Rajesh Murthy is the Founder Architect and Vice President of Engineering at Intellicus Technologies, a data integration and BI product company. Being the Founder Architect and VP of the company, Rajesh spearheads numerous responsibilities including Product Development/Design, Engineering, and Marketing and Sales.
Predictive/advanced analytics is transforming the HR landscape. From identifying and picking the right candidate for the job to analyzing employee performance and attrition, several organizations are adopting HR analytics to benefit several vital areas of human resource processes.
The idea behind embracing AI-ML-driven predictive analytics is to get an in-depth insight into how to efficiently manage employees and reach business goals. The information generated helps business leaders devise strategies on talent development, improvise workforce planning, recruiting processes, and address gaps in individual performance.
So, what are the ways in which predictive HR analytics helps to forecast resource demand?
Improvise Workforce Planning: AI-ML-driven analytics helps organizations forecast resource demand (workforce) across the organization and efficiently distribute it. Predictive analytics help leaders ensure that they hire the right talent for the right role to eliminate issues like low workforce in the team. It also allows organizations to stay a step ahead by predicting the potential demand gaps and competency issues. HR analytics predict day-to-day requirements and allocation needs for dynamic workplaces such as call centers and maintain a steady supply of resources. The managers also identify which teams are executing assigned projects on time and which are overburdened.
Moreover, predictive analytics via AI and ML empowers businesses to predict volume based on historical arrival patterns, seasonality, festivity, and others. Based on forecasts, companies can predict staff count and automate the staff count’s schedule and roster. Organizations achieve higher efficiency and ROI through AI-driven smart scheduling. Also, analytics can help organizations manage performance in real-time, optimize revenue, and provide better services to customers.
Demand Analysis and Hiring: After getting a comprehensive picture of resource demand, organizations receive different data patterns and get predictive insights on hiring for the next 6 to twelve months. Using predictive HR analytics, business leaders spot the right time and location to fill a specific position in the organization. The technology lets companies know the number of candidates likely to accept or reject the job offers. Making use of these insights, available at every stage, the HR team then plans to implement solutions to increase the probability of successful hiring.
Predictive HR analytics further enables businesses to correlate these insights with expenses and revenue. It helps business heads to foresee cost per hire in different departments across locations and plan recruitment budgets accordingly.
Employee Performance Management: Using predictive analytics, the HR department tracks employee performance trends and identifies key factors contributing to their overall achievements. The AI-ML-driven method enables employees to understand their goals and KPIs. Moreover, they identify areas where their performance is good and the ones they need to work on. All in all, HR professionals have a company-wide picture of the strengths and weaknesses of each employee, team, and need for resources in different departments.
Project Management: Cost overruns, time delays, or the need for more resources are some of the reasons why projects get out of hand. Predictive analytics address these concerns to a large extent. Predictive analytics allows organizations to understand each aspect of a project better, identify possible desirable or undesirable developments in real-time.
Budgeting and Planning: The technology enables organizations to analyze several expenses associated with each employee, department, or business unit located in different cities.
Impact of predictive analytics on resource demand
One of the use cases of predictive analysis is in BPM (business process management). A decade ago, BPM was executed manually. But now, as businesses are undergoing digital transformation, data has become an essential enterprise asset. It is quickly revolutionizing the business processes while making them better and faster. Moreover, the data being generated from these activities quickly goes up to trillions of rows. This is where predictive analytics plays a game-changing role by providing insights in forecasting resource demand.
A study by McKinsey highlights that organizations improve their recruiting efficiency by 80% by applying predictive analytics. It also leads to a 50% decrease in attrition rate, increasing business productivity by 25%.
Predictive HR analytics enhances human judgment contributing to more accurate outcomes in terms of resource management and hiring. With data recognized as a new resource, organizations are required to focus on building a foundation of high-quality data to strengthen their predictive analytics processes. The future potential of predictive HR analytics depends on how well businesses take advantage of it. Further, it becomes important to identify what added value it can deliver by analyzing trends, enhancing the employee experience, and improving business operations. A recent report suggests that the global predictive analytics market size will reach $21.5 billion by 2025 from $7.2 billion in 2020, growing at a CAGR of 24.5%. Hence, it’s high time organizations tap the potential of predictive analytics to unlock more opportunities in the future.