All You Need to Know About HR Analytics

All You Need to Know About HR Analytics
All You Need to Know About HR Analytics

Data analytics has been the hot career option of the decade, and for a good reason. Where there is data, analytics is bound to follow. Humans are a logic-oriented species and feel comfortable when things make sense. The department of Human Resources is no different in this aspect. Let’s look at why HR analytics today play a crucial role in an organization.

HR Analytics Explained

The department of Human Resources has evolved exponentially thanks to the progress in technology. We are in the era of digital HR. There is more information available today due to the number of ways we can capture data online. This data helps us answer important questions - for instance, how investing in human capital will generate revenue, minimize expenses, mitigate risks, and execute strategic plans for the overall success of the organization and the individual.

HR analytics give us a statistical response to questions like: How high is the organization’s employee turnover rate? Which department is losing more employees? Or, how many employees quit within a year from joining? Some of these questions might be easy to answer, but it doesn’t hurt to back them up using well-tested HR analytics and statistical results. The HR department uses gut feeling and economics to stay afloat but not anymore. There is a new arsenal in store to be explored and launched.

Areas of HR Analytics

1. Performance assessment: Some analytic tools assist HR in establishing benchmarks for the employees to reach certain profit margins. These benchmarks are then used to train existing employees to enhance their performance and on the new employees to help them see the direction the organization is headed in. Organizations also gather data to assess individuals and team performances. This assessment is based on 360-degree information and provides insights into who is underperforming and its possible reason. It also helps the HR plan incentives and training programs for the employees. 

2. Assessing promotions and salary decisions: Along with the full performance assessment, these tools help make more informed decisions on promotions, bonuses, and other incentive-related issues. Earlier, these were done solely based on their immediate performance results, but what if a situation arises where an employee has been consistently performing well while another employee just had one great performance? How do you decide which merit carries more weight? HR analytic tools that use Artificial Intelligence Algorithms help make unbiased decisions.

3. Understanding problem areas to improve retention: The employee turnover rate is the highest cause of concern to many organizations, big or small. It is a visible problem, financially draining but is not focused enough to pinpoint the problem areas. HR analytics uncover trends in data that show us areas in the organization that need to be reviewed; thereby, they help narrow down what could be the problem’s source.

What Is the Role of HR Analytics in Modern Business?

Organizations today seek out HR Analytic experts with soft skills, making their profile very similar to that of an HR Business Partner. Data analysts are the first to apply for this job, but data analysis isn’t 100% of the job description. The other requirements include HR duties that a regular analyst would not be able to perform independently. Apart from Data Analysis, the HR duties include:

1. Communication and consultation: Be it spending time with stakeholders or business managers, it is important to set expectations and communicate the analysis results to the audience. Furthermore, provide consultation wherever necessary.

2. Employee and performance management: Analysing resumes using AI Tools that in turn help with the hiring process. The algorithms of using HR analytics evaluate the possible reasons behind employee turnover rates, training, and development pitfalls while also keeping tabs on employee’s job satisfaction, job performance assessments, and any relevant information about every member of the organization.

3. Statistical analysis and insightful models: HR analytics work on data integration and provide analysis like cost-benefit. Statistical analysis is capable of social-networking analysis for HR. This plays a vital role in creating training programs, interaction schemes, and project planning.

Examples in HR Analytics

Let us look at how HR analytics affects modern business processes:

1. Turnover Rate

HRs usually don't know the overarching reasons behind the turnover rate. They have records of individual instances, but there are no scientific answers backing up the turnover trend. Since this is an issue that burns holes in the organization’s pockets, HR analytics collect and analyze data to look for patterns and trends in the numbers that might indicate the reason. Creating a predictive model further helps track employees and flag them when they fall short of their required goals. 

2. Recruitment

Organizations no longer seek employees who are solely competent; as society turns more millennial-dominant, so must the workforce. Recruiting an employee that fits the technical needs along with social demographics is not an easy task. Talent analytics quicken the process by pre-analyzing the candidates and identify the ones who will outperform the rest. 

3. Employee Engagement

HR analytics tools will track the employee’s every movement, from social engagements, working hours, and paid and unpaid vacation days to their performance in activities. This has helped give an insight into employee behavior. With enough data, AI tools can be trained to catch unsatisfactory work routines before they turn into trouble - while also helping the HR department reward the employees who do consistently good work.

How Do HR Analytics Work?

The four main tasks of HR analytics are collection, measurement, analysis, and application. 

