomes. It involves using sophisticated analytical techniques to recommend strategies for enhancing employee satisfaction, reducing turnover, and improving overall workforce effectiveness. Prescriptive analytics can help HR leaders make informed decisions on how to best allocate resources and design HR policies.
Different phases of Predictive Modelling:
Predictive modeling is a statistical or machine learning technique used to forecast future events or outcomes by analyzing patterns in historical and current data. The process of developing a predictive model can be broken down into several key phases, each critical to ensuring the model’s accuracy, effectiveness, and applicability to real-world scenarios. These phases typically include:
-
Problem Definition:
The first step involves clearly defining the problem or question that the predictive model aims to solve or answer. This includes understanding the business or research objectives, identifying the target variable (what you are trying to predict), and determining the scope and limitations of the model.
-
Data Collection:
In this phase, relevant data is gathered from various sources that will be used to train and test the model. Data can come from internal databases, external datasets, or real-time data streams, depending on the problem being addressed.
-
Data Preprocessing:
Raw data often contain errors, missing values, or inconsistencies that need to be addressed before modeling. This phase involves cleaning the data, dealing with missing values, and possibly transforming variables to make the data suitable for analysis. It may also involve feature selection or extraction to identify the most relevant variables for the model.
-
Exploratory Data Analysis (EDA):
EDA is a crucial step where data scientists explore and visualize the data to uncover patterns, anomalies, or relationships between variables. This helps in gaining insights into the data and informing the choice of modeling techniques.
-
Model Selection:
Based on the insights from EDA and the nature of the problem, one or more predictive modeling techniques are selected. Common methods include linear regression, logistic regression, decision trees, random forests, gradient boosting machines, and neural networks, among others.
-
Model Training:
The selected model is trained using a portion of the collected data. This involves adjusting the model’s parameters so that it can accurately predict the target variable based on the input features.
-
Model Testing and Validation:
The trained model is tested on a separate dataset (not used during training) to evaluate its performance. Metrics such as accuracy, precision, recall, F1 score, or mean squared error are used, depending on the type of prediction problem (classification or regression). Cross-validation techniques may also be employed to ensure the model’s generalizability.
-
Model Tuning:
Based on the performance metrics, the model may be adjusted or tuned to improve its accuracy. This could involve tweaking the model parameters, selecting different features, or trying different modeling techniques.
-
Deployment:
Once the model performs satisfactorily, it is deployed into a production environment where it can start making predictions on new data. This phase also involves integrating the model with existing systems and processes.
-
Monitoring and Maintenance:
After deployment, the model’s performance is continuously monitored to ensure it remains accurate over time. As new data becomes available, the model may need to be retrained or updated to maintain its effectiveness.
