An HR strategy is what, exactly?
An HR strategy is a plan that links a company's human resources to its operations and uses solutions that put the needs of its most critical problems first.
All of the major HR functions, including hiring, talent management, remuneration, succession planning, and company culture, are guided by the HR strategy. According to Gartner, it is a long-term strategy that coordinates HR procedures across the entire organisation and is distinguished by:
A solid comprehension of the objectives, strategy, and mission of your company.
Determining the essential traits and talents required to reach those objectives.
Determining the gaps between the present situation and the organization's future needs by assessing the current talents and skills of your talent and the HR function.
The creation of HR objectives to close gaps and define standards for gauging the effectiveness of strategy execution.
Annual review and communication of the HR strategy inside the organisation.
Data analysis aids in better decision-making.
Why is a data-driven HR strategy necessary for organisations?
In all HR functional areas, a data-driven HR strategy removes prejudice and human error in decision-making.
It gives HR professionals the ability to take prompt, fact-based business choices that increase productivity and agility. It also aids in securing top management support for strategic projects by providing verifiable cost-benefit analysis and predicted business results.
1. To end discrimination in hiring
When evaluating candidate skill sets, a data-driven recruitment strategy prioritises objective skill assessment test results over credentials and prior work experience. Businesses may identify which candidates are most likely to be around the company for a longer period of time by comparing new-hire retention data with personality and psychometric test results.
HR professionals may learn more about how to improve the effectiveness and efficiency of the hiring process by doing a thorough analysis of recruiting data including the average time to fill positions, the average cost per hire, and the percentage of candidates that withdraw during the application process.
2. To increase staff engagement
Traditionally, HR teams have used survey methods to gather and comprehend employee perceptions of their company. In order to comprehend employee feelings and their employee engagement initiatives, advanced artificial intelligence (AI)-based algorithms can also analyse unstructured text data from employee dialogues across electronic channels including emails, chats, and social media.
3. In order to facilitate strategic workforce planning
Workforce planning identifies skill shortages in the current teams and helps avoid over or understaffing. As part of succession planning, it also enables you to recognise and develop your best performers for upcoming leadership responsibilities.
You can forecast the skill sets and size of the workforce you will require in the future by analysing the composition of the current workforce, performance statistics, and organisational growth estimates. It assists HR departments in making data-driven cost-benefit analysis-based decisions regarding recruiting full-time workers, contractors, or outsourcing.
4. To anticipate and avoid attrition
To acquire insights into the potential causes of attrition, you can analyse attrition rates by filtering data across many parameters, such as the team or manager, employee groups, or demography. Analysing attrition patterns month over month might help spot warning signs like rising attrition among recent hires or specific underrepresented demographics.
You can identify top management executives at flight risk by using predictive analytical models based on indicators like average employee tenure, employee satisfaction ratings, and assessment data. To avoid this kind of loss, HR Teams can do a root cause analysis and add the required elements to their employee value proposition, such as flexibility, internal career mobility, or leadership chances, based on feedback surveys.
5. To create a workforce that is truly diverse
Your DE&I goals can be supported by data and analytics. Measuring the demographic makeup of the workforce is insufficient on its own. A holistic picture of the diversity and inclusivity of your organisation can be obtained by tracking important workforce metrics across employee categories, such as retention, promotion rates, pay fairness, and employee resource group (ERG) involvement.
6. To increase training efficacy
Utilising data, you can tailor instructional materials to each employee's position and career goals. Monitoring training effectiveness and its effects on employee productivity over time is another crucial use of data analytics. To determine whether the training programmes fulfil the expectations of the students and their learning objectives, HR Teams can use objective test assessments and post-training surveys.
You can determine the training techniques that produce the highest return on investment by comparing the average cost of training per employee across several modes, such as online, offline, live, and self-paced, with each mode's effectiveness.
7. To control payroll risks
Payroll data analytics can assist in creating risk management plans that can forewarn you in advance of potential frauds including ghost employees, timesheet padding, and proxy attendance. Payroll security protocols that are data-driven can identify potential security breaches brought on by cyberattacks and assist in taking quick corrective action.
