Posted by In today’s fast-paced and data-driven business landscape, organizations are increasingly turning to data science to transform the way they manage their workforce. The integration of HR analytics and data science in workforce management has emerged as a game-changer, offering valuable insights and strategies that drive enhanced HR decision-making and operational efficiency. This article explores the vital role of data science in HR strategy, workforce planning, data management, and the emergence of specialized data science services tailored to meet the unique needs of modern businesses with workforce management cycle.
Importance of Data Science in Data Management
The significance of data science lies in its ability to integrate expertise from programming, mathematics, and statistics to extract valuable insights and understand data better. The growing importance of data science services can be attributed to the immense value that data holds. For instance, Southwest Airlines managed to save a staggering $100 million by effectively utilizing data. This allowed them to minimize plane idle time on the tarmac and optimize resource utilization. In essence, in the modern business landscape, it is inconceivable for any company to operate without leveraging the power of data.
How is it relevant to Workforce Management?
In the digital age, data has emerged as a game-changer for organizations across industries. The field of HR strategy with data science has become a powerful tool, particularly in the domain of workforce management. Data science harnesses the potential of HR analytics and artificial intelligence to enhance human resource processes, optimize workforce efficiency, and drive productivity.
Among all the departments including Operations, Training, and Quality, Workforce Management stands out as the one that heavily relies on data. This becomes evident from the WFM cycle, which includes:
- Forecasting
- Planning
- Scheduling
- Real-Time Analysis
- Reporting
To gain insights into the significance and application of Data Science principles in Workforce Management, let’s briefly examine each section.
- Forecasting – It is a critical element in Workforce Management (WFM) and is known as “Predictive Analytics” in the context of Data Science. Statistical methods such as exponential smoothing and ARIMA are used for Volume or Full-Time Equivalent (FTE) forecasting over time, which is referred to as “Time Series Analysis.”
In the field of Data Science, Time Series is a significant and comprehensive concept that involves extensive knowledge and practical application from start to finish.
E.g.- During a holiday season for a contract centre, the forecasted call volumes indicate that there will be a significant increase in calls on Friday due to a product launch. This allows the contact centre to plan and allocate more agents to handle the anticipated high call volume on that day.
2. Planning – Without proper planning, a business cannot thrive; it is the key to its successful operation. Once the forecasting phase is completed, the data obtained is used in the planning stage to develop strategies related to recruitment, budgeting, revenue projections, and infrastructure decisions.
Data is utilized to analyze the available resources during the hiring process, incorporating descriptive and predictive statistics from Data Science services. This analysis informs the selection of suitable candidates for the organization.
E.g.- Based on the forecasted call volumes, the contact centre management uses Data Science algorithms to create staffing plans for the week. The algorithm considers agent availability, skills, and the predicted call demand at different times. The result is an optimized plan that ensures sufficient agent coverage during peak hours while minimizing idle time during slower periods.
3. Scheduling – The prevailing trend in Scheduling revolves around the Gig economy and preference-based scheduling. It is crucial to gather employee data and analyse it to anticipate their shifting preferences effectively.
While traditional methods like Erlang and Linear workload are still in use, the future of scheduling lies in data-driven artificial intelligence-based scheduling.
E.g.- Using Data Science-driven scheduling, the contact centre assigns shift to agents considering their skillsets, preferences, and contractual obligations. Suppose an agent prefers to work evenings and has excellent technical skills. The scheduling algorithm will assign them to handle technical support calls during evening shifts, leading to higher job satisfaction and increased efficiency.
4. Real-Time Analysis – As a Workforce Management (WFM) professional, a significant portion of our time is dedicated to future planning. However, the actual implementation of the plan occurs during Traffic Management or Real-Time Monitoring. During this stage, companies utilize immediate data to make predictions and devise plans for the remainder of the day.
This involves determining factors such as the feasibility of allowing additional breaks or auxiliary time while still meeting service level targets. It also entails deciding whether acquiring more overtime is necessary to clear the emails in the queue, among other considerations.
E.g.- In real-time, the contact centre monitors incoming call volumes and service levels. If there is an unexpected surge in calls, Data Science algorithms can dynamically adjust agent assignments. For instance, the system can identify that a group of agents is skilled in handling a specific type of customer inquiry and automatically route more calls to that group to meet service level targets promptly.
5. Reporting – Reporting is the sole domain that reveals actual performance compared to the initial plans. This data holds valuable insights for further analysis and predictions.
Business Intelligence or Data Visualization is a branch of Data Science that enables storytelling through data. The presentation of data uncovers hidden information, strengthening decision-making and making it more strategic.
E.g. – Data Science is employed to generate comprehensive reports on agent performance metrics, customer satisfaction scores, and key operational KPIs. The reports provide insights into the average handling time, first call resolution rate, and customer feedback. By analysing this data, the contact centre identifies areas for improvement, such as agent training needs or process optimizations, to enhance overall performance.
Wrapping Up
Bravo! you’ve come a long way. Now you have fair understanding about importance of data science in workforce management.
Examples mentioned in each stage of Workforce management cycle demonstrate how Data Science concepts are applied at each stage to optimize workforce management, improve customer experiences, and enhance overall operational efficiency.
But if you’re still confused about what kind of data science services or tools you need for your organisation. We at Polestar Solutions assist you with solving your personalized requirements with our discovery workshop.
To leverage a tailored data science solution for your organization, book a free consultation today!