The Types of HR Analytics and How They Accelerate Business Growth.

The process of HR analytics can be broken down into three main components: data, analysis, and reporting.

Data (Metrics)

You’ll need to start by identifying useful data points, or metrics. The data needs to be quantifiable because numbers are easier to compare on a large scale.

Accordingly, qualitative variables like employee satisfaction are easier to analyze with quantitative data points like numerical surveys and Key Performance Indicators (KPIs) like turnover. KPIs reflect priority issues for your industry, like revenue and productivity.

HR software makes it easier to collect and process data automatically, from absenteeism to the relative popularity of different employee benefits.

Analysis (Analytics)

Collecting numbers is only the beginning. You’ll also need to compare recent and long-term data for context. Was the company this short-staffed last month? Are seasonal patterns (like holiday vacations) skewing short-term data?

Some KPIs need to be calculated from multiple data points, like “time to hire” and “offer acceptance rate.” Analysis provides context for a fuller picture of circumstances and the overall health of the business.

Reporting (Results)

Reports may be the starting point as well as the end product of HR analytics. Your raw data (metrics) may be collected from a variety of automatic reports.

You already have recordkeeping systems for tracking weekly employee hours, productivity, and absenteeism. Processing and analyzing that data highlights meaningful trends and answers questions about whether current policies need changes.

Reporting highlights the key findings of data analysis, making it easy to see and understand results.

Collecting Data

In reality, you can and should collect data from every HR process your company run. The easiest way to collect data for HR analytics is to make sure you are using single-source HR software. Each stage of the employee lifecycle (and ongoing productivity) can be managed and measured with the right software.

Recruiting

How long does it take you to fill a posted opening? How long does the application process take for new hires? Track your efficiency and success rate with recruiting software.

Onboarding

How many new hires complete 100% of the training process? Could you save with virtual onboarding? How many new hires stay longer than 1 year?

Performance Management

Exactly how much does performance vary between individual employees? Are absenteeism and tardiness affecting productivity? Are remote workers as efficient as on-site staff?

Learning Management

How many employees completed the required training on schedule? Quiz workers before and after training sessions to check effectiveness. Cross-train employees and close skill gaps with a Learning Management System.

Benefits Administration

Which benefits are most (and least) popular with employees? How many employees missed the deadline for open enrollment? Could emerging benefits or perks improve retention?

There are four main types of HR analytics that organizations can use to accelerate business growth.

1. Descriptive analytics is the most basic form of HR analytics. It involves analyzing historical data to understand what has happened in the past. It is known as decision analytics and uses statistical analysis techniques to explain or summarize a particular set of historical, raw data. It focuses on past data to account for what happened but doesn’t make predictions for the future.

How descriptive analytics works

Descriptive analytics can use a combination of numerical data and qualitative data. It involves performing mathematical calculations, such as central tendency, frequency, variation, ranking, range, deviation, etc. This allows HR to see patterns and inconsistencies to improve planning.

Descriptive analytics can help with:

• Assessing behaviour
• Comparing characteristics across time
• Spotting anomalies
• Identifying strengths and weaknesses

Descriptive analytics advantages

– The simplest form of data analysis.

– Requires only basic math skills, and it allows you to present complex data in an easy-to-digest format.

Descriptive analytics disadvantages

– Limited to a simple analysis of a few variables after the fact.

– For instance, an employee headcount summary captures a time period and reports the “what” but not the “why” or “how.”

Descriptive analytics examples

Efficiency metrics that HR has traditionally tracked fall under the descriptive analytics category. Here are two examples:

• PTO: Using descriptive analytics, HR can analyze the average number of paid time off days that employees use in one year.
• Turnover: Descriptive analytics could be used to analyze employee turnover rates to compare the annual turnover between two teams or two departments.

2. Diagnostic analytics goes beyond descriptive analytics to help organizations understand why something happened. It aims to determine the underlying reasons for what the data exposes. Although it is based on the same historical data as descriptive analytics, there is a key difference. Diagnostic analytics goes into the next step of summarizing what happened in understandable terms. It digs for the “why” behind the data’s trends, correlations, and anomalies.

Diagnostic analytics process

Conducting a diagnostic analysis typically involves the following steps:

1. Identifying the patterns and anomalies within the data that raise questions and need to be studied further.
2. Discovering what factors could be contributing to the patterns and anomalies to identify the relevant data.
3. Determining causal connections by analyzing the data with various methods.

There are multiple diagnostic analytics techniques, including:

• Data drilling: Taking information from a more general overview and providing a more granular view of the data.
• Data mining: Extracting patterns from data to help predict future events
• Probability theory: Quantifying uncertain measures of random events
• Regression analysis: Determining which variables will impact an outcome
• Correlation analysis: Tests the relationships between variables
• Statistical analysis: Collecting and interpreting data to determine underlying patterns

What is the purpose of diagnostic analytics?

Diagnostic analytics is used to transform data into worthwhile insights. It identifies patterns, variances, and causal relationships while also considering internal and external factors that could be influencing them. This helps HR see the big picture of a situation and zero in on which factors have the potential to create problems. Then you can focus your efforts in the right place to mitigate them.

