HR data-driven organizations use the data to make HR decisions. HR analytics, sometimes called people analytics, workforce analytics, or talent analytics, focuses on using data to analyze people’s issues to provide important organizational insights. This makes it possible to make smarter, data-driven decisions.
Analytics in HR is a relatively new tool. It helps your company analyze data to determine the effect of various HR KPIs on overall business performance. In other words, HR analytics is a human resources management strategy based on data.
Companies have focused more on HR analytics during the past few years. HR data-driven organizations are establishing analytics systems solely focused on bringing improvements to the business.
Importance of HR analytics in HR data-driven organizations
The foundation of HR analytics is data. Improving decision-making in these areas helps HR practitioners gather, organize, and analyze HR data-related functions, including hiring, managing, engaging, and retaining top people.
Every day, HR departments use software tools and technology to produce large amounts of data. But getting the most out of the data and appropriately interpreting it is the primary goal. The following list of benefits of HR analytics
1. Improves HR performance
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Most HR data-driven organizations have improved HR performance. Better decision-making is one key element that can boost HR performance. HR analytics is crucial given the growing requirement for data that enables this.
2. Identify the top talent
Accessing and evaluating data gathered about a company's employees can provide important information. The identification of the best-performing employees inside an organization is a prime illustration of this.
3. Determine the causes of attrition:
Employee data can uncover departments with high attrition rates and patterns of attrition, find commonalities and establish core causes.
4. Predict in-demand positions and skills inside the company:
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HR analytics data can assist practitioners in precisely determining positional and skill requirements within their companies.
5. Changes in how HR functions as a strategic partner:
The HR department is uniquely positioned to affect employment decisions within an organization, thanks to HR analytics. HR can provide the corporate executive with precise, tested, and verifiable data to support employee hiring, retention, and engagement decisions. This is so because information on employees provides useful insight, greatly enhancing the strength of human resources.
HR Metrics used in HR data-driven organizations
Without a shadow of a doubt, talent plays a significant role in an organization's success. A long-term indicator of a company's success is its ability to acquire, manage, and harness resources.
1. HR data-driven organizations metrics: Absenteeism Rate
The absenteeism rate examines unscheduled missed workdays over a predetermined time frame. For instance, whereas scheduled vacation time using paid time off that has been approved in advance does not affect the absence percentage, a sick day does.
Absenteeism rate = (Total # of unplanned absences / total time period) x 100
2. HR data-driven organizations metrics: Involuntary Turnover rate
This KPI tracks the number of fired employees. Layoffs and involuntary terminations, also known as being fired, fall under this category. However, this figure does not account for workers who depart on their own. This indicator may represent attempts to manage the staff and hiring procedures to find the best candidate for the job.
Turnover rate = (# of employees who involuntarily left the company over a given period / average number of employees in the same period) x 100
3. HR data-driven organizations metrics: Voluntary Turnover rate
Employees who voluntarily leave their jobs are considered under voluntary turnover. High turnover may signify one or more problems, such as inadequate pay, a toxic workplace environment, or a poor benefits package. For an organization, a high voluntary turnover rate can be expensive. Monitor this measure to learn more about how retention efforts are performing.
4. HR data-driven organizations metrics: Revenue per employee
Derived by dividing the total number of employees by the company's revenue. This shows the typical revenue that each employee produces. It is a gauge of how effectively an organization enables employee-based revenue production. This indicator demonstrates the effectiveness of the company as a whole. The \"revenue per employee\" measure serves as a gauge of the effectiveness of recruited personnel.
5. HR data-driven organizations metrics: Offer acceptance rate
How frequently do job seekers accept your offer? This may impact your hiring procedures, compensation and benefit plans, and reputation with future employees. The frequency with which employees take the initial offer presented to them without haggling for better terms or additional benefits is a variation of this rate.
This is the number of official job offers (not verbal ones) that were accepted to all the offers made over a specific time period. A greater percentage (above 85%) denotes a favorable ratio. The company's talent acquisition strategy can be revised using this data if it is lower.
6. HR data driven organizations metrics: Cost of hire
The \"cost per hiring (CPH)\" measure displays how much it costs the business to hire new employees, similar to the \"time to hire\" metric. This also indicates how effectively the hiring process is working.
