As research keeps proving that companies that implement data-driven decision-making are more efficient and profitable than their competitors who do not leverage data, it is essential to understand what workforce analytics is, its benefits, and its importance.
In this article, you will discover what workforce analytics is, why it’s important, how to implement it, and more.
What is workforce analytics?
Workforce analytics refers to the process of collecting workforce-related data types, both from internal and external sources, analyzing them, and turning these insights into actionable information.
It uses data and statistical models to analyze employee-related data and employee performance. As opposed to people analytics, workforce analytics takes a broader perspective on work in general instead of focusing solely on the people.
Workforce analytics refers to the process of collecting workforce-related data types, both from internal and external sources, analyzing them, and turning these insights into actionable information.
What is the purpose of workforce analytics
As briefly mentioned above, workforce analytics aims to aid macro-level planning to improve business operations and performance. Apart from these benefits, related to saving time and expenses, the importance of workforce insights also refers to intangible aspects, including creating a cohesive team and improvements in the corporate culture.
For instance, gathering employee sentiment information through surveys or questionnaires allows recognizing how satisfied employees are under current working conditions. If a certain field is lacking, you can aim to improve it. If a certain field is thriving, you can use it for recruitment purposes.
This encourages better business performance since happier employees are more motivated and efficient. This also helps managers identify what attracts an efficient workforce to craft suitable strategies to motivate employees, enhance job satisfaction, and more.
Benefits of workforce analytics:
- Decreasing retention rate by identifying turnover triggers, such as lack of training, lack of promotion opportunities, and more.
- Learning which recruitment strategies are efficient.
- Finding areas where labor costs can be decreased by implementing more efficient processes.
- Predicting the required future workforce by having an insight into the turnover rate.
- Optimizing and improving employee experience and satisfaction.
- Enhancing the overall business performance through HR-related actions, such as identifying and retaining top-performing talent.
What are the types of workforce data?
Workforce data encompasses 4 types of data that help organizations understand and optimize their workforce: descriptive, predictive, prescriptive, and diagnostic.
Descriptive workforce data analysis
Descriptive data refers to information that provides the current state of a workforce. It includes details like employee demographics (age, gender, education), job roles, department sizes, and performance metrics.
Descriptive data helps organizations understand who their employees are and how they are performing, offering a baseline for more complex analyses like predictive and prescriptive analytics.
It is often the first step in workforce analytics, providing the foundational insights needed to assess workforce trends and inform decision-making.
Predictive workforce analytics
One of the main types of workforce analytics is predictive workforce analytics. This refers to conducting workforce analytics to make predictions so you can forecast using historical information. The main statistical models used in this type of workforce analytics are machine learning and data mining.
Predictive workforce data analysis is used by numerous businesses to improve employee experience and optimize their processes. It is often conducted via regression analysis, pattern matching, multivariate statistics, among other methods that help firms understand what is likely to happen in the future, both in terms of risks and talent outcomes.
Prescriptive and diagnostic workforce analytics
Apart from predictive analytics, business leaders also employ prescriptive analytics and diagnostic analytics.
The first one uses the same data as predictive workforce analytics, namely historical data. The difference is that prescriptive analytics is used to plan the best course of action after you conduct predictive workforce analytics to make forecasts.
In other words, it is seen as the next step to find ways of mitigating challenges and addressing workforce issues. The main purpose, as a result, is to ensure that you meet your desired objectives.
Diagnostic analytics is a past-looking method of identifying the causes of your workforce failure or success. This is also extremely important for businesses because it can show you clear workforce trends, so you can then leverage this information to optimize your business performance.
How to implement workforce analytics?
Workforce analytics uses historical data collected either by your organization or externally to improve financial performance and optimize operations. There are different software solutions and statistical tools that allow measuring, characterizing, and organizing employee data, but the basic steps of the process are similar:
- Identify a problem or question you want to address.
- According to the problem or question, decide what data would help you address it.
- Build a workforce analysis dashboard.
- Find a relevant objective or benchmark to compare your results.
- Decide on the right analysis method and report your results.
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Examples
Let’s assume that you are a business manager who wants to know if highly trained employees exhibit improved employee engagement.
The next step would be to identify what information you need to measure this; in this case, you should get access to the relationship between your two variables, training completion data and employee survey results.
Comparing your results with benchmarks (other historical results or the completion of training courses), you need to collect and analyze the employee data from all of your company’s departments.
One of the main data analysis methods, in this case, would be a correlation analysis between employee satisfaction and the training completion rate. This would help you identify whether highly trained employees are more satisfied compared to employees who did not do or successfully finish their training sessions.
Another example would be to identify potential red flags regarding employee turnover. Let’s assume you can analyze whether social engagement, promotion history, salary, and job role affect employee turnover. Using an algorithm, you would be able to know which one of these factors you need to address to enhance the employee retention rate.
Workforce analytics vs HR analytics
Professional workforce analytics is more evolved than HR analytics software. It has a larger scope, so you can use it to improve your team at an individual level, collectively, and even boost the entire firm’s productivity and operational efficiency.
One of the primary purposes of leveraging workforce analysis is to improve the retention rate of top-performing employees.
Workforce analysis is linked to other HR analytics areas, such as performance management. However, the main difference between different types of HR analytics and workforce data analytics is the macro focus of the latter one.
In other words, workforce data analytics consists of firm-wide insights into the workforce and has a larger scope than HR analytics.
Workforce analytics tools and software
The concept of workforce data analysis evolving is not a surprise since there are numerous software solutions that use employee-related information to allow HR professionals to improve their operations.
Some of the most popular ones are Visier People, Anaplan, and ActivTrak.
Workforce data analysis tools enable analyzing data using statistical methods, turning it into actionable insights. These tools and software are used to improve your hiring abilities, retention rate, optimize the workforce, and more.
Workforce analytics software consists of machine learning and artificial intelligence that helps you generate rich, predictive insights for improvement and optimization.
There are numerous software solutions on the market, but most of them have several basic functions:
- Automated data collection and analysis;
- Predictive analysis results (for macro-level planning);
- Benchmarking for efficient and accurate comparison;
- Reporting and data management.
Applying workforce analytics: use cases
Workforce analytics data can be applied to three major categories: recruitment, retention, and talent management.
Recruitment
In terms of recruitment, workforce analytics tools allow you to:
- Scan public resumes automatically;
- Perform background and social media checks;
- Shortlist potential candidates;
- Match potential candidates with current talent better;
- Decrease recruitment costs;
- Mitigate race and gender bias.
Retention
In terms of retention, workforce analysis helps with:
- Tracking compensation rates for underacknowledged overperforming employees;
- Observing behavioral patterns for productivity;
- Identifying behavioral patterns that show early signs of potential resignation.
Talent management
In terms of talent management, workforce analytics opens the door for:
- Setting checkpoints regarding performance to measure and support future potential;
- Uncovering training or skill shortage to offer proper learning opportunities.
Summary
All in all, business leaders now have access to abundant information. The large amounts of data make it difficult to collect and analyze, which is why numerous firms now use workforce insights projects to collect, organize, and extract actionable insights regarding their employees.
Data-driven decisions help to improve workforce management, make the business outcomes more efficient, and improve its performance in the long run.
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