HR Analytics has progressed a long way over the years. Businesses can now analyze a wide range of data to determine whether their personnel, resource, and workforce analytics are relevant. HR Analytics, the use of statistics, modeling, and assessment of employee-related aspects to optimize business results, enables HR managers to make data-driven choices to recruit, operate, and employ a thriving workforce.
Here’s a brief overview of “What is HR Analytics? – Descriptive, Predictive, And Prescriptive.”
What Is HR Analytics?
The practice of gathering and analyzing Human Resource (HR) data to boost an effective and efficient workforce performance is known as HR Analytics. The method is also known as talent analytics, people analytics, or workforce analytics. This data analysis approach uses commonly acquired HR data and compares it to HR and organizational objectives. This gives quantifiable data on how HR efforts contribute to the company’s goals and strategy.
For instance, if a software development firm has a high personnel turnover rate, then the organization is not entirely productive. It takes time and money to get employees up to full productivity. HR Analytics gives data-backed knowledge into what works well and what does not, allowing firms to improve and organize more effectively for the future.
As in the above scenario, understanding the root reason for the firm’s high turnover can give significant insight into how it can be decreased. Reduced turnover allows the organization to boost revenue and performance.
What Are the Benefits of HR Analytics?
HR Analytics can assist an organization’s management in processing previously unknown data, implementing new measures, and making better business choices.
- Analyzing specific data and gaining a better grasp of employee opinion via staff satisfaction surveys can give several hints as to why employees choose to leave a firm willingly. This can assist us in putting specific preventative measures in place to reduce the leave rate.
- Data allows team leaders and the HR department to discover what inspires employees and what prevents them from attaining their full potential. The next stage is going beyond the performance evaluation to find trends and build an implementation strategy.
- A comprehensive data analysis can demonstrate if the benefits provided by the firm translate into improved performance or enhanced retention. They can even inform us whether salary rises have minimal effect on particular populations.
- Data can be used to learn how people are feeling and change workplaces to provide a pleasant experience for individuals.
- Create reasonably accurate estimates of staff variations using predictive HR Analytics. Identifying personnel surpluses or shortages in a certain sector will be considerably easier.
- Precise, objective information enables us to make better judgments and take effective measures which affect corporate finances.
Examples in HR Analytics
An employee leaving to join another firm they believe is a better match for them is not an ideal situation for an employer, and it can be expensive in terms of time wasted and earnings. Organizations must identify the underlying cause or trends for turnover to avoid it from becoming a recurring issue.
HR Analytics assists in the prevention of employee turnover
– By collecting and analyzing historical employee churn data to find trends and patterns that suggest why workers depart.
– Collecting data and finding trends of employee productivity and engagement to better understand existing employee job satisfaction.
– Developing a prediction algorithm to identify employees who may be on the verge of quitting.
- Recruitment: HR departments are continually on the lookout for applicants with the proper skills and traits to fit the organization’s performance criteria and the most recent industry trends. However, sorting through stacks of applications daily, shortlisting, and making recruiting decisions based on basic facts is a daunting task that may result in neglecting potential applicants.
HR Analytics assist in the streamlining of the employee recruiting process by:
– Allowing for the rapid and automatic collecting of candidate data from different sources.
– Using HR technology and skills data, we assist recruiters in writing better job advertising to attract the appropriate candidates assisting recruiters in narrowing the list of institutions to visit or collaborate with to save time and resources
– Providing historical data on periods of over-recruiting and under-hiring, using which firms may establish effective long-term hiring strategies
– Choosing where recruiters should advertise job vacancies
– Identifying applicants with traits comparable to the present top-performing employees in the firm
– Comparing recruiting expenses across locations in comparison to a standard
- Employee retention
Employee retention refers to the procedures, rules, and tactics utilized to maintain skilled individuals and decrease turnover in your firm. The primary goal is to limit the number of workers who leave the company over a given period.
Work-life balance refers to the interplay of professional and personal activities and how they are prioritized for employees. Consider how much time an individual invests at work, how much time is spent at home, and how much time is spent thinking about work when at home. Keeping this under balance is difficult but necessary.
How Does HR Analytics work?
HR Analytics collects and analyzes data that may help firms get essential insight into their operations. Overall, HR Analytics takes a multi-step approach to better understanding personnel.
One of the first tasks in HR Analytics is to collect relevant data. Employee identities, performance, statistics on high-performers and low-performers, workforce demographics, pay, promotions, learning, attendance, commitment, acquisition, and turnover are all part of this data. Companies’ HR departments collect and analyze this data to assess the effectiveness of critical HR processes such as talent management, recruiting, and training. Generally, the data needed to perform HR Analytics originates from the existing HR systems. Furthermore, innovative data-collection technologies, such as cloud-based systems, can generate the data. The data should be simple to get and integrate into a reporting system. The collected data must also be structured and categorized. Companies simply refer to the provided information in the future.
Businesses use HR metrics to assess and compare the data they have collected. There must be information that can be traced back. For HR to be able to compare current data and analyze changes, a continual intake of data would be necessary. A comparative baseline is also required to follow the progression of data. Companies utilize three important metrics to measure data: organizational performance, operations, and process optimization.
