HR Analytics for Organizational growth
In recent years, the field of human resource management has grown excessively. People management has traditionally been one of the more human-centered facets of an organization, making it difficult for professionals to clearly measure and compare results, such as in the sales. HR Analytics has shifted the model, bringing a data-driven approach built on modern human capital processes to accelerate long-term market success by engaging HR KPIs and OKRs. Since there is so much data available within the organization, HR teams must first determine which data is most important and how to use it to get the best return on investment. Management can strengthen their game and offer practical guidance by assessing recruiting costs, recognizing workforce attrition rate, seeking training tools to improve employee performance, and answering even more queries.
What is HR analytics ?
Call it as people analytics or talent analytics or workforce analytics all referred to HR analytics which involves collecting, analyzing, and reporting HR data like Employee surveys, Attendance record, reviews, career history, Demographic and Personality data, and much more. This data helps to measure overall business performance and make decisions based on data for organization growth. Since that day Human Resource Management has changed dramatically with technological transformation. The HRMS has shifted from an operational discipline towards a more strategic approach, The popularity of the term Strategic Human Resource Management (SHRM) demonstrates this. The data-driven approach that characterizes HR analytics is in line with this development, by using analytics HR doesn't have to rely on gut feeling anymore. Furthermore, HR analytics helps to test the effectiveness of HR policies and different interventions within the organization. In this age of disruption and uncertainty, it is vital to make the best decisions in order to navigate our new realities.
What are the 4 different levels of HR Analytics
1. Descriptive analytics
Descriptive analytics is the process of using active and historical data to identify trends. It’s the most basic and simplest level of analytics. Organizations often spend most of their time at this level, thinking about dashboards and why they exist: to create reports and present what happened in the past. It's a vital step in the world of analysis and decision-making, but it's really only the first step. It is important to go beyond the initial observations and to delve into the ideas, which constitute the second level of analysis.
2. Diagnostic analytics
Diagnostic analysis is where we answer what and why. This is where the ability to ask questions about data and tie those questions to business goals and imperatives is most important. Imagine going to a doctor where the only thing they do is look at you, remark "oh yeah, you look sick", and then walk out of the room. It won't do much for your health. We need to be able to understand what causes disease. The doctor should observe you, diagnose you, and then give you a treatment plan to help you feel better. With analytics it's the same thing you make an observation, identify the descriptive analysis and go to the diagnosis.
3. Predictive analytics
With Predictive analytics organizations can predict the result of different decisions, test their success, find business pain points, make more predictions, and more. This flow allows HR to see how the first three levels can work together within the organizations. Predictive analytics involves technologies like machine learning, algorithms, and artificial intelligence, which gives it power because that's where data science comes in. With the combined level of analytics at the first two levels, organizations can really see success with their data and analytics strategies. However, the reality is that most people in your organization don't spend much time on predictive analytics these days. Management spend most of their time on descriptions and diagnoses, but prediction is a very important part of the puzzle.
4. Prescriptive analytics
Prescriptive analytics focuses on finding the best action plan. Prescriptive analytics is about descriptive analytics and predictive analytics, but emphasizes actionable insights rather than monitoring data. Descriptive analytics offers BI information about what happened, and predictive analytics focuses on predicting possible outcomes, prescriptive analytics aims to find the best solution with a variety of options. Furthermore, the domain also enables businesses to make decisions based on optimizing the outcome of future events or risks and provides a model to study them.
What are the Benefits of HR Analytics
Best talent acquisition when HR tracks data on key recruitment metrics such as candidate experience, cost per hire, application completion, quality of source, and quality of hire, HR instantly gains valuable insight into the recruitment process. HR can identify areas that need improving using analytic tools, and make changes to the organization acquisition process that will have a noticeable impact.
Prevents workplace misconduct Another important benefit that HR analytics will help is preventing misconduct in the workplace. For example, using data gathered from incident reports can help a company or department identify trends or common occurrences of misconduct. This information can be used to identify areas where training can help prevent future misconduct.
Improving employee experience matters, so providing a great work environment or a seamless recruitment and onboarding process can improve retention and contribute to better talent acquisition in the future. Candidate and employee experience is a critical topic in HR and one of the top trends in HR. Research found that 67% of job seekers encountered a technical issue during the application process that could affect their ability to find top talent.
Employee Professional development is often an integral part of any organizational culture. Helping employees to grow and learn new skills that aids to career development will have a direct impact on company performance. Collecting data on where employees might need training to identify if employees are making the most of training opportunities.
Better Performance can only be achieved with the right skill set. Providing employees with the right training can boost the production and improve the overall performance of the organization increasing the values of both internal and organizational assets.
Conclusion
Without data it is impossible for HR to manage people and organize their activities at the same time, with the data centric approach many organizations are moving towards HRMS solutions that provide a complete employee life cycle from hire to retirement. The best HRMS software.