HR teams can enhance payroll procedures to ensure fast and accurate payouts by analysing payroll KPIs including the number of off-cycle payments, percentage of delayed payments, number of payroll-related complaints, and number of payroll compliance concerns.
8. To create compensation plans that are competitive
Making proposals to qualified individuals requires a thorough review of market salary data in order to regularly equal or beat the competition. Building trust with applicants through a data-based strategy will enable you to lead with your best offer and increase offer acceptance rates.
In order to understand the total costs of financial and non-financial compensation and align them with budgets, HR teams can benefit from tracking compensation indicators like employee cost factor (ECF) and Return on Human Capital investment.
9. To control performance
Businesses are eschewing traditional appraisal techniques that frequently provide judgements based on manager prejudice. A data-driven HR strategy, however, can assist prevent such unfair bias. Objectives and Key Results (OKRs), a contemporary goal-setting technique, define measurable targets and data from 360-degree feedback to enable managers to objectively evaluate employee performance.
The proper performance pay for employees can also be determined by monitoring performance parameters like average revenue per employee, service effectiveness, sales productivity, and absence rate.
10. To facilitate HR cost optimisation
During periods of business or economic depression, you must make judgements based on an HR data-driven approach to cost optimisation activities. You can find possibilities to rationalise benefit packages and variable payouts while preserving the employee experience with the aid of HR cost analytics and employee feedback data. In order to cut expenses, workforce data can also assist you in identifying outsourcing possibilities, internal task deployments, and assignments appropriate for gig workers.
10 Key Steps for Creating a Data-Driven HR Strategy
To better understand how employees feel about the company and what they need, HR leaders should use analytical solutions. This will help them create HR strategies that are more focused on the needs of the workforce. Ten essential steps are listed below:
1. Identify a business issue
Instead of starting with a human resources issue, such as how to reduce attrition, think about how to boost business profitability or quarterly revenue. Choose a bigger company goal and the actions you'll take to achieve it.
2. Before looking at the data, create a straightforward hypothesis
Don't jump to conclusions by looking at the numbers too quickly. Create hypotheses that can be quickly tested using questionnaires, forms, roundtables, etc. The following is an illustration of a testable hypothesis: "An incentive system increases branch profitability."
3. Gather Data
Only gather the information required for a particular analysis.
4. Analysis of Data
Keep an eye out for revelations that confirm or refute the notion.
5. Rely on past information
Strategic decisions may be influenced by pre-existing HR metrics, like salary histories, attrition rates, employee engagement, and others.
6. Disclose Insights
Organisations will be able to identify trends by compiling the data into a single source.
7. Provide HR advice to help the company's situation
Analyse the information to see what it has to say about a business issue. For instance, "There is a single talent that will increase our sales."
8. Recognise cultural quirks
The way candidates communicate with potential employers and the notion of what constitutes a suitable benchmark for particular measures can both be impacted by cultural variations. The findings generated from regional data sets and even the hypotheses can be interpreted incorrectly if local nuances are not taken into account.
9. Data tells a story
To establish a strong data-driven argument, develop a compelling narrative around the data. Make succinct presentations where the advice is based on the information from your own research.
10. Create a mission and vision statement for HR in your organisation
The mission and vision statements provide an overview of the HR strategy and act as a compass for all ensuing policies and choices.
In Conclusion
Transitioning from operational reporting to predictive modelling in HR offers a progressive approach as automation advances. While operational reporting provides historical and current insights, predictive modelling utilizes past data to forecast future scenarios. As automation generates substantial HR data, it becomes the bedrock for predictive models, allowing anticipation of outcomes such as turnover rates and skill requirements.
Central to this shift is a robust database; precise predictive models rely on high-quality input. Accompanied by dependable HR software, data collection, analysis, and model implementation are streamlined, enhancing metric tracking and refining based on real-world results.
In essence, embracing predictive modelling empowers proactive HR strategies, leveraging data for informed planning. With the growth of automation and data, accurate predictions become attainable. Through merging reliable data, predictive models, and solid software, HR gains the capability to effectively foresee and manage future workforce dynamics, fostering overall organizational triumph.
For assistance in developing an HR Strategy for your organisation, get in touch with Job Booster India.
