Bringing HR, payroll and finance together: A guide to workforce analytics

April 24, 2025

Drew Maginn

What are workforce analytics, and why are they important?

Before reviewing some steps your organization can take to leverage workforce analytics, it is important to start by sharing a simple definition:
Workforce analytics includes collecting, analyzing and using data about your workforce to make well-informed business decisions about key areas, including managing your talent, finding opportunities for efficiencies and aligning your workforce with strategic goals.

The majority of the data used for workforce analytics will come from your human resources and finance departments and allow for key performance indicators about employees to be evaluated on an ongoing basis. But before you can start fully realizing the benefits of workforce analytics, there are some key steps to follow to fully integrate this work into your business practices.

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Your step-by-step guide to workforce analytics

Step 1: Gather and consolidate your data
This first step may look different depending on how your organization currently manages its data.

For an organization with a data management system, such as enterprise resource planning software or a human resource information system, key data points from human resources (e.g., employee attendance), finance (e.g., revenue per employee) and payroll (e.g., salary and compensation) will already be centralized and accessible in a shared space. The data should be stored in an easily comparable format with the opportunity to customize and share reports with the most up-to-date information, assuming each department is accountable for their own data.

For an organization without a data management system, this step will be more time consuming and include:

Collecting and moving your data to a shared space (e.g., collaboration folder in Microsoft SharePoint or Google Drive).
Reviewing and organizing your data to make sure it is accurate, up-to-date and in a standardized format (e.g., available in Excel, using same headings and sub-headings).
Combining your data using common indicators such as employee ID or reference numbers.

” Workforce analytics includes collecting, analyzing and using data about your workforce to make well-informed business decisions about key areas, including managing your talent, finding opportunities for efficiencies and aligning your workforce with strategic goals. ”

Step 2: Identify your key questions

Facilitate a collaborative process between departments to identify some shared questions that you’re hoping to answer and what data you will use to support your analysis. For example, if you’re concerned about employee absenteeism and its impact on your organization, you may want to track patterns in sick days and leaves of absence to compare with employee performance ratings, productivity rates and revenue/sales per employee. You will also need to discuss the time period used to track this data to ensure that any insights are reliable and that enough time has passed to be confident that any trends or patterns that emerge are true indicators of the state of the organization.

For organizations in the early stages of exploring their workforce analytics, it might be wise to start with a smaller number of indicators at the beginning. There will always be the opportunity to add indicators over time once you’ve established your ability to complete these steps, but you never want to risk derailing the entire process by overburdening your team with an overwhelming amount of data.

Step 3: Analyze data and predict trends

With your questions and data identified, your team will use predictive insights to analyze this data, both past and present, to track trends over time and determine whether any action needs to be taken. This will require patience within each department to stay true to your initial scope (e.g., What data to analyze? How long to track?) and avoid taking any premature actions that don’t allow time for data trends and patterns to take shape. While ongoing data sharing, through simple data visualizations like charts and graphs, may be helpful to make predictions, there needs to be a shared commitment to validating these predictions. Using the previous example of employee absenteeism, you may notice increased sick days during the winter months, which could either be a sign of a disengaged staff taking “mental health days,” or simply an unfortunate wave of sicknesses passing through your office.

Step 4: Make data-driven decisions, take action and monitor results

Once enough time has passed to properly analyze and monitor the data, trends can be identified, whether predicted or not, that can inform direct actions taken by your organization. At the core of workforce analytics is using data to make informed decisions, so any actions must be directly linked to the data and not simply based on intuition or potential bias among team members.

There may be a need to hold on certain actions to allow more time to pass or implement subsequent actions if no change is found. For example, increased employee absenteeism linked to lower performance and productivity may be addressed through a commitment to more robust and supportive workplace wellness and employee engagement programs. However, if the trend continues for certain employees while others show improvement, future actions considered may include moving in a different direction that focuses on performance improvement that could ultimately lead to dismissals.

Embracing a workforce planning cycle

As tempting as it may be to follow these steps with a set beginning and end point, engaging with workforce analytics is best viewed as a cyclical process. As one trend is identified and addressed, another might be emerging in the background. To keep your team accountable to this work on an ongoing basis, you might also need to embed certain processes and procedures that don’t allow workforce analytics to fall to off your radar:

Make your data easy to access: This may seem like common sense, but some organizations create data systems that are not easily accessible or shareable across departments. Whether that involves limited access to data outside certain departments or password-protected spreadsheets, finding ways to create a more open data-sharing process benefits everyone across the organization.
Meet and share findings: While data sharing is important, it is limited when conversations are limited. Without regular meetings to share and discuss the data, with clear takeaways and ownership across departments, this type of work can quickly become a missed opportunity.

Establish benchmarks and set alerts: Clear benchmarks allow the organization to hold itself accountable to taking action if any indicators fall below a certain organizational expectation. It should be immediately flagged if a certain indicator falls below a certain threshold, and a discussion should follow across impacted departments.

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