1. AI tools and algorithms are incompatible with bad quality data. The collection of high-quality data from various sources or processing data to become more algorithm friendly is the first crucial step in HR analytics. In order to obtain a decent analysis, HRs need to collect data about almost everything, including absenteeism, social engagements, salary, and performance history. 

2. Data is measured on an ongoing basis. Current data is compared to historical data and company norms. 

3. The next step is to analyze the results based on the metrics, identifying impactful changes to the organization. Depending on the desired outcome, analysts choose from three options: (1) Descriptive analysis, which uses historical data to prepare for future improvements; (2) Predictive analysis, which uses historical data to predict future risks and opportunities using statistics; and (3) Prescriptive analysis, which goes the extra mile to forecast the consequences or results of the predictive analysis. 

4. The final and crucial step is to apply said analysis. In order to gain real-life improvements, the HRs plan actions using the findings and to get the ground moving. This is a long and straining step on the HR employees as these changes don’t show immediate results, but the department needs to trust their work and continue to gather results until data patterns show signs of improvement. 

Examples of HR Metrics

HR analytic metrics are used in a multitude of areas, including:

1. Time and recruitment cost to hire: From the time of job postings, scheduling interviews to recruiting and training the newly hired, everything, including time and costs, is monitored, tracked, and tweaked to improve for the future using HR analytics tools. 

2. Time since last promotion and performance potential: Using AI tools to avoid faulty reasoning behind high potential turnover. Measuring and mapping an individual’s performance and potential in three levels using the 9-box grid. 

3. Revenue per employee: This metric keeps tabs on the entire organization. It is an indicator of the quality of the organization’s employees. This measure is also similar to the employee utilization rate, which tracks the number of hours employees work on billable hours.

4. Engagement rating: Engagement of employees is one of the least desired HR soft skills. Since it is the only social interaction most departments tend to have with HR, it is assumed this is all HRs do. However, people who are satisfied with their jobs and are particularly proud of their workplace come from an office of well-engaged employees.

Benefits and Disadvantages of HR Analytics

Let’s begin with the benefits of HR analytics, which usually drive long-term positive results for the organizations that use it well.

  • Unbiased decision making skill is an asset to any organization. To be able to use data and analytics to take the next step towards company success. Organizations need not rely on assumptions and guesswork to make impactful changes. This obsession over the accuracy of decision-making will also drive the HR business partners to take well-thought-out actions, leaving less room for mistakes. 
  • Figuring out the reason behind a high employee turnover rate is the most beneficial aspect of HR duties. HR Analytic tools help identify lapses in data and improve the rate of employee retention. 
  • Improved employee engagement comes with a bonus of reduced absenteeism. It is an educated parallel to draw between high absenteeism and low levels of employee engagement. Using AI tools, companies get better insights into employee behavior and help create an environment that boosts employee engagement and performance. 
  • Unbiased and well-analyzed recruitment processes. HRs can identify the merits required to promote growth in an organization and whether or not the potential candidate’s profiles match up to the standards of the existing employees.
  • Artificial intelligence algorithms and predictive analysis tools are trained to look for and display trends that prepare the organization to stay on its desired route while making informed decisions for all its future needs. - thus improving the overall performance and experience for both the individual and the company.

Disadvantages, on the other hand, begin with data privacy and end with poorly trained models.

  • Data privacy is not a challenge due to strict laws in most countries, but it is also difficult to acquire data on a large scale for every company employee. 
  • Due to the size of the data collected, security breaches are a constant concern. It is safe to assume that cybercriminals will target employee health data more than credit card information due to the higher importance of the former over the latter.
  • High acquisition and maintenance costs, more so for smaller businesses, are uncalculated costs.
  • Let us not ignore the fact that while AI tools help recognize potential candidates based on their resumes and keywords, it is painfully obvious that these decisions are biased against women and minorities. Due to the lack of historical data available on them, they are less likely to be chosen by the algorithm and, in turn, by the company. 

Predictive HR Analytics

The simple task of collecting data, statistical algorithms, and machine learning techniques in order to forecast the potential outcomes is called Predictive analysis. The endgame is to train models to see beyond the current hour and provide a well-calculated assessment of what the future might hold. 

Predictive analysis has played a major role in data science and is only now extending its hands into the HR department. It is introduced to the growing volume and types of data and the potential recognized in analyzing this data to create impact. It is a multi-step process that first uses predictive algorithms on historical data to create a model, which is then sent the new data in order to make predictions using forecast algorithms.