CPH can be determined by multiplying internal and external recruiting expenses by the total number of hires. Both the costs and the number of hiring will reflect the chosen measurement period, which could be monthly or yearly.
7. HR data driven organizations metrics: Time to hire
Long hiring processes may cause qualified candidates to be overlooked in favor of quicker, more effective hiring procedures. Examine how long it took from when the person you hired submitted their application or was otherwise sourced for the position to when the new employee starts working once your hiring procedure is complete. This statistic offers perceptions of the experience of prospective hires.
Time to hire = # of days elapsed from time job is posted to the first day on the job
8. HR data driven organizations metrics: Time to fill
The period of time between posting a job opportunity and employing a candidate to fill it. Recruiters might change their recruitment strategy by monitoring the time to fill to determine the regions where the most time is being spent. This statistic, similar to the time to hire KPI, examines the time between the approval of a new role or a position opening and the time that role is filled.
The time it takes to create a job description, post the job, find candidates, hold interviews, and extend an offer are all included in this statistic.
Time to fill = # of days job positions are open/total # of job positions open
9. HR data-driven organizations metrics: Human capital risk (HCR)
This may include employee-related risks such as lacking a particular skill to fill a new type of job and the scarcity of qualified candidates for leadership positions. It may also include the possibility that an employee will quit their job for several reasons, including their relationship with managers, pay, and the lack of a clear succession plan as employee-related risks. All of these parameters can be measured using HR analytics.
10. HR data-driven organizations metrics: Cost of training per employee
Add up all the costs expended for employee training, including software licenses, room rentals, training staff salaries, and any additional charges. To determine your return on investment for training, compare the results in increased revenue, higher efficiency, and enhanced customer and staff satisfaction.
How to become an HR data-driven organization
1. Pick metrics and KPIs to track and forecast
It is a task in and of itself to choose metrics and indicators for HR systems with predictive capabilities. To predict results, machine learning models consider characteristics of the problem called features (future events or values).
Specialists must choose pertinent features for the training of a model to create one that can predict outcomes with the required accuracy. Therefore, you must assess the collection of potential causes for the specific (positive or negative) future occurrence you wish to forecast.
2. Describe the data sources
Add these portals to your list if you intend to take into account information that applicants or current employees provide on social networking websites. Additionally, confirm that you have permission to access and utilize individual-level data gathered by third-party survey providers. Documenting the sources to obtain this data won't be difficult once you've specified the indicators to monitor or predict.
3. Choose a tool: a ready-made one or a custom one
As a result, you specified the systems from which to obtain data, dealt with questions of data ownership with partners, described the range of duties your HR system must carry out, the metrics and KPIs to measure, and events or forecasts. The decision to purchase an already-made tool or create your own system comes next.
4. Assemble a team
You'll need to assemble a team or hire one if developing a custom HR analytics solution is a better option for your business. The team must include human resources and technology experts who will translate business requirements into features and implement the solution's back and front end.
5. Data collection:
From hiring practices to talent management, and overall employee performance, the HR department may learn a lot. The time has come to gather the data you will need to complete your research after defining your broad objectives. A contemporary BI reporting tool can assist you in doing this by making it simple to combine data from many sources and compile it into an understandable report.
Employee profiles, high performers, pay histories, demographics, training, engagement, retention, turnover, and much other information are frequently gathered during this process.
6. Visualize your findings:
Gathering pertinent HR data and establishing goals is useless if you can't draw conclusions from them. You must use contemporary visuals to make the material clear to all audiences. To demonstrate the effectiveness of visualizing your human resources data, we've already provided you with a selection of HR dashboard examples. This will make you more relatable to non-technical people because it clarifies and simplifies the material.
7. Share your research and apply it:
Processes for HR data analytics that are effective save businesses time and money. It is time to communicate your findings and implement them after you have visualized your data and drawn pertinent conclusions. For instance, if you discovered that creative teams have a high turnover rate, you could implement retention initiatives to stop any more departures. A key takeaway from this is to be constantly changing. Data-driven decisions present a wide range of opportunities, and HR departments that keep on top of them will win.
Conclusion
HR data driven organizations apply research methodologies and cutting-edge statistical tools to analyze HR data to find answers or make long-term choices. Utilizing the data held by the organization may help choose the best course of action.