The pandemic has shown that an agile Human Resources (HR) function is a key to organizational resilience in treacherous market conditions.
Thought-leader HR Teams that were able to adapt to changing employee expectations and reinvent their employee value propositions accordingly were successful in retaining talent. One of the chief differentiators of high-performing HR departments is the continuous adherence to data-backed HR strategies.
Know more about how data-driven HR strategies can benefit your organization with real-life examples:
What is a Data-Driven HR Strategy?
In a data-driven strategy, the HR function makes decisions basis insights derived from data rather than human instincts or judgments. Based on their data maturity, HR teams may use operational reports for forensic data analysis, visual dashboards for real-time monitoring of KPIs, or analytical models for predicting HR outcomes and scenario planning.
Why do Organizations need a Data-Driven HR Strategy?
A data-driven HR strategy eliminates bias and human error in decision-making across all HR functional areas.
It empowers HR professionals to make quick, evidence-based business decisions that drive agility and productivity. It also helps secure top management buy-in for strategic initiatives with predictable business results and quantifiable cost-benefit analysis.
1. To Eliminate Bias in Recruitment
A data-driven recruitment process relies on objective skill assessment test scores more than qualifications and past work experience to judge candidate skillsets. By analyzing new-hire retention data against personality and psychometric test scores, enterprises can predict which candidates will likely stay with the organization longer.
An in-depth assessment of recruitment metrics such as average time-to-fill, the average cost per hire, and the percentage of candidates who drop out mid-process can provide HR executives with insights into how to increase the efficiency and effectiveness of the recruitment process.
2. To Boost Employee Engagement
Conventionally, HR teams use survey techniques to capture and understand how employees feel about their organization. Advanced artificial intelligence (AI)-based algorithms can also analyze unstructured text data from employee conversations over electronic channels such as emails, chats, and social media to understand employee sentiments and their employee engagement efforts.
For example, IBM used its internal social media platform – IBM Connections – to engage employees in revamping their existing performance management systems. It used its sentiment analysis tool ‘Social Pulse’ for real-time analysis of comments and feedback from employees across 170 countries. IBM discovered that employees were unhappy with a curve-based grading system, and it immediately did away with this rating method.
3. To Enable Strategic Workforce Planning
Workforce planning helps prevent over or understaffing and identify skill gaps within the current teams. It also enables you to identify and nurture your top performers for future leadership roles as part of succession planning.
Analyzing the current workforce composition, performance data, and organizational growth projections can help you predict the skillsets and size of the workforce you will need in the future. It helps HR teams decide about hiring full-time employees, contractors, or outsourcing based on data-driven cost-benefit analysis.
4. To Predict and Prevent Attrition
You can analyze attrition rates by filtering data across several criteria like the team or manager, employee segments, or demography to gain insights into the possible reasons for attrition. Analyzing attrition trends month-on-month can help identify red flags such as increasing exits among new hires or particular underrepresented communities.
Predictive analytical models based on metrics such as average employee tenure, employee satisfaction scores, and appraisal data can help you identify top management executives at flight risk. HR Teams can conduct a root cause analysis and augment their employee value proposition with necessary components such as flexibility, internal job mobility, or leadership opportunities based on feedback surveys to prevent this type of attrition.
5. To Build a Truly Diverse Workforce
Data and analytics can support your DE&I objectives. Measuring the demographic distribution of the workforce alone does not suffice. Tracking key workforce metrics across employee groups such as retention, promotion rates, pay equity, and employee resource group (ERG) participation provides a complete picture of the diversity and inclusiveness of your organization.
The American multinational tech-giant Intel leveraged data to meet DE&I objectives. It collected data from 7000 employees to understand key drivers for improved DE&I. One of the insights resulted in the launch of Intel’s Warmline, a confidential employee service. Since its inception, it has received more than 20,000 cases and successfully achieved a 90% retention rate among employees utilizing this service.
