Change Management Christopher SmithFebruary 14, 2022

HR analytics examples: The technology that will change workplaces

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HR analytics examples: The technology that will change workplaces

HR analytics have the power to change workplaces across the world. Using the power of data to make good business decisions can have benefits that include improved productivity, increased revenue, and satisfied employees who are happier, more engaged and more likely to stay with the organization. 

At its best, leveraging HR analytics becomes integral to an organization’s strategic planning. It should not be limited to the remit of the HR department. Instead, all parts of the organization should be involved in HR analytics processes, such as the gathering of data, data-aggregation and analysis. Siloing HR analytics within the HR department is not effective for proper workforce planning or optimization in a data-driven business. To fully engage all departments in an organization, HR analytics need to embrace the technology that will drive success.

What should organizations analyze as part of the HR analytics process?

It is important to determine exactly what your organization should be measuring when it embarks on a program of data collection and analysis. It is important for organizations to determine what data should be tracked and measured so they can make the best possible decisions. The metrics will vary between organizations, so it is important to look carefully at what information needs to be gathered for optimal outcomes.  

HR analytics examples include the following 10 metrics and associated data:

  • Staff demographics: It might seem obvious, but knowing about the demographics of your employees can be an easy way to obtain important insights. Workforces with an average older or younger age present different HR challenges, such as older workers leaving staffing gaps when they retire, losing the experience and knowledge of retiring workers, and younger workers being statistically more likely to stay for shorter periods in jobs. These insights can then help with strategic HR processes, including succession planning and staff retention and employee training programs.
  • Staff turnover: As well as the raw numbers, such as churn rate, it is useful to examine how long employees stay with the organization before moving on, the ratio of resignations to terminations, and data from exit interviews to determine whether there are any common reasons for leaving. This data can be used to look at ways to improve staff retention and employee satisfaction. 
  • Absenteeism: Again, raw numbers, such as number of days off taken per employee, are useful here, but more insights will be gained if organizations drill down into the reasons for absences. While some absences are beyond the control of employees or employers, such as bereavement leave, sick leave data can be an important warning signal if stress-related conditions are prevalent. Being aware of these sort of emerging trends is useful for implementing proactive strategies to reduce employee stress and manage workloads better.
  • Time taken to recruit staff: Examining data on how long the recruitment process takes from the time a vacancy comes up, through to the advertising, interviewing, and final hiring decisions, can be very instructive. If time-consuming pinch points are identified, employers are then able to examine ways to streamline the recruitment process, including introducing technology to help sort through job applications more efficiently.  
  • Cost of recruitment: Recruitment can be expensive as well as time-consuming. Data on where an organization is spending its recruitment budget is useful, especially if the HR team has been tasked with identifying cost savings. For example, such metrics can help determine if the organization is using the most cost-effective ways to advertise vacancies. If using an expensive advertising platform is not attracting the right candidates, that could be an easy way to save money and look for alternative ways to share targeted recruitment messages. 
  • Revenue generated per employee: This metric can be useful, although it needs to be handled with care and fairness. Most organizations will have a variety of positions, some of which are directly responsible for generating revenue, such as sales and marketing teams, while other roles are important for the company to function, but not necessarily directly responsible for generating revenue, such as certain administrative roles. However, if this figure is calculated as a company’s total revenue divided by the number of employees, it can create a general measure of how much money every team member creates for the organization.
  • Organizational performance: The performance of an organization can be measured by a wide combination of metrics, but specifically this is about measuring employee productivity. Such measurements can be taken in a number of ways, such as analyzing time sheets and work logs, as well as equations such as dividing revenue by labor hours.
  • Employee engagement: This metric goes beyond mere happiness at work. It is about ensuring employees are proactively invested and involved in playing their part in the company’s success. Properly engaged employees are more likely to stay with a company for longer, so it is a good way to detect trends that may affect staff retention. 
  • External labor market data: There are many external metrics that can be used to help optimize a workforce and improve strategic planning. Data about imigration patterns and demographics in the local area or the regions in which a company operates can help predict and prevent labor shortages, for example.
  • Effectiveness of technology: Measuring the effectiveness of technologies is important and a regular audit can help ensure an organization is using the best available solutions for its needs. This includes HR technologies. A report to senior management on the effectiveness of existing technologies and areas for improvement can form an important part of a business case for new or upgraded solutions. Digital adoption should be a positive journey for an organization rather than a chore.

Once an organization has determined which metrics are important for success, a clear business case can be made for investing in HR analytics technologies.

