What is Workforce Analytics?
In the past, Human Resources departments have had to make many decisions based on emotion or intuition. While many aspects of the field still rely on human decision-making skills, new technology has allowed companies to make decisions that are driven by data and facts. This is where the field of workforce analytics (also called human resource analytics and people analytics) comes into play.
In short, workforce analytics is a set of tools and practices that uses data and statistical analysis to provide valuable insights for decisions made by human resources. This happens on both an individual and company-wide level.
It starts by accurately gathering and reporting on a large amount of important data and performance metrics of an organization. Human resource analysts then work to answer big questions and take action to improve certain aspects of a company’s well-being.
There are a number of ways that different companies think about and implement workforce analytics. However, some aspects of data collecting, reporting, and analyzing remain consistent, and the possibilities are still being discovered.
Why Use Workforce Analytics?
While analytics have been present in other departments such as finance and accounting, human resource departments have traditionally fallen behind. However, many leading HR professionals are now starting to integrate these same principles into managing people.
Workforce analytics is about informing strategy for human capital management through data-driven decisions. Using analytics helps identify and visualize patterns and truths in ways that would otherwise be impossible without certain insights. In the same way, we can now eliminate a lot of bias and human error in an increasingly diverse workforce.
These tools and practices also help track a company’s ROI. Each input will have direct and measurable outcomes for the company budget, and understanding this relationship can help gauge whether or not a decision or process is profitable for the company overall. Workforce analytics also illustrates the direct impact of human resources on the success of a business.
Types of Analytics
In essence, there are four categories of analytics that can be used to understand a problem and make an informed decision. Different HCM software and data management systems will have different capabilities when it comes to reporting these.
Starting at the most basic level, many companies already report descriptive and diagnostic analytics. However, the overall goal of analytics is to remove guesswork by reporting predictive and prescriptive figures for taking action. Each type plays an important role.
Descriptive analytics seeks to describe the current state or historical statistics of the company’s performance. Metrics like employee turnover rate, engagement levels, department-wide performance or attendance are all examples of descriptive analytics. These figures describe what is happening or has happened.
This type of report provides numbers and insights on the current scope of the problem without trying to understand causality or suggested actions. In other words, these are just the facts. This is usually the first type of insight you will want to use when solving a problem.
Diagnostic analytics goes a step further by identifying the cause(s) of descriptive analytics. By looking at the factors that influence certain trends, this type of report seeks to gain insight into why something is happening.
If a company has a high turnover rate (descriptive analytics), often the next step is to diagnose the underlying issue that is making employees leave. This helps HR professionals better understand the problem at hand and informs a possible solution.
Predictive analytics is about making projections for the future based on existing data and insight. Often influenced by current and historical data, predictive analytics helps visualize the future needs or performance of a company in a given area. This type of report tries to predict what will happen in the future. Scenario modeling and forecasting workforce needs are both forms of predictive analytics.
Prescriptive analytics provide suggested actions for the future based on predictive analytics, historical data, and desired outcomes. In other words, it helps answer how certain outcomes can be achieved or influenced.
By using data and insight gathered as a result of certain actions and outcomes, prescriptive analytics tries to provide the best solution to improve on a problem. For this reason, it is perhaps the most intelligent, comprehensive, and valuable form of insight HR professionals can utilize.
The Analytics Process - How to Do It
The best workforce analytics practices are driven by new technology and software that assists with gathering, reporting, and analyzing data. While there are a lot of programs out there that assist with HCM and other metrics with some incredible tools for visualization, not many of them provide all of the insight necessary. For this reason, you will often need to utilize multiple programs to answer all aspects of a given question.
Even if you decide to outsource data processing to another company, you will likely still need to have someone in-house to use and interpret the data to make decisions based on company objectives.
That said, we have derived seven key steps to using workforce analytics to make important human resource decisions. From data collecting to taking action, it starts with asking the right questions.
1. Ask the Right Questions
Before you can know how to improve the performance of your workforce, you will likely be acquainted with a problem at hand. The ultimate question at first is, “How do you fix it?”
You will want to start thinking of this problem in the form of a quantifiable question. In other words, the answer to the question should be measurable by facts and data.
Do not start by asking how you can improve upon or fix the problem just yet. The first stage of asking questions should be about understanding the problem at hand with a very general idea of what you hope to accomplish. You may need to think of this in the form of multiple questions to cover every aspect of your problem.
For example, you might ask, “What drives employee engagement at our company?” or “What factors indicate that an employee is about to leave the company?” or “Which aspects of a potential candidate could indicate retention later on?”
2. Collect the Right Data
Often the best insight is derived by understanding a problem from multiple angles. In many cases, you might already be overwhelmed with the amount of data reports available. However, it’s important to know that not all of it matters with regard to the question at hand. Because of this, you should at least try to decide which areas are most important to understanding your problem.
Still, you will want to keep an open mind to what different types of data can tell you. You might need to collect both internal and external data to truly diagnose the problem.
For example: If you are asking about what causes turnover rate, you may want to collect data such as hire rate, number of terminations over time, employee engagement surveys, certain performance metrics, payroll, and external market data. However, you might not need to collect data about customer satisfaction or LinkedIn profiles.
3. Clean the Data
Before analyzing the data and running reports, it’s important to clean that data first. Be sure the data is error-free and up to date. Account for missing numbers and remove any outliers or duplicate figures that can skew results.