This kind of analysis is important in helping organizations detect fraud. HR analytic tools that are trained for pattern detection help prevent cyber criminal activities. Constant monitoring of all actions across networks can spot real-time abnormalities and zero in on fraud or advanced persistent threats. It helps optimize marketing campaigns by determining customer responses or purchases. It also pushes cross-sell opportunities to help companies attract, retain, and grow their most profitable customer base. 

Predictive analysis also improves operations in companies that forecast inventory and manage resources by using predictive models. For instance, Airlines and other booking-based businesses use these models to set ticket prices, maximize occupancy to increase revenue. It is a smart way to function efficiently. One of the crucial ways to use predictive analytics is to help reduce risks - and those risks could be due to hiring the wrong employee or working with clients with low trustworthiness. 

The Algorithm Behind HR Analytics

The most used algorithm in HR Analytics is in the hiring process. This is a key duty to oversee the hiring and onboarding of new employees. Human managers are more likely to pick employees who have similar likes and dislikes as the person and may most likely also add other biases towards the potential candidates. It is a no-brainer that algorithms go past the bias on a human level. There are discrepancies in the decision-making process due to the historical data used to train the model, but the algorithm itself is unbiased. 

Workforce planning is the next HR duty that is now assisted by AI algorithms. Predictive algorithms find patterns in data and allow people to predict future trends more accurately. These algorithms play a role in identifying successful employees and help influence their retention. This, in turn, helps the organization’s turnover rate, answering crucial questions like who is at risk of quitting the company, what reasons they have to leave, and, finally, what can be done to retain these employees.

Algorithms ultimately provide an insight into the working of an organization by collecting and analyzing data as per the requirement. The algorithms themselves are highly customizable to attain specific goals, which means that if an organization would like to retain the average scoring employees instead of the cream of the crop, then they are free to do so. The algorithm only dictates what is pre-dictated by the HR analytics and HR business partners.

How HR Analytics Affects Modern Business Processes

HR analytics of today optimize almost every aspect of HR duties. Lowering the employee turnover rate will show long-term financial benefits. Now that analysis can be done on all types of data, the organization has multiple ways of acquiring small-scale data in order to turn it into something insightful. Better quality hires always yield a better long-term return on investment rates. The transformation of HR as a strategic partner benefits the company by improving employee engagement, thereby reducing absenteeism. 

All in all, it pushes organizations into the future, turning over to an era of digital HR.

What HR Analytics Tools Do You Need?

There are many HR analytics tools in the market to choose from, but which ones are the most essential? In this section, we aim to give you our top six recommendations in order to make your life easier:

1. Excel

Microsoft Excel should be the most basic tool in your kit, and the chances are good that you already use it for a thing or two. Getting a handle on Excel functions will allow you to manipulate a lot of data very efficiently and help you summarise your findings with ease.

2. Python

Python is an easy-to-learn programming language that is very handy for data analysis. Jupyter, Spyder, and PyCharm are some of the most popular IDEs related to Python that you will need to know in order to fully utilize the potential of this powerful programming language. Python is incredibly adaptable and robust, and you will find many uses for it outside the bounds of HR.

3. R

R programming language is built towards handling larger datasets with ease. Even if the dataset comes with a million rows and columns, R makes it easy to visualize it all. RStudio is the most popular IDE for this programming language. This is a powerful visualization tool to have in your back pocket for whatever reason.

4. Tableau

Tableau is primarily a tool used for aggregating and collating data from numerous sources. It then takes this data and visually represents it in a huge variety of charts and graphs. This makes it popular because anyone can take away the most important points from just glancing at the visualization. 

5. PowerBI

Like Tableau, PowerBI is also a data collection and representation tool. It can collect data from very diverse sources, ranging from Excel files and SQL databases to a whole Twitter feed. It then converts the data thus collected into an HR dashboard for reporting and visualization. Being backed by Microsoft makes it one of the most popular options for this role.

6. Visier

Visier is a workforce analytics software. Feed it data, and it will give you insight into your workforce that you can then use as per your needs. Its internal algorithms predict when someone will leave, move departments, etc. As an HR manager, information about who is motivated and who isn’t should be invaluable to you.

There were our top six picks for tools you must have. There are others, of course. SPSS, Oracle, etc., are all very important tools to have and master, but if you need the best, these are the ones you should consider getting your hands on first.