Examples of HR measurements include:
– Turnover rate: The percentage of employees that leave their positions after one year of employment with a company
– Absenteeism: The average number of days off and the frequency with which employees take time off
– Employing expenses: The overall cost of finding and hiring individuals
Types of HR Analytics – Descriptive, Predictive, And Prescriptive Analytics
Let’s learn the types of HR Analytics one by one:
- Insight into the past: Descriptive HR Analytics
Descriptive HR Analytics meaning describes or summarizes raw data to make it human-interpretable. They are analytics that describes what happened in the past. The past is any moment where an activity occurs, whether one minute ago or one year ago. Descriptive analytics are essential because they enable us to learn from previous behaviors and comprehend how they may impact future results.
- Knowing the Future: Predictive HR Analytics
The capacity to predict what could happen is at the heart of predictive analytics. Understanding the future is the goal of these analyses. Based on data, predictive analytics offers firms actionable insights. Predictive analytics calculates the likelihood of a future result. It is critical to note that no statistical technique can accurately predict the future. These statistics are used by businesses to anticipate what could happen in the future. This is because predictive analytics is built on probability.
- Guidance on Potential Outcomes: Prescriptive HR Analytics
Prescriptive HR Analytics, a relatively new area, allows users to prescribe multiple alternative possible actions and steer them towards a solution. In a word, these analytics are all about making recommendations. Prescriptive analytics seeks to quantify the impact of future actions to advise on potential outcomes before the decisions are taken. Prescriptive analytics anticipates what will happen and why it will happen and provides ideas for actions that will benefit the projections.
How Do They work? Descriptive, Predictive, And Prescriptive Analytics
Let’s understand how Descriptive, Predictive, And Prescriptive Analytics work in detail:
Descriptive Analytics Process
The descriptive analytics process is divided into five major steps:
- First, measures that effectively evaluate performance against corporate goals, such as enhancing operational efficiency or raising revenue, are developed. KPI (key performance indicator) governance is critical to the success of descriptive analytics.
- Data is gathered from sources such as reports and databases. To effectively measure against KPIs, businesses must organize and arrange the appropriate data sources to extract the required data and produce metrics depending on the present status of the business.
- Data preparation occurs before the analysis stage and is crucial to ensuring correctness; it is also one of the most time-consuming tasks for the analyst.
- Summary statistics, clustering, pattern monitoring, and regression analysis are utilized to detect trends in data and assess performance,
- Lastly, charts and graphs are utilized to show findings in an easy-to-understand format.
Examples of Descriptive HR Analytics
- Past events, such as sales and operational data or marketing campaigns, are summarized.
- Data involving social media usage and interaction, such as Instagram and Facebook likes
- Reporting general trends
- Organizing survey findings
Predictive Analytics Process
Probabilities underpin predictive analytics. Predictive analytics strives to forecast potential strategic objectives and the probability of those events by using methods such as data mining, statistical modeling (mathematical relationships between variables to predict outcomes), and machine learning algorithms (classification, regression, and clustering techniques). Machine learning algorithms use existing information and seek to fill in the omitted data with the best approximations possible to produce predictions.
Predictive Analytics HR Examples
- Anticipating preferences
- Determining whether or not employees are considering leaving and then encouraging them to stay
- IT security entails spotting potential security vulnerabilities that necessitate further investigation
- Predicting staff and resource requirements
Prescriptive analytics extends what has been learned from descriptive and predictive analysis by proposing the best potential courses of action for an organization. This is the most difficult level of the business analytics process, needing specialized analytics skills to complete.
Several methods and tools, including principles, statistics, and machine learning algorithms, can be applied to access data, including internal and external data. Machine learning’s capabilities go much beyond what a person can do while striving to attain the same outcomes.
Examples of prescriptive HR Analytics
- Tracking prices
- Improving management, maintenance, pricing modeling, production, and storage
- Assessing factors like readmission rates and the cost-effectiveness of operations
- Risk assessment in terms of price and premium information for customers
The Benefits and Challenges: Descriptive, Predictive, And Prescriptive Analytics
- Employing HR Analytics can allow you to test your theories and assumptions before making any changes to your hiring and talent management procedures.
- Analytics can assist you in improving the performance indicators of your HR team. If the human resources department has access to information such as the turnover rate or the results of an employer satisfaction survey, they will improve the working experience.
- Track statistics such as job acceptance rates and average time-to-hire per position if you do HR analysis appropriately. You can take it a step further by including post-interview surveys for your applicants to see how you can improve the candidate experience.
- By utilizing HR predictive analytics, you can respond to indications such as a reduction in employee satisfaction survey results. This will help you to address issues before they become major ones and devise for the unavoidable.
- If the HR department is utilizing a complicated platform and isn’t trained in data analysis, they may miss out on opportunities or derive false conclusions.
- Potential security risks if analytics and third-party technologies with access to sensitive data do not meet the appropriate security requirements.
- If the data violates privacy or accesses confidential employee information, you may face ethical and legal difficulties. If you ever consider analyzing conversation, you must first obtain approval from your staff and consult with a lawyer.
HR Analytics is a treasure for every organization. Every problem has interconnected causes that are difficult to uncover in the absence of sufficient evidence and data. As a result, HR Analytics plays a significant part in your employees and business success. A single decision can determine your future success. HR Analytics leads you on the right path, so all you have to do is follow it. Each business has its HR statistics, and so do you, and beat your competition. If you want a serious digital HR analytics career, UNext Jigsaw’s Certificate Program in People Analytics & Digital HR is perfect for you.