4. Analyze the Data
Once the data is cleaned, you will need to use the tools and software at your disposal to create charts and visualize the data for important insights. This means identifying trends and patterns, as well as drawing conclusions by plotting the numbers in different forms. Create reports and models that tell a story. Use this data to make projections for the future and analyze risks and anticipated outcomes.
You will also want to understand what factors in your analysis are influencing one another and how. In this stage, it is important to note that correlation does not always indicate causation. Use best practices when analyzing results and consider hiring a professional who is experienced with statistics to help you to correctly interpret the data.
5. Understand and Form Goals
You have already posed the right questions to understand your problem, and you have a general idea of what you want to accomplish. However, your analysis will likely reveal insights that you did not previously expect. With the understanding you now have of the situation, ask your team what you want to do with the information. What problems need to be solved?
To do this, it’s important to set specific goals with deadlines and define success for those goals in a measurable way. This is where you decide on your strategy for improvement based on data analysis.
Consider the goals of the company as a whole and what you want to accomplish. While any organization has the potential to consistently improve, ask your team to define the priorities for this project or season.
6. Take Action
Using prescriptive analytics, decide what factors or variables can facilitate change. While not all things will be controllable, there are sure to be areas your company can influence which will help you reach the desired goals.
Calculate the financial impact of these decisions and make predictions for how it might look to implement your solution. In this stage, you are using the data to your advantage to make informed management decisions.
7. Re-Analyze and Adjust
Once you take action to achieve these goals, you will want to track the success and ROI data of your decisions to measure impact. If you are not meeting your goals, you will want to re-analyze the data and adjust your strategy to reach the desired state.
Insights will also change over time. As more data and new decisions are logged into your company history, inputs and outputs will affect predictions for the future and suggested actions.
Some Possible Insights
Depending on the company and the problem at hand, there are a variety of ways to analyze data and make informed strategic decisions. Since human resources involves everything from payroll to performance metrics, the jurisdiction of workforce analytics can be very wide. Many companies have discovered surprising insights and taken powerful actions based on analytics from a variety of different reports. Here are a few examples of things that workforce analytics can help companies understand.
In virtually every department of a company, people are constantly making moves based on financial impact alone. However, some necessary HR decisions are difficult to justify with a financial team because they don’t always have an obvious or direct impact on profit.
By combining insights and analyzing causal relationships, workforce analytics can illustrate the financial dimensions of things like employee engagement and team morale. By seeing the direct monetary figure attached to certain key metrics, HR professionals can help prioritize important decisions or next steps.
You may have an idea about the turnover rate for a particular position at your company. However, you may not know how high it truly is or why it is happening.
Descriptive analytics helps you understand the general demographics of your churning employees, their tenure, pay, engagement, and other trends that appear consistent. Diagnostic analytics works to assign causality. In some cases, companies have discovered that retention is not the problem, but rather their hiring criteria is the root source of the high turnover rate. You can then use prescriptive analytics to reevaluate your process.
You might also discover certain behaviors or performance data that indicate an employee is about to leave and prepare for this by hiring more talent or trying to keep them on longer.
Based on analytics from training data, interviews, and performance metrics from the top talent in an organization, Human Resource professionals can decide which characteristics to look for during the hiring process. Certain factors present during the first interview may indicate retention or higher performance later on.
You might also calculate the metrics of your current interview or application process and adjust for cost-effectiveness and efficiency. Some of the same analytics may help identify which employees will be best to backfill certain positions when others quit.
Some of the most desired types of reports revolve around employee engagement. With 85% of employees reporting a lack of engagement at work as recently as 2017, many employers are scrambling to understand why engagement is so low and how they can bring it up.
Workforce analytics tools and processes can help understand this issue (which is happening on a global scale) and use data to inform decisions for improving it. Things like employee incentive programs and benefits packages are often designed using analytics related to engagement.
Almost all large companies already track employee performance metrics. Everything from attendance to sales quotas, productivity, and quality are all aspects of an employee’s performance that can be analyzed to identify trends for the entire department.
For instance, if productivity is low across a department, it may be time to update certain processes to increase efficiency. This data is often used when creating a performance improvement plan (PIP) and can be used to offer promotions to employees who are already doing well.
Staffing and Scheduling
Busy seasons and certain holidays may require more employees on staff to maintain service levels. While some days such as Black Friday may have increased customer volumes, other trends can only be identified through historical data and predictive analytics.
By tracking employee schedules, your HCM software and other tools can also help you maintain an adequate level of staffing when other employees take time off.
One of the most important aspects of hiring your employees is training them to perform at their best for your company. The more complex the job, the more costly an extensive training program can be. Workforce analytics can analyze training benchmarks and performance data for your employees over time and help you decide how to maximize the effectiveness of your training and onboarding program.
Payroll and Benefits
Since HR also has a large hand in the process of running payroll, it’s also important to understand payroll and benefits data for your organization. Combining internal and external market data, you can assess if your employees are leaving to seek employment elsewhere based on better pay. You might need to restructure your benefits packages to be sure your employees are being compensated at competitive rates.
There are a number of different ways that workforce analytics can help you make better decisions with regard to managing employees. Some problems need to be considered from a variety of angles and visualized in a way that predicts the best action and desired outcome.
The fact is that these practices are changing the way HR professionals contribute to the success of a company. At Criterion, we believe we have a unique HCM solution waiting for you that can provide insight into your workforce and help you manage your team more effectively.
If you would like to see how you can help your company achieve its goals by making data-driven decisions, click here to schedule a demo today.