6. To Improve Training Effectiveness
You can leverage data to personalize learning content according to employee role and career aspirations. Another important application of data analytics is to track training effectiveness and its impact on employee productivity over time. HR Teams can use post-training surveys and objective test assessments to understand if the training courses meet employee expectations and learning goals.
Analyzing the average training expense per employee across different modes – like online, offline, live, and self-paced – versus the efficacy of each can help you identify the training methods that generate the most return on investment.
7. To Manage Payroll Risks
Payroll data analytics can help design risk mitigation strategies that can proactively alert you about potential frauds like ghost employees, timesheet padding, and proxy attendance. Data-driven payroll security protocols can flag potential security breaches due to cyberattacks and help take prompt corrective action.
Analyzing payroll metrics such as the number of off-cycle payments, percentage of delayed payments, number of payroll-related complaints, and number of payroll compliance issues can help HR teams improve the payroll workflows to ensure prompt and accurate payouts.
8. To Design Competitive Compensation Packages
An in-depth analysis of market salary data is essential to match or outperform competition consistently while making offers to selected candidates. A data-based approach can empower you to lead with your best offer and build trust among candidates to drive up offer acceptance rates.
Monitoring compensation metrics such as employee cost factor (ECF) and Return on Human Capital investment helps HR teams to understand the overall costs of financial and non-financial compensation and align them to budgets.
9. To Manage Performance Better
Enterprises are moving away from conventional appraisal methods that frequently result in evaluations based on manager bias. But, a data-driven HR strategy can help avoid such unjust bias. Modern goal-setting methods such as Objectives and Key Results (OKRs) define measurable goals, and 360-degree feedback data allow managers to assess employee performance fairly.
Also, tracking performance metrics such as average revenue per employee, service efficiency, sales productivity, and absenteeism rate offer insight into determining the correct performance pay for employees.
10. To Enable HR Cost Optimization
You must take decisions as per HR data driven approach toward cost optimization efforts during business or economic downturns. HR cost analytics and employee feedback data can help you identify opportunities for rationalizing benefits packages and variable payouts while protecting the employee experience. Workforce data can also help you identify outsourcing options, internal job deployments, and assignments suitable for gig workers to reduce costs.
How to Build Your Data-Driven HR Strategy?
1. Define HR Goals
HR leaders must collaborate with top management to align HR strategic goals with the vision and objectives of the organization.
2. Identify Measurement Areas
Identify the cascading HR metrics you need to measure based on the HR goals. It will also help you identify the associated processes and workflows.
3. Gather Data with Automation
Automation is the true catalyst for a data-driven HR strategy. However, to make full use of it, you must be data-ready for HRMS implementation. Once implemented, it would ensure clean data collection and allows the designing of reports, dashboards, and analytical models required to track metrics and derive insights into business processes.
4. Inculcate a data-driven HR culture
Each strategic HR recommendation must come from a clear hypothesis being validated or rejected by analyzing data patterns. Employing prototypes is also one of the essential features of a data-driven culture.
5. Leverage Storytelling
HR teams must be adept at crafting compelling data visualizations and concise presentations. It helps effectively convey the business question and its data-based answer to the top management.
Real-life Example
Global Investment Bank Credit Suisse used churn analytics to predict and arrest attrition. A one-point reduction in unwanted attrition rates saved the bank $75 million to $100 million a year.
Its analytical teams studied employee data points like raises, promotions, and life transitions and developed algorithms to predict if they will stay or leave in the subsequent year. They discovered that changing responsibilities helps in employee retention.
The bank launched a global effort to reach out to current employees for internal moves whenever jobs opened up. Their recruiters also used attrition probability estimates in deciding which employees to target for internal job postings.
Final Words
As your HR function matures in terms of automation and creating a robust data foundation, you must aim to transition from operational reporting and diagnostic analysis toward creating predictive models. Data analytics in robust HR software can help accurately predict business outcomes in different scenarios if high volumes of reliable data are available for machine learning.