Decoding the HR analytics technologies and terminologies

The array of technologies for HR analytics can be overwhelming, so it is important to understand the main solutions and what they can do for organizations. HR analytics examples need to be explained to senior management along with the technology that can be used to optimize workforces and help achieve organizational goals. 

When obtaining senior management buy-in by making a solid business case for technological investment, being able to coherently explain the solutions and the terminologies will add credibility to your presentation. Presentations that focus on technology can easily slip into jargon, so being able to explain everything clearly will bolster the business case. 

Here are some key terminologies that may come up when presenting a business case for investing in HR analytics technology: 

Artificial intelligence (AI): AI is no longer the domain of science fiction. It is well and truly here to stay and is becoming an increasingly important technology for so many organizations across the world. Put simply, it is the creation of intelligent machines and computer programs. It incorporates some of the other functionalities covered in this list, such as machine learning and predictive analytics. Patterns and trends in data can be identified and logic applied to solve problems via AI technologies. This can be an enormous time-saver when analyzing detailed HR data. 

Machine learning: Machine learning is becoming better understood and, as a result, more popular with workplaces. Like AI, it can seem daunting at first, but if organizations are prepared to trust and embrace the technology, it can become a vital part of any HR analytics system. With machine learning, algorithms build models based on information that has been entered into the system. The applications of machine learning are broad, from filtering spam emails and text analysis, right through to detailed data analysis, developing models that adapt and evolve as more data is entered, and predictive analytics.

The cloud: The cloud has become almost ubiquitous as it plays a large role in everyday life – everything from workplace data to personal photos can now be stored on the cloud. Cloud computing uses remote servers to complete IT tasks, often via a pay-per-usage scheme so services can be scaled up or down depending on business requirements. Cloud-based systems can be an easy business case sell because they are generally affordable, easy to use, and facilitate remote working. With more companies either keeping employees working from home or embracing hybrid working since the Covid-19 pandemic, this is definitely a major benefit.

Streaming analytics: This is when data is processed and analyzed continuously rather than in batches. This is especially useful when quick but smart decisions need to be made in a fast-paced work environment, as insights can be revealed in real time, rather than waiting for batches of data to be gathered, aggregated and analyzed. This can be a good way to pick up emerging trends – if a trend has the potential to cause problems, such as a drop in productivity, action can be taken quickly. And if a trend points to a positive change, such as a potential upswing in revenue, a company can be ready for this growth and deploy staff accordingly.

Predictive and prescriptive analytics: Solutions that offer HR departments predictive and prescriptive analytics take data analysis beyond the collection of simple descriptive data, such as staff turnover figures. As the names suggest, predictive analytics tools analyze the data to make predictions, and prescriptive analytics tools go a step further and the technology can make suggestions for organizational improvement. Of course, there will always be a role for human decision-making, but technology can streamline the process by revealing insights that otherwise might have been missed.

Data virtualization: These systems remove data bottlenecks, make the data consistent, and provide all relevant information on demand for easy access. Improved access to data saves time and helps speed up decision-making processes.

Programming languages: it is smart to familiarize yourself with programming languages, such as  Python and R, when investigating HR technologies. Learning programming languages can be a valuable skill for HR professionals as well as the IT team. Python and R are the two main programming languages that are exciting the data science community. They can be used interchangeably. Python is generally considered to be easier to learn although it has slightly fewer functionalities. These languages can help HR professionals with functions such as statistical analysis and creating data visualizations.

Spreadsheets: This might seem like a back-to-basics – or even a retrograde – technology, but spreadsheets have remained part of the workplace IT arsenal because they are usually an effective way to collate data. The difference is that spreadsheet technology has come a long way and it is worth investigating the latest options. Powerful tools that are part of many spreadsheet software programs include increased automation for creating more meaningful data, real time analysis, and long-term trend analysis. Improved security is a common feature of many new spreadsheet programs, which is important when handling often sensitive data.

In conclusion, we are in an era where knowledge is power. For forward-thinking organizations, one of the greatest knowledge-based powers is best practice data gathering and analysis for smart decision-making. HR analytics are changing the way HR departments operate on a truly global scale. When large quantities of data can be utilized in ways that were either impossible or extremely difficult before today’s technologies were fully developed, the potential is enormous. 

When the solutions can save time and money by helping HR departments and senior managers make decisions, sometimes before problems fully emerge, the return on investment becomes obvious and rapid. And it is not just about time and money – when these technologies can help ensure employees are satisfied and fully engaged in the success of the organization, company goals will be met and exceeded. 

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