The Optimal Way to Get Started With HR Analytics

Getting into HR Analytics is like starting any new subject - you have to learn the basics and then go up the levels until you master it. The HR analytics learning process is a five-step program that goes something like this: 

  • It begins with creating a collective mindset in order to understand the organization’s business goals. Before the HR department can work on training their models, they need to finalize their model goals that align with that of the company. Next comes training the HR department members to prepare them for the data-related workload and to use the AI tools. 
  • The department then brings in the data scientists to identify and analyze the metrics to achieve company goals. Even though the HR department has evolved to learn and use data strategy, analysis, and communication, the data scientists are more experienced in looking for patterns and narrowing down on the metrics. They tend to serve as mentors to the HR colleagues to help understand and apply the insights.
  • Start the department off on small tasks like collecting and analyzing the relevant data in order to present them to the HR business partners and discuss the forthcoming steps. When completed quickly and successfully, small projects are called ‘’quick wins,” and these are projects that show significant, high-impact results in a short amount of time.
  • Obtaining insights into the analysis will help us narrow down on the right HR analytics solution. Depending on the requirements, the solution should: be cloud-based, possess statistical analysis and machine learning technology, answer key business questions, be easy to use, be powered with visualization tools, and be subscription-based.
  • The final step in the process is to communicate the impact this information and analysis will have on the organization. Visual tools come in handy when trying to explain the consequences of applying said analysis. It helps decide which way to go forth and which pitfalls to avoid.

If you are truly interested in mastering analytics for HR, then the People Analytics Certification Program from AIHR is a good place to start. To be honest, enrolling in any course that takes you step-by-step through the world of HR analytics is a good choice, but this one offers three invaluable core courses:

1. HR Analytics Leader Course. If you want to lead an analytics department yourself, then this one's for you.

2. HR Data Analyst Course. If you fancy yourself more of a data analyst and like working with software like Excel, PowerBI, and Jupyter, then this is a must.

3. Strategic HR Metrics Course. Analytics is built upon metrics. If you want to understand how everything meshes together and how analytics function, then you should do this course.

Analytics will make your HR data more interesting, and seeing the patterns will allow you to make better decisions regarding your workforce. You will get a better understanding of who you should invest in more. That in itself is worthy of investment, but how much should you invest?

How Much Should You Invest in HR Analytics Solutions?

Time is the greatest resource in our world right now. Every bit of technology we have today is aimed at giving us more time. HR analytics is no different. Data analysis and visualization have gotten so much easier and simpler with the advent of new technology. It takes a fraction of the time it used to take a few years ago to collate and report a large data set.

And this will only get better and better. More accurate, too. Predictive analysis on the part of HR analytics is an actual thing. It is proven to work, and many companies are using this to tap into trends and improve their bottom line. Better HR analytics helps out companies in various ways - better recruiting, better strategies for improving engagement and satisfaction among workers, better human capital management as a whole - and this is just the tip of the iceberg.

Can you put a price on that? In an ideal world, every company would be investing everything they had in better HR analytics, but not every company can hope to do so. So, depending on your budget, you have a few choices to make. Chief among which is how much should invest in HR analytics solutions?

There is no clear answer to this. It is true that the more you invest, the more profitable your returns will be. It is also true that predictive HR analytics takes a while to get going. You have to consider whether you are willing and able to cover for it until then. There are ways of getting around this with minimal expense - the combination of Excel and PowerBI is a potent one and has been used successfully around the world.

So, what should you do? There is no answer. Take into account your budget, the size of your company, the needs you need to fulfill, and how much you truly need to incorporate analytics. Once you have the answers to those questions, look for solutions that will cover your bases. That is the best and most efficient way of knowing just how much you should invest in HR analytics solutions.

Start a Journey With Lanteria

So, now that you know everything there is to know about HR analytics solutions, we come to the end of our journey. We hope to have given you a glimpse into the future of HR. The key takeaway is that mastering the inner workings of analytics will likely improve your chances of getting the best output out of your employees and your company as a whole.

However, we also understand that incorporating and mastering something new and fairly advanced as HR analytics can be a tall order. It is hard work, without a doubt, and we are here to offer a helping hand. Lanteria has years of experience in the field of HR analytics. We have helped many companies configure and set up their own HR system and are confident in our ability to extend aid to you as well!

So, if you need help, get it from the best in the business. Contact us today for a free demo!

Similar posts

Similar posts

Similar posts

Similar posts

Similar posts

Similar posts