Diagnostic analytics advantage

– Shows a more comprehensive interpretation of the data for informed decision-making.

Diagnostic analytics disadvantages

– Focuses on past occurrences which makes it very reactive.

– Can’t provide actionable insights to support your planning process.

Diagnostic analytics use case

Let’s look at an example of diagnostic analytics put into action with HR:

Diagnostic analytics can be used to improve your employees’ engagement and your company culture. Digging into the data from internal surveys and exit interviews should uncover the areas that make employees feel connected and satisfied in their work and those that don’t. Since highly engaged employees tend to be the most productive, linking engagement scores to performance measures will show the impact. A 2019 report noted how shoe retailer Clarks discovered that for every 1% improvement in employee engagement, there was a 0.4% increase in the company’s performance.

3. Predictive analytics uses statistical models and machine learning algorithms to predict future workforce trends. This type of analytics can be used to forecast future workforce needs, identify high-performing employees, and predict which employees are at risk of leaving the organization. Predictive analytics can help organizations make more informed decisions about their workforce and develop strategies to address potential issues before they occur. The model’s accuracy is evaluated by applying it to new data.

Predictive HR analytics support better HR decisions. It translates historical data gathered from areas, such as job skills, employee engagement, productivity, resumes, etc. into forecasts about what to expect in the future. These predictions furnish HR leaders with information that will improve decision-making in areas such as hiring the right candidates, bridging the skills gap, and retaining top talent.

Predictive analytics advantage

– It can reduce human error, help you avoid risks, improve operational efficiencies, and refine the forecasting for your organization.

Predictive analytics disadvantages

– It requires substantial and relevant data (big data sets).

– It’s also challenging to ensure that all of the variables are considered, and the model must be updated as data changes.

Predictive analytics is a valuable tool in many HR functions. Here is a predictive analytics use case on recruitment:

Recruitment: Predictive analytics can analyze data from the hiring process (resumes, job descriptions, etc.) to narrow in on the desired skill sets. Certain elements of social media profiles and answers to automated application questions can also reveal the key attributes that indicate a candidate is a right fit for long-term success with the organization. Then you can tailor your recruitment strategy to attract and engage this type of applicant.

Furthermore, you can implement predictive analytics to estimate what your future demand for certain roles will be. This allows you to start recruiting at the appropriate time and target suitable candidates.

4. Prescriptive analytics takes predictive analytics one step further by providing recommendations for actions that organizations can take to achieve their desired outcomes. It is the final and most complex stage of the analytics journey. It offers options for where and how to act to achieve success. Because of its complexity, prescriptive analytics is also known as the ‘final frontier of analytic capabilities’.

Prescriptive analytics relies on big data and uses an assortment of technical tools, including:

• Machine learning
• Algorithms
• Artificial intelligence
• Pattern recognition

How prescriptive analytics works

You can think of prescriptive analytics like Netflix for business. It works in the same way that Netflix suggests films based on viewing behaviours. Prescriptive analytics goes beyond predictive analytics with a more pre-emptive approach to looking at the future.

Predictive analytics simply predicts a decision or action’s most likely outcomes. With prescriptive analytics, you can forecast what will happen next, why, and what you can do next. It anticipates the most likely scenarios and which interventions have the potential to bring optimal results.

Prescriptive analytics advantage

– Equips HR leaders to make informed, real-time decisions to improve performance, solve complicated problems, and take advantage of opportunities.

Prescriptive analytics disadvantages

– An iterative process that requires time. Also, the quality of recommendations is dependent on the quality of the data, so it won’t be effective if your data is incomplete or unreliable.

– You must also be careful about weighing the options presented and ensure that taking the recommended action is reasonable from an HR perspective.

– Algorithms can’t always reflect the diverse intricacies of dealing with human beings.

Prescriptive analytics example

Here is a prescriptive analytics use case related to HR.

Attrition: As mentioned above, Experian is using AI to predict high-flight-risk employees. However, they are also taking the next step with prescriptive analytics to prevent the contributing factors to flight risk from happening. According to Experian HR executive Olly Britnell, “We’re using machine learning to track interventions such as changing the team structure offering more training, and then tracking which ones are having an impact.”

Conclusion

A study by McKinsey & Company found that organizations that use HR analytics to inform their workforce decisions are more likely to achieve their business goals. According to the study, these organizations are three times more likely to outperform their peers in revenue growth and twice as likely to outperform their peers in profit growth. This demonstrates the value of HR analytics in promoting organizational growth.

Therefore, HR analytics is a powerful tool that can help organizations make data-driven decisions about their workforce. By using descriptive, diagnostic, predictive, and prescriptive analytics, organizations can gain insights into workforce trends, identify areas for improvement, and develop strategies to achieve their business goals.

References:

1. McKinsey & Company. (2017). People Analytics: Recalculating the Route. Retrieved from https://www.mckinsey.com/business-functions/organization/our-insights/people-analytics-recalculating-the-route
2. Deloitte. (2019). 2019 Global Human Capital Trends. Retrieved from https://www2.deloitte.com/content/dam/insights/us/articles/2019-global-human-capital-trends/DI_HC-2019_Global-Human-Capital-Trends.pdf